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CLEVRER-Humans: Describing Physical and Causal Events the Human Way | Building machines that can reason about physical events and their causal
relationships is crucial for flexible interaction with the physical world.
However, most existing physical and causal reasoning benchmarks are exclusively
based on synthetically generated events and synthetic natural language
descriptions of causal relationships. This design brings up two issues. First,
there is a lack of diversity in both event types and natural language
descriptions; second, causal relationships based on manually-defined heuristics
are different from human judgments. To address both shortcomings, we present
the CLEVRER-Humans benchmark, a video reasoning dataset for causal judgment of
physical events with human labels. We employ two techniques to improve data
collection efficiency: first, a novel iterative event cloze task to elicit a
new representation of events in videos, which we term Causal Event Graphs
(CEGs); second, a data augmentation technique based on neural language
generative models. We convert the collected CEGs into questions and answers to
be consistent with prior work. Finally, we study a collection of baseline
approaches for CLEVRER-Humans question-answering, highlighting the great
challenges set forth by our benchmark. | [
"Jiayuan Mao",
"Xuelin Yang",
"Xikun Zhang",
"Noah D. Goodman",
"Jiajun Wu"
] | 2023-10-05 16:09:48 | http://arxiv.org/abs/2310.03635v1 | http://arxiv.org/pdf/2310.03635v1 | 2310.03635v1 |
High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning | A robot self-model is a task-agnostic representation of the robot's physical
morphology that can be used for motion planning tasks in absence of classical
geometric kinematic models. In particular, when the latter are hard to engineer
or the robot's kinematics change unexpectedly, human-free self-modeling is a
necessary feature of truly autonomous agents. In this work, we leverage neural
fields to allow a robot to self-model its kinematics as a neural-implicit query
model learned only from 2D images annotated with camera poses and
configurations. This enables significantly greater applicability than existing
approaches which have been dependent on depth images or geometry knowledge. To
this end, alongside a curricular data sampling strategy, we propose a new
encoder-based neural density field architecture for dynamic object-centric
scenes conditioned on high numbers of degrees of freedom (DOFs). In a 7-DOF
robot test setup, the learned self-model achieves a Chamfer-L2 distance of 2%
of the robot's workspace dimension. We demonstrate the capabilities of this
model on a motion planning task as an exemplary downstream application. | [
"Lennart Schulze",
"Hod Lipson"
] | 2023-10-05 16:01:29 | http://arxiv.org/abs/2310.03624v1 | http://arxiv.org/pdf/2310.03624v1 | 2310.03624v1 |
CLASSify: A Web-Based Tool for Machine Learning | Machine learning classification problems are widespread in bioinformatics,
but the technical knowledge required to perform model training, optimization,
and inference can prevent researchers from utilizing this technology. This
article presents an automated tool for machine learning classification problems
to simplify the process of training models and producing results while
providing informative visualizations and insights into the data. This tool
supports both binary and multiclass classification problems, and it provides
access to a variety of models and methods. Synthetic data can be generated
within the interface to fill missing values, balance class labels, or generate
entirely new datasets. It also provides support for feature evaluation and
generates explainability scores to indicate which features influence the output
the most. We present CLASSify, an open-source tool for simplifying the user
experience of solving classification problems without the need for knowledge of
machine learning. | [
"Aaron D. Mullen",
"Samuel E. Armstrong",
"Jeff Talbert",
"V. K. Cody Bumgardner"
] | 2023-10-05 15:51:36 | http://arxiv.org/abs/2310.03618v1 | http://arxiv.org/pdf/2310.03618v1 | 2310.03618v1 |
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally | Deep Neural Networks (DNNs) have been the driving force behind many of the
recent advances in machine learning. However, research has shown that DNNs are
vulnerable to adversarial examples -- input samples that have been perturbed to
force DNN-based models to make errors. As a result, Adversarial Machine
Learning (AdvML) has gained a lot of attention, and researchers have
investigated these vulnerabilities in various settings and modalities. In
addition, DNNs have also been found to incorporate embedded bias and often
produce unexplainable predictions, which can result in anti-social AI
applications. The emergence of new AI technologies that leverage Large Language
Models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing
anti-social applications at scale. AdvML for Social Good (AdvML4G) is an
emerging field that repurposes the AdvML bug to invent pro-social applications.
Regulators, practitioners, and researchers should collaborate to encourage the
development of pro-social applications and hinder the development of
anti-social ones. In this work, we provide the first comprehensive review of
the emerging field of AdvML4G. This paper encompasses a taxonomy that
highlights the emergence of AdvML4G, a discussion of the differences and
similarities between AdvML4G and AdvML, a taxonomy covering social good-related
concepts and aspects, an exploration of the motivations behind the emergence of
AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the
works that utilize AdvML4G as an auxiliary tool for innovating pro-social
applications. Finally, we elaborate upon various challenges and open research
issues that require significant attention from the research community. | [
"Shawqi Al-Maliki",
"Adnan Qayyum",
"Hassan Ali",
"Mohamed Abdallah",
"Junaid Qadir",
"Dinh Thai Hoang",
"Dusit Niyato",
"Ala Al-Fuqaha"
] | 2023-10-05 15:49:04 | http://arxiv.org/abs/2310.03614v1 | http://arxiv.org/pdf/2310.03614v1 | 2310.03614v1 |
Solving a Class of Non-Convex Minimax Optimization in Federated Learning | The minimax problems arise throughout machine learning applications, ranging
from adversarial training and policy evaluation in reinforcement learning to
AUROC maximization. To address the large-scale data challenges across multiple
clients with communication-efficient distributed training, federated learning
(FL) is gaining popularity. Many optimization algorithms for minimax problems
have been developed in the centralized setting (\emph{i.e.} single-machine).
Nonetheless, the algorithm for minimax problems under FL is still
underexplored. In this paper, we study a class of federated nonconvex minimax
optimization problems. We propose FL algorithms (FedSGDA+ and FedSGDA-M) and
reduce existing complexity results for the most common minimax problems. For
nonconvex-concave problems, we propose FedSGDA+ and reduce the communication
complexity to $O(\varepsilon^{-6})$. Under nonconvex-strongly-concave and
nonconvex-PL minimax settings, we prove that FedSGDA-M has the best-known
sample complexity of $O(\kappa^{3} N^{-1}\varepsilon^{-3})$ and the best-known
communication complexity of $O(\kappa^{2}\varepsilon^{-2})$. FedSGDA-M is the
first algorithm to match the best sample complexity $O(\varepsilon^{-3})$
achieved by the single-machine method under the nonconvex-strongly-concave
setting. Extensive experimental results on fair classification and AUROC
maximization show the efficiency of our algorithms. | [
"Xidong Wu",
"Jianhui Sun",
"Zhengmian Hu",
"Aidong Zhang",
"Heng Huang"
] | 2023-10-05 15:48:41 | http://arxiv.org/abs/2310.03613v1 | http://arxiv.org/pdf/2310.03613v1 | 2310.03613v1 |
GENER: A Parallel Layer Deep Learning Network To Detect Gene-Gene Interactions From Gene Expression Data | Detecting and discovering new gene interactions based on known gene
expressions and gene interaction data presents a significant challenge. Various
statistical and deep learning methods have attempted to tackle this challenge
by leveraging the topological structure of gene interactions and gene
expression patterns to predict novel gene interactions. In contrast, some
approaches have focused exclusively on utilizing gene expression profiles. In
this context, we introduce GENER, a parallel-layer deep learning network
designed exclusively for the identification of gene-gene relationships using
gene expression data. We conducted two training experiments and compared the
performance of our network with that of existing statistical and deep learning
approaches. Notably, our model achieved an average AUROC score of 0.834 on the
combined BioGRID&DREAM5 dataset, outperforming competing methods in predicting
gene-gene interactions. | [
"Ahmed Fakhry",
"Raneem Khafagy",
"Adriaan-Alexander Ludl"
] | 2023-10-05 15:45:53 | http://arxiv.org/abs/2310.03611v2 | http://arxiv.org/pdf/2310.03611v2 | 2310.03611v2 |
Comparing Time-Series Analysis Approaches Utilized in Research Papers to Forecast COVID-19 Cases in Africa: A Literature Review | This literature review aimed to compare various time-series analysis
approaches utilized in forecasting COVID-19 cases in Africa. The study involved
a methodical search for English-language research papers published between
January 2020 and July 2023, focusing specifically on papers that utilized
time-series analysis approaches on COVID-19 datasets in Africa. A variety of
databases including PubMed, Google Scholar, Scopus, and Web of Science were
utilized for this process. The research papers underwent an evaluation process
to extract relevant information regarding the implementation and performance of
the time-series analysis models. The study highlighted the different
methodologies employed, evaluating their effectiveness and limitations in
forecasting the spread of the virus. The result of this review could contribute
deeper insights into the field, and future research should consider these
insights to improve time series analysis models and explore the integration of
different approaches for enhanced public health decision-making. | [
"Ali Ebadi",
"Ebrahim Sahafizadeh"
] | 2023-10-05 15:36:47 | http://arxiv.org/abs/2310.03606v1 | http://arxiv.org/pdf/2310.03606v1 | 2310.03606v1 |
FASER: Binary Code Similarity Search through the use of Intermediate Representations | Being able to identify functions of interest in cross-architecture software
is useful whether you are analysing for malware, securing the software supply
chain or conducting vulnerability research. Cross-Architecture Binary Code
Similarity Search has been explored in numerous studies and has used a wide
range of different data sources to achieve its goals. The data sources
typically used draw on common structures derived from binaries such as function
control flow graphs or binary level call graphs, the output of the disassembly
process or the outputs of a dynamic analysis approach. One data source which
has received less attention is binary intermediate representations. Binary
Intermediate representations possess two interesting properties: they are cross
architecture by their very nature and encode the semantics of a function
explicitly to support downstream usage. Within this paper we propose Function
as a String Encoded Representation (FASER) which combines long document
transformers with the use of intermediate representations to create a model
capable of cross architecture function search without the need for manual
feature engineering, pre-training or a dynamic analysis step. We compare our
approach against a series of baseline approaches for two tasks; A general
function search task and a targeted vulnerability search task. Our approach
demonstrates strong performance across both tasks, performing better than all
baseline approaches. | [
"Josh Collyer",
"Tim Watson",
"Iain Phillips"
] | 2023-10-05 15:36:35 | http://arxiv.org/abs/2310.03605v2 | http://arxiv.org/pdf/2310.03605v2 | 2310.03605v2 |
Sampling via Gradient Flows in the Space of Probability Measures | Sampling a target probability distribution with an unknown normalization
constant is a fundamental challenge in computational science and engineering.
Recent work shows that algorithms derived by considering gradient flows in the
space of probability measures open up new avenues for algorithm development.
This paper makes three contributions to this sampling approach by scrutinizing
the design components of such gradient flows. Any instantiation of a gradient
flow for sampling needs an energy functional and a metric to determine the
flow, as well as numerical approximations of the flow to derive algorithms. Our
first contribution is to show that the Kullback-Leibler divergence, as an
energy functional, has the unique property (among all f-divergences) that
gradient flows resulting from it do not depend on the normalization constant of
the target distribution. Our second contribution is to study the choice of
metric from the perspective of invariance. The Fisher-Rao metric is known as
the unique choice (up to scaling) that is diffeomorphism invariant. As a
computationally tractable alternative, we introduce a relaxed, affine
invariance property for the metrics and gradient flows. In particular, we
construct various affine invariant Wasserstein and Stein gradient flows. Affine
invariant gradient flows are shown to behave more favorably than their
non-affine-invariant counterparts when sampling highly anisotropic
distributions, in theory and by using particle methods. Our third contribution
is to study, and develop efficient algorithms based on Gaussian approximations
of the gradient flows; this leads to an alternative to particle methods. We
establish connections between various Gaussian approximate gradient flows,
discuss their relation to gradient methods arising from parametric variational
inference, and study their convergence properties both theoretically and
numerically. | [
"Yifan Chen",
"Daniel Zhengyu Huang",
"Jiaoyang Huang",
"Sebastian Reich",
"Andrew M Stuart"
] | 2023-10-05 15:20:35 | http://arxiv.org/abs/2310.03597v1 | http://arxiv.org/pdf/2310.03597v1 | 2310.03597v1 |
TimeGPT-1 | In this paper, we introduce TimeGPT, the first foundation model for time
series, capable of generating accurate predictions for diverse datasets not
seen during training. We evaluate our pre-trained model against established
statistical, machine learning, and deep learning methods, demonstrating that
TimeGPT zero-shot inference excels in performance, efficiency, and simplicity.
Our study provides compelling evidence that insights from other domains of
artificial intelligence can be effectively applied to time series analysis. We
conclude that large-scale time series models offer an exciting opportunity to
democratize access to precise predictions and reduce uncertainty by leveraging
the capabilities of contemporary advancements in deep learning. | [
"Azul Garza",
"Max Mergenthaler-Canseco"
] | 2023-10-05 15:14:00 | http://arxiv.org/abs/2310.03589v1 | http://arxiv.org/pdf/2310.03589v1 | 2310.03589v1 |
Smoothing Methods for Automatic Differentiation Across Conditional Branches | Programs involving discontinuities introduced by control flow constructs such
as conditional branches pose challenges to mathematical optimization methods
that assume a degree of smoothness in the objective function's response
surface. Smooth interpretation (SI) is a form of abstract interpretation that
approximates the convolution of a program's output with a Gaussian kernel, thus
smoothing its output in a principled manner. Here, we combine SI with automatic
differentiation (AD) to efficiently compute gradients of smoothed programs. In
contrast to AD across a regular program execution, these gradients also capture
the effects of alternative control flow paths. The combination of SI with AD
enables the direct gradient-based parameter synthesis for branching programs,
allowing for instance the calibration of simulation models or their combination
with neural network models in machine learning pipelines. We detail the effects
of the approximations made for tractability in SI and propose a novel Monte
Carlo estimator that avoids the underlying assumptions by estimating the
smoothed programs' gradients through a combination of AD and sampling. Using
DiscoGrad, our tool for automatically translating simple C++ programs to a
smooth differentiable form, we perform an extensive evaluation. We compare the
combination of SI with AD and our Monte Carlo estimator to existing
gradient-free and stochastic methods on four non-trivial and originally
discontinuous problems ranging from classical simulation-based optimization to
neural network-driven control. While the optimization progress with the
SI-based estimator depends on the complexity of the programs' control flow, our
Monte Carlo estimator is competitive in all problems, exhibiting the fastest
convergence by a substantial margin in our highest-dimensional problem. | [
"Justin N. Kreikemeyer",
"Philipp Andelfinger"
] | 2023-10-05 15:08:37 | http://arxiv.org/abs/2310.03585v1 | http://arxiv.org/pdf/2310.03585v1 | 2310.03585v1 |
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End | Autonomous robots must navigate reliably in unknown environments even under
compromised exteroceptive perception, or perception failures. Such failures
often occur when harsh environments lead to degraded sensing, or when the
perception algorithm misinterprets the scene due to limited generalization. In
this paper, we model perception failures as invisible obstacles and pits, and
train a reinforcement learning (RL) based local navigation policy to guide our
legged robot. Unlike previous works relying on heuristics and anomaly detection
to update navigational information, we train our navigation policy to
reconstruct the environment information in the latent space from corrupted
perception and react to perception failures end-to-end. To this end, we
incorporate both proprioception and exteroception into our policy inputs,
thereby enabling the policy to sense collisions on different body parts and
pits, prompting corresponding reactions. We validate our approach in simulation
and on the real quadruped robot ANYmal running in real-time (<10 ms CPU
inference). In a quantitative comparison with existing heuristic-based locally
reactive planners, our policy increases the success rate over 30% when facing
perception failures. Project Page: https://bit.ly/45NBTuh. | [
"Jin Jin",
"Chong Zhang",
"Jonas Frey",
"Nikita Rudin",
"Matias Mattamala",
"Cesar Cadena",
"Marco Hutter"
] | 2023-10-05 15:01:31 | http://arxiv.org/abs/2310.03581v1 | http://arxiv.org/pdf/2310.03581v1 | 2310.03581v1 |
Targeted Adversarial Attacks on Generalizable Neural Radiance Fields | Neural Radiance Fields (NeRFs) have recently emerged as a powerful tool for
3D scene representation and rendering. These data-driven models can learn to
synthesize high-quality images from sparse 2D observations, enabling realistic
and interactive scene reconstructions. However, the growing usage of NeRFs in
critical applications such as augmented reality, robotics, and virtual
environments could be threatened by adversarial attacks.
In this paper we present how generalizable NeRFs can be attacked by both
low-intensity adversarial attacks and adversarial patches, where the later
could be robust enough to be used in real world applications. We also
demonstrate targeted attacks, where a specific, predefined output scene is
generated by these attack with success. | [
"Andras Horvath",
"Csaba M. Jozsa"
] | 2023-10-05 14:59:18 | http://arxiv.org/abs/2310.03578v1 | http://arxiv.org/pdf/2310.03578v1 | 2310.03578v1 |
Analysis of learning a flow-based generative model from limited sample complexity | We study the problem of training a flow-based generative model, parametrized
by a two-layer autoencoder, to sample from a high-dimensional Gaussian mixture.
We provide a sharp end-to-end analysis of the problem. First, we provide a
tight closed-form characterization of the learnt velocity field, when
parametrized by a shallow denoising auto-encoder trained on a finite number $n$
of samples from the target distribution. Building on this analysis, we provide
a sharp description of the corresponding generative flow, which pushes the base
Gaussian density forward to an approximation of the target density. In
particular, we provide closed-form formulae for the distance between the mean
of the generated mixture and the mean of the target mixture, which we show
decays as $\Theta_n(\frac{1}{n})$. Finally, this rate is shown to be in fact
Bayes-optimal. | [
"Hugo Cui",
"Florent Krzakala",
"Eric Vanden-Eijnden",
"Lenka Zdeborová"
] | 2023-10-05 14:53:40 | http://arxiv.org/abs/2310.03575v1 | http://arxiv.org/pdf/2310.03575v1 | 2310.03575v1 |
Residual Multi-Fidelity Neural Network Computing | In this work, we consider the general problem of constructing a neural
network surrogate model using multi-fidelity information. Given an inexpensive
low-fidelity and an expensive high-fidelity computational model, we present a
residual multi-fidelity computational framework that formulates the correlation
between models as a residual function, a possibly non-linear mapping between 1)
the shared input space of the models together with the low-fidelity model
output and 2) the discrepancy between the two model outputs. To accomplish
this, we train two neural networks to work in concert. The first network learns
the residual function on a small set of high-fidelity and low-fidelity data.
Once trained, this network is used to generate additional synthetic
high-fidelity data, which is used in the training of a second network. This
second network, once trained, acts as our surrogate for the high-fidelity
quantity of interest. We present three numerical examples to demonstrate the
power of the proposed framework. In particular, we show that dramatic savings
in computational cost may be achieved when the output predictions are desired
to be accurate within small tolerances. | [
"Owen Davis",
"Mohammad Motamed",
"Raul Tempone"
] | 2023-10-05 14:43:16 | http://arxiv.org/abs/2310.03572v1 | http://arxiv.org/pdf/2310.03572v1 | 2310.03572v1 |
BID-NeRF: RGB-D image pose estimation with inverted Neural Radiance Fields | We aim to improve the Inverted Neural Radiance Fields (iNeRF) algorithm which
defines the image pose estimation problem as a NeRF based iterative linear
optimization. NeRFs are novel neural space representation models that can
synthesize photorealistic novel views of real-world scenes or objects. Our
contributions are as follows: we extend the localization optimization objective
with a depth-based loss function, we introduce a multi-image based loss
function where a sequence of images with known relative poses are used without
increasing the computational complexity, we omit hierarchical sampling during
volumetric rendering, meaning only the coarse model is used for pose
estimation, and we how that by extending the sampling interval convergence can
be achieved even or higher initial pose estimate errors. With the proposed
modifications the convergence speed is significantly improved, and the basin of
convergence is substantially extended. | [
"Ágoston István Csehi",
"Csaba Máté Józsa"
] | 2023-10-05 14:27:06 | http://arxiv.org/abs/2310.03563v1 | http://arxiv.org/pdf/2310.03563v1 | 2310.03563v1 |
Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective | Probabilistic modelling of power systems operation and planning processes
depends on data-driven methods, which require sufficiently large datasets. When
historical data lacks this, it is desired to model the underlying data
generation mechanism as a probability distribution to assess the data quality
and generate more data, if needed. Kernel density estimation (KDE) based models
are popular choices for this task, but they fail to adapt to data regions with
varying densities. In this paper, an adaptive KDE model is employed to
circumvent this, where each kernel in the model has an individual bandwidth.
The leave-one-out maximum log-likelihood (LOO-MLL) criterion is proposed to
prevent the singular solutions that the regular MLL criterion gives rise to,
and it is proven that LOO-MLL prevents these. Relying on this guaranteed
robustness, the model is extended by assigning learnable weights to the
kernels. In addition, a modified expectation-maximization algorithm is employed
to accelerate the optimization speed reliably. The performance of the proposed
method and models are exhibited on two power systems datasets using different
statistical tests and by comparison with Gaussian mixture models. Results show
that the proposed models have promising performance, in addition to their
singularity prevention guarantees. | [
"Kutay Bölat",
"Simon H. Tindemans",
"Peter Palensky"
] | 2023-10-05 14:08:42 | http://arxiv.org/abs/2310.03556v1 | http://arxiv.org/pdf/2310.03556v1 | 2310.03556v1 |
Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models | Posterior sampling has been shown to be a powerful Bayesian approach for
solving imaging inverse problems. The recent plug-and-play unadjusted Langevin
algorithm (PnP-ULA) has emerged as a promising method for Monte Carlo sampling
and minimum mean squared error (MMSE) estimation by combining physical
measurement models with deep-learning priors specified using image denoisers.
However, the intricate relationship between the sampling distribution of
PnP-ULA and the mismatched data-fidelity and denoiser has not been
theoretically analyzed. We address this gap by proposing a posterior-L2
pseudometric and using it to quantify an explicit error bound for PnP-ULA under
mismatched posterior distribution. We numerically validate our theory on
several inverse problems such as sampling from Gaussian mixture models and
image deblurring. Our results suggest that the sensitivity of the sampling
distribution of PnP-ULA to a mismatch in the measurement model and the denoiser
can be precisely characterized. | [
"Marien Renaud",
"Jiaming Liu",
"Valentin de Bortoli",
"Andrés Almansa",
"Ulugbek S. Kamilov"
] | 2023-10-05 13:57:53 | http://arxiv.org/abs/2310.03546v1 | http://arxiv.org/pdf/2310.03546v1 | 2310.03546v1 |
Distribution-free risk assessment of regression-based machine learning algorithms | Machine learning algorithms have grown in sophistication over the years and
are increasingly deployed for real-life applications. However, when using
machine learning techniques in practical settings, particularly in high-risk
applications such as medicine and engineering, obtaining the failure
probability of the predictive model is critical. We refer to this problem as
the risk-assessment task. We focus on regression algorithms and the
risk-assessment task of computing the probability of the true label lying
inside an interval defined around the model's prediction. We solve the
risk-assessment problem using the conformal prediction approach, which provides
prediction intervals that are guaranteed to contain the true label with a given
probability. Using this coverage property, we prove that our approximated
failure probability is conservative in the sense that it is not lower than the
true failure probability of the ML algorithm. We conduct extensive experiments
to empirically study the accuracy of the proposed method for problems with and
without covariate shift. Our analysis focuses on different modeling regimes,
dataset sizes, and conformal prediction methodologies. | [
"Sukrita Singh",
"Neeraj Sarna",
"Yuanyuan Li",
"Yang Li",
"Agni Orfanoudaki",
"Michael Berger"
] | 2023-10-05 13:57:24 | http://arxiv.org/abs/2310.03545v1 | http://arxiv.org/pdf/2310.03545v1 | 2310.03545v1 |
Lightweight Boosting Models for User Response Prediction Using Adversarial Validation | The ACM RecSys Challenge 2023, organized by ShareChat, aims to predict the
probability of the app being installed. This paper describes the lightweight
solution to this challenge. We formulate the task as a user response prediction
task. For rapid prototyping for the task, we propose a lightweight solution
including the following steps: 1) using adversarial validation, we effectively
eliminate uninformative features from a dataset; 2) to address noisy continuous
features and categorical features with a large number of unique values, we
employ feature engineering techniques.; 3) we leverage Gradient Boosted
Decision Trees (GBDT) for their exceptional performance and scalability. The
experiments show that a single LightGBM model, without additional ensembling,
performs quite well. Our team achieved ninth place in the challenge with the
final leaderboard score of 6.059065. Code for our approach can be found here:
https://github.com/choco9966/recsys-challenge-2023. | [
"Hyeonwoo Kim",
"Wonsung Lee"
] | 2023-10-05 13:57:05 | http://arxiv.org/abs/2310.03778v1 | http://arxiv.org/pdf/2310.03778v1 | 2310.03778v1 |
Joint Group Invariant Functions on Data-Parameter Domain Induce Universal Neural Networks | The symmetry and geometry of input data are considered to be encoded in the
internal data representation inside the neural network, but the specific
encoding rule has been less investigated. By focusing on a joint group
invariant function on the data-parameter domain, we present a systematic rule
to find a dual group action on the parameter domain from a group action on the
data domain. Further, we introduce generalized neural networks induced from the
joint invariant functions, and present a new group theoretic proof of their
universality theorems by using Schur's lemma. Since traditional universality
theorems were demonstrated based on functional analytical methods, this study
sheds light on the group theoretic aspect of the approximation theory,
connecting geometric deep learning to abstract harmonic analysis. | [
"Sho Sonoda",
"Hideyuki Ishi",
"Isao Ishikawa",
"Masahiro Ikeda"
] | 2023-10-05 13:30:37 | http://arxiv.org/abs/2310.03530v1 | http://arxiv.org/pdf/2310.03530v1 | 2310.03530v1 |
Deep Ridgelet Transform: Voice with Koopman Operator Proves Universality of Formal Deep Networks | We identify hidden layers inside a DNN with group actions on the data space,
and formulate the DNN as a dual voice transform with respect to Koopman
operator, a linear representation of the group action. Based on the group
theoretic arguments, particularly by using Schur's lemma, we show a simple
proof of the universality of those DNNs. | [
"Sho Sonoda",
"Yuka Hashimoto",
"Isao Ishikawa",
"Masahiro Ikeda"
] | 2023-10-05 13:29:46 | http://arxiv.org/abs/2310.03529v1 | http://arxiv.org/pdf/2310.03529v1 | 2310.03529v1 |
High-dimensional Bayesian Optimization with Group Testing | Bayesian optimization is an effective method for optimizing
expensive-to-evaluate black-box functions. High-dimensional problems are
particularly challenging as the surrogate model of the objective suffers from
the curse of dimensionality, which makes accurate modeling difficult. We
propose a group testing approach to identify active variables to facilitate
efficient optimization in these domains. The proposed algorithm, Group Testing
Bayesian Optimization (GTBO), first runs a testing phase where groups of
variables are systematically selected and tested on whether they influence the
objective. To that end, we extend the well-established theory of group testing
to functions of continuous ranges. In the second phase, GTBO guides
optimization by placing more importance on the active dimensions. By exploiting
the axis-aligned subspace assumption, GTBO is competitive against
state-of-the-art methods on several synthetic and real-world high-dimensional
optimization tasks. Furthermore, GTBO aids in the discovery of active
parameters in applications, thereby enhancing practitioners' understanding of
the problem at hand. | [
"Erik Orm Hellsten",
"Carl Hvarfner",
"Leonard Papenmeier",
"Luigi Nardi"
] | 2023-10-05 12:52:27 | http://arxiv.org/abs/2310.03515v1 | http://arxiv.org/pdf/2310.03515v1 | 2310.03515v1 |
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models | Otago Exercise Program (OEP) is a rehabilitation program for older adults to
improve frailty, sarcopenia, and balance. Accurate monitoring of patient
involvement in OEP is challenging, as self-reports (diaries) are often
unreliable. With the development of wearable sensors, Human Activity
Recognition (HAR) systems using wearable sensors have revolutionized
healthcare. However, their usage for OEP still shows limited performance. The
objective of this study is to build an unobtrusive and accurate system to
monitor OEP for older adults. Data was collected from older adults wearing a
single waist-mounted Inertial Measurement Unit (IMU). Two datasets were
collected, one in a laboratory setting, and one at the homes of the patients. A
hierarchical system is proposed with two stages: 1) using a deep learning model
to recognize whether the patients are performing OEP or activities of daily
life (ADLs) using a 10-minute sliding window; 2) based on stage 1, using a
6-second sliding window to recognize the OEP sub-classes performed. The results
showed that in stage 1, OEP could be recognized with window-wise f1-scores over
0.95 and Intersection-over-Union (IoU) f1-scores over 0.85 for both datasets.
In stage 2, for the home scenario, four activities could be recognized with
f1-scores over 0.8: ankle plantarflexors, abdominal muscles, knee bends, and
sit-to-stand. The results showed the potential of monitoring the compliance of
OEP using a single IMU in daily life. Also, some OEP sub-classes are possible
to be recognized for further analysis. | [
"Meng Shang",
"Lenore Dedeyne",
"Jolan Dupont",
"Laura Vercauteren",
"Nadjia Amini",
"Laurence Lapauw",
"Evelien Gielen",
"Sabine Verschueren",
"Carolina Varon",
"Walter De Raedt",
"Bart Vanrumste"
] | 2023-10-05 12:46:56 | http://arxiv.org/abs/2310.03512v1 | http://arxiv.org/pdf/2310.03512v1 | 2310.03512v1 |
Deep Generative Models of Music Expectation | A prominent theory of affective response to music revolves around the
concepts of surprisal and expectation. In prior work, this idea has been
operationalized in the form of probabilistic models of music which allow for
precise computation of song (or note-by-note) probabilities, conditioned on a
'training set' of prior musical or cultural experiences. To date, however,
these models have been limited to compute exact probabilities through
hand-crafted features or restricted to linear models which are likely not
sufficient to represent the complex conditional distributions present in music.
In this work, we propose to use modern deep probabilistic generative models in
the form of a Diffusion Model to compute an approximate likelihood of a musical
input sequence. Unlike prior work, such a generative model parameterized by
deep neural networks is able to learn complex non-linear features directly from
a training set itself. In doing so, we expect to find that such models are able
to more accurately represent the 'surprisal' of music for human listeners. From
the literature, it is known that there is an inverted U-shaped relationship
between surprisal and the amount human subjects 'like' a given song. In this
work we show that pre-trained diffusion models indeed yield musical surprisal
values which exhibit a negative quadratic relationship with measured subject
'liking' ratings, and that the quality of this relationship is competitive with
state of the art methods such as IDyOM. We therefore present this model a
preliminary step in developing modern deep generative models of music
expectation and subjective likability. | [
"Ninon Lizé Masclef",
"T. Anderson Keller"
] | 2023-10-05 12:25:39 | http://arxiv.org/abs/2310.03500v1 | http://arxiv.org/pdf/2310.03500v1 | 2310.03500v1 |
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning | A key limitation preventing the wider adoption of autonomous agents trained
via deep reinforcement learning (RL) is their limited ability to generalise to
new environments, even when these share similar characteristics with
environments encountered during training. In this work, we investigate how a
non-uniform sampling strategy of individual environment instances, or levels,
affects the zero-shot generalisation (ZSG) ability of RL agents, considering
two failure modes: overfitting and over-generalisation. As a first step, we
measure the mutual information (MI) between the agent's internal representation
and the set of training levels, which we find to be well-correlated to instance
overfitting. In contrast to uniform sampling, adaptive sampling strategies
prioritising levels based on their value loss are more effective at maintaining
lower MI, which provides a novel theoretical justification for this class of
techniques. We then turn our attention to unsupervised environment design (UED)
methods, which adaptively generate new training levels and minimise MI more
effectively than methods sampling from a fixed set. However, we find UED
methods significantly shift the training distribution, resulting in
over-generalisation and worse ZSG performance over the distribution of
interest. To prevent both instance overfitting and over-generalisation, we
introduce self-supervised environment design (SSED). SSED generates levels
using a variational autoencoder, effectively reducing MI while minimising the
shift with the distribution of interest, and leads to statistically significant
improvements in ZSG over fixed-set level sampling strategies and UED methods. | [
"Samuel Garcin",
"James Doran",
"Shangmin Guo",
"Christopher G. Lucas",
"Stefano V. Albrecht"
] | 2023-10-05 12:08:12 | http://arxiv.org/abs/2310.03494v1 | http://arxiv.org/pdf/2310.03494v1 | 2310.03494v1 |
TPDR: A Novel Two-Step Transformer-based Product and Class Description Match and Retrieval Method | There is a niche of companies responsible for intermediating the purchase of
large batches of varied products for other companies, for which the main
challenge is to perform product description standardization, i.e., matching an
item described by a client with a product described in a catalog. The problem
is complex since the client's product description may be: (1) potentially
noisy; (2) short and uninformative (e.g., missing information about model and
size); and (3) cross-language. In this paper, we formalize this problem as a
ranking task: given an initial client product specification (query), return the
most appropriate standardized descriptions (response). In this paper, we
propose TPDR, a two-step Transformer-based Product and Class Description
Retrieval method that is able to explore the semantic correspondence between IS
and SD, by exploiting attention mechanisms and contrastive learning. First,
TPDR employs the transformers as two encoders sharing the embedding vector
space: one for encoding the IS and another for the SD, in which corresponding
pairs (IS, SD) must be close in the vector space. Closeness is further enforced
by a contrastive learning mechanism leveraging a specialized loss function.
TPDR also exploits a (second) re-ranking step based on syntactic features that
are very important for the exact matching (model, dimension) of certain
products that may have been neglected by the transformers. To evaluate our
proposal, we consider 11 datasets from a real company, covering different
application contexts. Our solution was able to retrieve the correct
standardized product before the 5th ranking position in 71% of the cases and
its correct category in the first position in 80% of the situations. Moreover,
the effectiveness gains over purely syntactic or semantic baselines reach up to
3.7 times, solving cases that none of the approaches in isolation can do by
themselves. | [
"Washington Cunha",
"Celso França",
"Leonardo Rocha",
"Marcos André Gonçalves"
] | 2023-10-05 12:02:51 | http://arxiv.org/abs/2310.03491v1 | http://arxiv.org/pdf/2310.03491v1 | 2310.03491v1 |
BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification | Brain tumors pose significant health challenges worldwide, with glioblastoma
being one of the most aggressive forms. Accurate determination of the
O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is
crucial for personalized treatment strategies. However, traditional methods are
labor-intensive and time-consuming. This paper proposes a novel multi-modal
approach, BTDNet, leveraging multi-parametric MRI scans, including FLAIR, T1w,
T1wCE, and T2 3D volumes, to predict MGMT promoter methylation status. BTDNet
addresses two main challenges: the variable volume lengths (i.e., each volume
consists of a different number of slices) and the volume-level annotations
(i.e., the whole 3D volume is annotated and not the independent slices that it
consists of). BTDNet consists of four components: i) the data augmentation one
(that performs geometric transformations, convex combinations of data pairs and
test-time data augmentation); ii) the 3D analysis one (that performs global
analysis through a CNN-RNN); iii) the routing one (that contains a mask layer
that handles variable input feature lengths), and iv) the modality fusion one
(that effectively enhances data representation, reduces ambiguities and
mitigates data scarcity). The proposed method outperforms by large margins the
state-of-the-art methods in the RSNA-ASNR-MICCAI BraTS 2021 Challenge, offering
a promising avenue for enhancing brain tumor diagnosis and treatment. | [
"Dimitrios Kollias",
"Karanjot Vendal",
"Priyanka Gadhavi",
"Solomon Russom"
] | 2023-10-05 11:56:06 | http://arxiv.org/abs/2310.03485v2 | http://arxiv.org/pdf/2310.03485v2 | 2310.03485v2 |
The Geometric Structure of Fully-Connected ReLU-Layers | We formalize and interpret the geometric structure of $d$-dimensional fully
connected ReLU-layers in neural networks. The parameters of a ReLU-layer induce
a natural partition of the input domain, such that in each sector of the
partition, the ReLU-layer can be greatly simplified. This leads to a geometric
interpretation of a ReLU-layer as a projection onto a polyhedral cone followed
by an affine transformation, in line with the description in
[doi:10.48550/arXiv.1905.08922] for convolutional networks with ReLU
activations. Further, this structure facilitates simplified expressions for
preimages of the intersection between partition sectors and hyperplanes, which
is useful when describing decision boundaries in a classification setting. We
investigate this in detail for a feed-forward network with one hidden
ReLU-layer, where we provide results on the geometric complexity of the
decision boundary generated by such networks, as well as proving that modulo an
affine transformation, such a network can only generate $d$ different decision
boundaries. Finally, the effect of adding more layers to the network is
discussed. | [
"Jonatan Vallin",
"Karl Larsson",
"Mats G. Larson"
] | 2023-10-05 11:54:07 | http://arxiv.org/abs/2310.03482v1 | http://arxiv.org/pdf/2310.03482v1 | 2310.03482v1 |
The Cadenza ICASSP 2024 Grand Challenge | The Cadenza project aims to enhance the audio quality of music for
individuals with hearing loss. As part of this, the project is organizing the
ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids. The
challenge can be tackled by decomposing the music at the hearing aid
microphones into vocals, bass, drums, and other components. These can then be
intelligently remixed in a personalized manner to improve audio quality.
Alternatively, an end-to-end approach could be used. Processes need to consider
the music itself, the gain applied to each component, and the listener's
hearing loss. The submitted entries will be evaluated using the intrusive
objective metric, the Hearing Aid Audio Quality Index (HAAQI). This paper
outlines the challenge. | [
"Gerardo Roa Dabike",
"Michael A. Akeroyd",
"Scott Bannister",
"Jon Barker",
"Trevor J. Cox",
"Bruno Fazenda",
"Jennifer Firth",
"Simone Graetzer",
"Alinka Greasley",
"Rebecca Vos",
"William Whitmer"
] | 2023-10-05 11:46:32 | http://arxiv.org/abs/2310.03480v1 | http://arxiv.org/pdf/2310.03480v1 | 2310.03480v1 |
Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties | In the past decade, Artificial Intelligence driven drug design and discovery
has been a hot research topic, where an important branch is molecule generation
by generative models, from GAN-based models and VAE-based models to the latest
diffusion-based models. However, most existing models pursue only the basic
properties like validity and uniqueness of the generated molecules, a few go
further to explicitly optimize one single important molecular property (e.g.
QED or PlogP), which makes most generated molecules little usefulness in
practice. In this paper, we present a novel approach to generating molecules
with desirable properties, which expands the diffusion model framework with
multiple innovative designs. The novelty is two-fold. On the one hand,
considering that the structures of molecules are complex and diverse, and
molecular properties are usually determined by some substructures (e.g.
pharmacophores), we propose to perform diffusion on two structural levels:
molecules and molecular fragments respectively, with which a mixed Gaussian
distribution is obtained for the reverse diffusion process. To get desirable
molecular fragments, we develop a novel electronic effect based fragmentation
method. On the other hand, we introduce two ways to explicitly optimize
multiple molecular properties under the diffusion model framework. First, as
potential drug molecules must be chemically valid, we optimize molecular
validity by an energy-guidance function. Second, since potential drug molecules
should be desirable in various properties, we employ a multi-objective
mechanism to optimize multiple molecular properties simultaneously. Extensive
experiments with two benchmark datasets QM9 and ZINC250k show that the
molecules generated by our proposed method have better validity, uniqueness,
novelty, Fr\'echet ChemNet Distance (FCD), QED, and PlogP than those generated
by current SOTA models. | [
"Siyuan Guo",
"Jihong Guan",
"Shuigeng Zhou"
] | 2023-10-05 11:43:21 | http://arxiv.org/abs/2310.04463v1 | http://arxiv.org/pdf/2310.04463v1 | 2310.04463v1 |
The Blame Problem in Evaluating Local Explanations, and How to Tackle it | The number of local model-agnostic explanation techniques proposed has grown
rapidly recently. One main reason is that the bar for developing new
explainability techniques is low due to the lack of optimal evaluation
measures. Without rigorous measures, it is hard to have concrete evidence of
whether the new explanation techniques can significantly outperform their
predecessors. Our study proposes a new taxonomy for evaluating local
explanations: robustness, evaluation using ground truth from synthetic datasets
and interpretable models, model randomization, and human-grounded evaluation.
Using this proposed taxonomy, we highlight that all categories of evaluation
methods, except those based on the ground truth from interpretable models,
suffer from a problem we call the "blame problem." In our study, we argue that
this category of evaluation measure is a more reasonable method for evaluating
local model-agnostic explanations. However, we show that even this category of
evaluation measures has further limitations. The evaluation of local
explanations remains an open research problem. | [
"Amir Hossein Akhavan Rahnama"
] | 2023-10-05 11:21:49 | http://arxiv.org/abs/2310.03466v1 | http://arxiv.org/pdf/2310.03466v1 | 2310.03466v1 |
Which mode is better for federated learning? Centralized or Decentralized | Both centralized and decentralized approaches have shown excellent
performance and great application value in federated learning (FL). However,
current studies do not provide sufficient evidence to show which one performs
better. Although from the optimization perspective, decentralized methods can
approach the comparable convergence of centralized methods with less
communication, its test performance has always been inefficient in empirical
studies. To comprehensively explore their behaviors in FL, we study their
excess risks, including the joint analysis of both optimization and
generalization. We prove that on smooth non-convex objectives, 1) centralized
FL (CFL) always generalizes better than decentralized FL (DFL); 2) from
perspectives of the excess risk and test error in CFL, adopting partial
participation is superior to full participation; and, 3) there is a necessary
requirement for the topology in DFL to avoid performance collapse as the
training scale increases. Based on some simple hardware metrics, we could
evaluate which framework is better in practice. Extensive experiments are
conducted on common setups in FL to validate that our theoretical analysis is
contextually valid in practical scenarios. | [
"Yan Sun",
"Li Shen",
"Dacheng Tao"
] | 2023-10-05 11:09:42 | http://arxiv.org/abs/2310.03461v1 | http://arxiv.org/pdf/2310.03461v1 | 2310.03461v1 |
Multi-Resolution Audio-Visual Feature Fusion for Temporal Action Localization | Temporal Action Localization (TAL) aims to identify actions' start, end, and
class labels in untrimmed videos. While recent advancements using transformer
networks and Feature Pyramid Networks (FPN) have enhanced visual feature
recognition in TAL tasks, less progress has been made in the integration of
audio features into such frameworks. This paper introduces the Multi-Resolution
Audio-Visual Feature Fusion (MRAV-FF), an innovative method to merge
audio-visual data across different temporal resolutions. Central to our
approach is a hierarchical gated cross-attention mechanism, which discerningly
weighs the importance of audio information at diverse temporal scales. Such a
technique not only refines the precision of regression boundaries but also
bolsters classification confidence. Importantly, MRAV-FF is versatile, making
it compatible with existing FPN TAL architectures and offering a significant
enhancement in performance when audio data is available. | [
"Edward Fish",
"Jon Weinbren",
"Andrew Gilbert"
] | 2023-10-05 10:54:33 | http://arxiv.org/abs/2310.03456v1 | http://arxiv.org/pdf/2310.03456v1 | 2310.03456v1 |
FLAIM: AIM-based Synthetic Data Generation in the Federated Setting | Preserving individual privacy while enabling collaborative data sharing is
crucial for organizations. Synthetic data generation is one solution, producing
artificial data that mirrors the statistical properties of private data. While
numerous techniques have been devised under differential privacy, they
predominantly assume data is centralized. However, data is often distributed
across multiple clients in a federated manner. In this work, we initiate the
study of federated synthetic tabular data generation. Building upon a SOTA
central method known as AIM, we present DistAIM and FLAIM. We show it is
straightforward to distribute AIM, extending a recent approach based on secure
multi-party computation which necessitates additional overhead, making it less
suited to federated scenarios. We then demonstrate that naively federating AIM
can lead to substantial degradation in utility under the presence of
heterogeneity. To mitigate both issues, we propose an augmented FLAIM approach
that maintains a private proxy of heterogeneity. We simulate our methods across
a range of benchmark datasets under different degrees of heterogeneity and show
this can improve utility while reducing overhead. | [
"Samuel Maddock",
"Graham Cormode",
"Carsten Maple"
] | 2023-10-05 10:34:47 | http://arxiv.org/abs/2310.03447v1 | http://arxiv.org/pdf/2310.03447v1 | 2310.03447v1 |
Variational Inference for GARCH-family Models | The Bayesian estimation of GARCH-family models has been typically addressed
through Monte Carlo sampling. Variational Inference is gaining popularity and
attention as a robust approach for Bayesian inference in complex machine
learning models; however, its adoption in econometrics and finance is limited.
This paper discusses the extent to which Variational Inference constitutes a
reliable and feasible alternative to Monte Carlo sampling for Bayesian
inference in GARCH-like models. Through a large-scale experiment involving the
constituents of the S&P 500 index, several Variational Inference optimizers, a
variety of volatility models, and a case study, we show that Variational
Inference is an attractive, remarkably well-calibrated, and competitive method
for Bayesian learning. | [
"Martin Magris",
"Alexandros Iosifidis"
] | 2023-10-05 10:21:31 | http://arxiv.org/abs/2310.03435v1 | http://arxiv.org/pdf/2310.03435v1 | 2310.03435v1 |
Neural Language Model Pruning for Automatic Speech Recognition | We study model pruning methods applied to Transformer-based neural network
language models for automatic speech recognition. We explore three aspects of
the pruning frame work, namely criterion, method and scheduler, analyzing their
contribution in terms of accuracy and inference speed. To the best of our
knowledge, such in-depth analyses on large-scale recognition systems has not
been reported in the literature. In addition, we propose a variant of low-rank
approximation suitable for incrementally compressing models, and delivering
multiple models with varied target sizes. Among other results, we show that a)
data-driven pruning outperforms magnitude-driven in several scenarios; b)
incremental pruning achieves higher accuracy compared to one-shot pruning,
especially when targeting smaller sizes; and c) low-rank approximation presents
the best trade-off between size reduction and inference speed-up for moderate
compression. | [
"Leonardo Emili",
"Thiago Fraga-Silva",
"Ernest Pusateri",
"Markus Nußbaum-Thom",
"Youssef Oualil"
] | 2023-10-05 10:01:32 | http://arxiv.org/abs/2310.03424v1 | http://arxiv.org/pdf/2310.03424v1 | 2310.03424v1 |
Pre-Training and Fine-Tuning Generative Flow Networks | Generative Flow Networks (GFlowNets) are amortized samplers that learn
stochastic policies to sequentially generate compositional objects from a given
unnormalized reward distribution. They can generate diverse sets of high-reward
objects, which is an important consideration in scientific discovery tasks.
However, as they are typically trained from a given extrinsic reward function,
it remains an important open challenge about how to leverage the power of
pre-training and train GFlowNets in an unsupervised fashion for efficient
adaptation to downstream tasks. Inspired by recent successes of unsupervised
pre-training in various domains, we introduce a novel approach for reward-free
pre-training of GFlowNets. By framing the training as a self-supervised
problem, we propose an outcome-conditioned GFlowNet (OC-GFN) that learns to
explore the candidate space. Specifically, OC-GFN learns to reach any targeted
outcomes, akin to goal-conditioned policies in reinforcement learning. We show
that the pre-trained OC-GFN model can allow for a direct extraction of a policy
capable of sampling from any new reward functions in downstream tasks.
Nonetheless, adapting OC-GFN on a downstream task-specific reward involves an
intractable marginalization over possible outcomes. We propose a novel way to
approximate this marginalization by learning an amortized predictor enabling
efficient fine-tuning. Extensive experimental results validate the efficacy of
our approach, demonstrating the effectiveness of pre-training the OC-GFN, and
its ability to swiftly adapt to downstream tasks and discover modes more
efficiently. This work may serve as a foundation for further exploration of
pre-training strategies in the context of GFlowNets. | [
"Ling Pan",
"Moksh Jain",
"Kanika Madan",
"Yoshua Bengio"
] | 2023-10-05 09:53:22 | http://arxiv.org/abs/2310.03419v1 | http://arxiv.org/pdf/2310.03419v1 | 2310.03419v1 |
Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary? | Over-the-Air (OtA) Federated Learning (FL) refers to an FL system where
multiple agents apply OtA computation for transmitting model updates to a
common edge server. Two important features of OtA computation, namely linear
processing and signal-level superposition, motivate the use of linear
compression with compressed sensing (CS) methods to reduce the number of data
samples transmitted over the channel. The previous works on applying CS methods
in OtA FL have primarily assumed that the original model update vectors are
sparse, or they have been sparsified before compression. However, it is unclear
whether linear compression with CS-based reconstruction is more effective than
directly sending the non-zero elements in the sparsified update vectors, under
the same total power constraint. In this study, we examine and compare several
communication designs with or without sparsification. Our findings demonstrate
that sparsification before compression is not necessary. Alternatively,
sparsification without linear compression can also achieve better performance
than the commonly considered setup that combines both. | [
"Adrian Edin",
"Zheng Chen"
] | 2023-10-05 09:29:23 | http://arxiv.org/abs/2310.03410v1 | http://arxiv.org/pdf/2310.03410v1 | 2310.03410v1 |
RUSOpt: Robotic UltraSound Probe Normalization with Bayesian Optimization for In-plane and Out-plane Scanning | The one of the significant challenges faced by autonomous robotic ultrasound
systems is acquiring high-quality images across different patients. The proper
orientation of the robotized probe plays a crucial role in governing the
quality of ultrasound images. To address this challenge, we propose a
sample-efficient method to automatically adjust the orientation of the
ultrasound probe normal to the point of contact on the scanning surface,
thereby improving the acoustic coupling of the probe and resulting image
quality. Our method utilizes Bayesian Optimization (BO) based search on the
scanning surface to efficiently search for the normalized probe orientation. We
formulate a novel objective function for BO that leverages the contact force
measurements and underlying mechanics to identify the normal. We further
incorporate a regularization scheme in BO to handle the noisy objective
function. The performance of the proposed strategy has been assessed through
experiments on urinary bladder phantoms. These phantoms included planar,
tilted, and rough surfaces, and were examined using both linear and convex
probes with varying search space limits. Further, simulation-based studies have
been carried out using 3D human mesh models. The results demonstrate that the
mean ($\pm$SD) absolute angular error averaged over all phantoms and 3D models
is $\boldsymbol{2.4\pm0.7^\circ}$ and $\boldsymbol{2.1\pm1.3^\circ}$,
respectively. | [
"Deepak Raina",
"Abhishek Mathur",
"Richard M. Voyles",
"Juan Wachs",
"SH Chandrashekhara",
"Subir Kumar Saha"
] | 2023-10-05 09:22:16 | http://arxiv.org/abs/2310.03406v1 | http://arxiv.org/pdf/2310.03406v1 | 2310.03406v1 |
EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation Learning | Deep learning models based on resting-state functional magnetic resonance
imaging (rs-fMRI) have been widely used to diagnose brain diseases,
particularly autism spectrum disorder (ASD). Existing studies have leveraged
the functional connectivity (FC) of rs-fMRI, achieving notable classification
performance. However, they have significant limitations, including the lack of
adequate information while using linear low-order FC as inputs to the model,
not considering individual characteristics (i.e., different symptoms or varying
stages of severity) among patients with ASD, and the non-explainability of the
decision process. To cover these limitations, we propose a novel
explainability-guided region of interest (ROI) selection (EAG-RS) framework
that identifies non-linear high-order functional associations among brain
regions by leveraging an explainable artificial intelligence technique and
selects class-discriminative regions for brain disease identification. The
proposed framework includes three steps: (i) inter-regional relation learning
to estimate non-linear relations through random seed-based network masking,
(ii) explainable connection-wise relevance score estimation to explore
high-order relations between functional connections, and (iii) non-linear
high-order FC-based diagnosis-informative ROI selection and classifier learning
to identify ASD. We validated the effectiveness of our proposed method by
conducting experiments using the Autism Brain Imaging Database Exchange (ABIDE)
dataset, demonstrating that the proposed method outperforms other comparative
methods in terms of various evaluation metrics. Furthermore, we qualitatively
analyzed the selected ROIs and identified ASD subtypes linked to previous
neuroscientific studies. | [
"Wonsik Jung",
"Eunjin Jeon",
"Eunsong Kang",
"Heung-Il Suk"
] | 2023-10-05 09:14:54 | http://arxiv.org/abs/2310.03404v1 | http://arxiv.org/pdf/2310.03404v1 | 2310.03404v1 |
Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning | Nowadays, billions of people engage in communication and express their
opinions on the internet daily. Unfortunately, not all of these expressions are
friendly or compliant, making content moderation an indispensable task. With
the successful development of Large Language Models (LLMs) in recent years,
LLM-based methods have become a feasible solution for handling tasks in various
domains. However, in the field of content moderation, there is still a lack of
detailed work that systematically introduces implementation details. In this
paper, we introduce how to fine-tune an LLM model that can be privately
deployed for content moderation. Specifically, we discuss whether incorporating
reasons during the fine-tuning process would be better or if it should be
treated as a classification task directly. We also explore the benefits of
utilizing reasons generated by more powerful LLMs for fine-tuning privately
deployed models and the impact of different processing approaches when the
answers generated by the more powerful LLMs are incorrect. We report the entire
research process and the key findings in this paper, hoping to provide valuable
experience for researchers who are fine-tuning privately deployed models in
their domain-specific research. | [
"Huan Ma",
"Changqing Zhang",
"Huazhu Fu",
"Peilin Zhao",
"Bingzhe Wu"
] | 2023-10-05 09:09:44 | http://arxiv.org/abs/2310.03400v1 | http://arxiv.org/pdf/2310.03400v1 | 2310.03400v1 |
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks | Graph neural networks (GNNs) learn the representation of nodes in a graph by
aggregating the neighborhood information in various ways. As these networks
grow in depth, their receptive field grows exponentially due to the increase in
neighborhood sizes, resulting in high memory costs. Graph sampling solves
memory issues in GNNs by sampling a small ratio of the nodes in the graph. This
way, GNNs can scale to much larger graphs. Most sampling methods focus on fixed
sampling heuristics, which may not generalize to different structures or tasks.
We introduce GRAPES, an adaptive graph sampling method that learns to identify
sets of influential nodes for training a GNN classifier. GRAPES uses a GFlowNet
to learn node sampling probabilities given the classification objectives. We
evaluate GRAPES across several small- and large-scale graph benchmarks and
demonstrate its effectiveness in accuracy and scalability. In contrast to
existing sampling methods, GRAPES maintains high accuracy even with small
sample sizes and, therefore, can scale to very large graphs. Our code is
publicly available at https://github.com/dfdazac/grapes. | [
"Taraneh Younesian",
"Thiviyan Thanapalasingam",
"Emile van Krieken",
"Daniel Daza",
"Peter Bloem"
] | 2023-10-05 09:08:47 | http://arxiv.org/abs/2310.03399v1 | http://arxiv.org/pdf/2310.03399v1 | 2310.03399v1 |
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein | We present a versatile adaptation of existing dimensionality reduction (DR)
objectives, enabling the simultaneous reduction of both sample and feature
sizes. Correspondances between input and embedding samples are computed through
a semi-relaxed Gromov-Wasserstein optimal transport (OT) problem. When the
embedding sample size matches that of the input, our model recovers classical
popular DR models. When the embedding's dimensionality is unconstrained, we
show that the OT plan delivers a competitive hard clustering. We emphasize the
importance of intermediate stages that blend DR and clustering for summarizing
real data and apply our method to visualize datasets of images. | [
"Hugues Van Assel",
"Cédric Vincent-Cuaz",
"Titouan Vayer",
"Rémi Flamary",
"Nicolas Courty"
] | 2023-10-05 09:04:53 | http://arxiv.org/abs/2310.03398v1 | http://arxiv.org/pdf/2310.03398v1 | 2310.03398v1 |
Learning to Simplify Spatial-Temporal Graphs in Gait Analysis | Gait analysis leverages unique walking patterns for person identification and
assessment across multiple domains. Among the methods used for gait analysis,
skeleton-based approaches have shown promise due to their robust and
interpretable features. However, these methods often rely on hand-crafted
spatial-temporal graphs that are based on human anatomy disregarding the
particularities of the dataset and task. This paper proposes a novel method to
simplify the spatial-temporal graph representation for gait-based gender
estimation, improving interpretability without losing performance. Our approach
employs two models, an upstream and a downstream model, that can adjust the
adjacency matrix for each walking instance, thereby removing the fixed nature
of the graph. By employing the Straight-Through Gumbel-Softmax trick, our model
is trainable end-to-end. We demonstrate the effectiveness of our approach on
the CASIA-B dataset for gait-based gender estimation. The resulting graphs are
interpretable and differ qualitatively from fixed graphs used in existing
models. Our research contributes to enhancing the explainability and
task-specific adaptability of gait recognition, promoting more efficient and
reliable gait-based biometrics. | [
"Adrian Cosma",
"Emilian Radoi"
] | 2023-10-05 09:03:51 | http://arxiv.org/abs/2310.03396v1 | http://arxiv.org/pdf/2310.03396v1 | 2310.03396v1 |
Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations | Deep learning-based numerical schemes for solving high-dimensional backward
stochastic differential equations (BSDEs) have recently raised plenty of
scientific interest. While they enable numerical methods to approximate very
high-dimensional BSDEs, their reliability has not been studied and is thus not
understood. In this work, we study uncertainty quantification (UQ) for a class
of deep learning-based BSDE schemes. More precisely, we review the sources of
uncertainty involved in the schemes and numerically study the impact of
different sources. Usually, the standard deviation (STD) of the approximate
solutions obtained from multiple runs of the algorithm with different datasets
is calculated to address the uncertainty. This approach is computationally
quite expensive, especially for high-dimensional problems. Hence, we develop a
UQ model that efficiently estimates the STD of the approximate solution using
only a single run of the algorithm. The model also estimates the mean of the
approximate solution, which can be leveraged to initialize the algorithm and
improve the optimization process. Our numerical experiments show that the UQ
model produces reliable estimates of the mean and STD of the approximate
solution for the considered class of deep learning-based BSDE schemes. The
estimated STD captures multiple sources of uncertainty, demonstrating its
effectiveness in quantifying the uncertainty. Additionally, the model
illustrates the improved performance when comparing different schemes based on
the estimated STD values. Furthermore, it can identify hyperparameter values
for which the scheme achieves good approximations. | [
"Lorenc Kapllani",
"Long Teng",
"Matthias Rottmann"
] | 2023-10-05 09:00:48 | http://arxiv.org/abs/2310.03393v1 | http://arxiv.org/pdf/2310.03393v1 | 2310.03393v1 |
OpenPatch: a 3D patchwork for Out-Of-Distribution detection | Moving deep learning models from the laboratory setting to the open world
entails preparing them to handle unforeseen conditions. In several applications
the occurrence of novel classes during deployment poses a significant threat,
thus it is crucial to effectively detect them. Ideally, this skill should be
used when needed without requiring any further computational training effort at
every new task. Out-of-distribution detection has attracted significant
attention in the last years, however the majority of the studies deal with 2D
images ignoring the inherent 3D nature of the real-world and often confusing
between domain and semantic novelty. In this work, we focus on the latter,
considering the objects geometric structure captured by 3D point clouds
regardless of the specific domain. We advance the field by introducing
OpenPatch that builds on a large pre-trained model and simply extracts from its
intermediate features a set of patch representations that describe each known
class. For any new sample, we obtain a novelty score by evaluating whether it
can be recomposed mainly by patches of a single known class or rather via the
contribution of multiple classes. We present an extensive experimental
evaluation of our approach for the task of semantic novelty detection on
real-world point cloud samples when the reference known data are synthetic. We
demonstrate that OpenPatch excels in both the full and few-shot known sample
scenarios, showcasing its robustness across varying pre-training objectives and
network backbones. The inherent training-free nature of our method allows for
its immediate application to a wide array of real-world tasks, offering a
compelling advantage over approaches that need expensive retraining efforts. | [
"Paolo Rabino",
"Antonio Alliegro",
"Francesco Cappio Borlino",
"Tatiana Tommasi"
] | 2023-10-05 08:49:51 | http://arxiv.org/abs/2310.03388v2 | http://arxiv.org/pdf/2310.03388v2 | 2310.03388v2 |
Machine learning the interaction network in coupled dynamical systems | The study of interacting dynamical systems continues to attract research
interest in various fields of science and engineering. In a collection of
interacting particles, the interaction network contains information about how
various components interact with one another. Inferring the information about
the interaction network from the dynamics of agents is a problem of
long-standing interest. In this work, we employ a self-supervised neural
network model to achieve two outcomes: to recover the interaction network and
to predict the dynamics of individual agents. Both these information are
inferred solely from the observed trajectory data. This work presents an
application of the Neural Relational Inference model to two dynamical systems:
coupled particles mediated by Hooke's law interaction and coupled phase
(Kuramoto) oscillators. | [
"Pawan R. Bhure",
"M. S. Santhanam"
] | 2023-10-05 08:29:00 | http://arxiv.org/abs/2310.03378v1 | http://arxiv.org/pdf/2310.03378v1 | 2310.03378v1 |
Swin-Tempo: Temporal-Aware Lung Nodule Detection in CT Scans as Video Sequences Using Swin Transformer-Enhanced UNet | Lung cancer is highly lethal, emphasizing the critical need for early
detection. However, identifying lung nodules poses significant challenges for
radiologists, who rely heavily on their expertise for accurate diagnosis. To
address this issue, computer-aided diagnosis (CAD) systems based on machine
learning techniques have emerged to assist doctors in identifying lung nodules
from computed tomography (CT) scans. Unfortunately, existing networks in this
domain often suffer from computational complexity, leading to high rates of
false negatives and false positives, limiting their effectiveness. To address
these challenges, we present an innovative model that harnesses the strengths
of both convolutional neural networks and vision transformers. Inspired by
object detection in videos, we treat each 3D CT image as a video, individual
slices as frames, and lung nodules as objects, enabling a time-series
application. The primary objective of our work is to overcome hardware
limitations during model training, allowing for efficient processing of 2D data
while utilizing inter-slice information for accurate identification based on 3D
image context. We validated the proposed network by applying a 10-fold
cross-validation technique to the publicly available Lung Nodule Analysis 2016
dataset. Our proposed architecture achieves an average sensitivity criterion of
97.84% and a competition performance metrics (CPM) of 96.0% with few
parameters. Comparative analysis with state-of-the-art advancements in lung
nodule identification demonstrates the significant accuracy achieved by our
proposed model. | [
"Hossein Jafari",
"Karim Faez",
"Hamidreza Amindavar"
] | 2023-10-05 07:48:55 | http://arxiv.org/abs/2310.03365v2 | http://arxiv.org/pdf/2310.03365v2 | 2310.03365v2 |
Robust Representation Learning via Asymmetric Negative Contrast and Reverse Attention | Deep neural networks are vulnerable to adversarial noise. Adversarial
training (AT) has been demonstrated to be the most effective defense strategy
to protect neural networks from being fooled. However, we find AT omits to
learning robust features, resulting in poor performance of adversarial
robustness. To address this issue, we highlight two characteristics of robust
representation: (1) $\bf{exclusion}$: the feature of natural examples keeps
away from that of other classes; (2) $\bf{alignment}$: the feature of natural
and corresponding adversarial examples is close to each other. These motivate
us to propose a generic framework of AT to gain robust representation, by the
asymmetric negative contrast and reverse attention. Specifically, we design an
asymmetric negative contrast based on predicted probabilities, to push away
examples of different classes in the feature space. Moreover, we propose to
weight feature by parameters of the linear classifier as the reverse attention,
to obtain class-aware feature and pull close the feature of the same class.
Empirical evaluations on three benchmark datasets show our methods greatly
advance the robustness of AT and achieve state-of-the-art performance. Code is
available at <https://github.com/changzhang777/ANCRA>. | [
"Nuoyan Zhou",
"Decheng Liu",
"Dawei Zhou",
"Xinbo Gao",
"Nannan Wang"
] | 2023-10-05 07:29:29 | http://arxiv.org/abs/2310.03358v1 | http://arxiv.org/pdf/2310.03358v1 | 2310.03358v1 |
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games | Self-play (SP) is a popular multi-agent reinforcement learning (MARL)
framework for solving competitive games, where each agent optimizes policy by
treating others as part of the environment. Despite the empirical successes,
the theoretical properties of SP-based methods are limited to two-player
zero-sum games. However, for mixed cooperative-competitive games where agents
on the same team need to cooperate with each other, we can show a simple
counter-example where SP-based methods cannot converge to a global Nash
equilibrium (NE) with high probability. Alternatively, Policy-Space Response
Oracles (PSRO) is an iterative framework for learning NE, where the best
responses w.r.t. previous policies are learned in each iteration. PSRO can be
directly extended to mixed cooperative-competitive settings by jointly learning
team best responses with all convergence properties unchanged. However, PSRO
requires repeatedly training joint policies from scratch till convergence,
which makes it hard to scale to complex games. In this work, we develop a novel
algorithm, Fictitious Cross-Play (FXP), which inherits the benefits from both
frameworks. FXP simultaneously trains an SP-based main policy and a counter
population of best response policies. The main policy is trained by fictitious
self-play and cross-play against the counter population, while the counter
policies are trained as the best responses to the main policy's past versions.
We validate our method in matrix games and show that FXP converges to global
NEs while SP methods fail. We also conduct experiments in a gridworld domain,
where FXP achieves higher Elo ratings and lower exploitabilities than
baselines, and a more challenging football game, where FXP defeats SOTA models
with over 94% win rate. | [
"Zelai Xu",
"Yancheng Liang",
"Chao Yu",
"Yu Wang",
"Yi Wu"
] | 2023-10-05 07:19:33 | http://arxiv.org/abs/2310.03354v1 | http://arxiv.org/pdf/2310.03354v1 | 2310.03354v1 |
Deep Geometric Learning with Monotonicity Constraints for Alzheimer's Disease Progression | Alzheimer's disease (AD) is a devastating neurodegenerative condition that
precedes progressive and irreversible dementia; thus, predicting its
progression over time is vital for clinical diagnosis and treatment. Numerous
studies have implemented structural magnetic resonance imaging (MRI) to model
AD progression, focusing on three integral aspects: (i) temporal variability,
(ii) incomplete observations, and (iii) temporal geometric characteristics.
However, deep learning-based approaches regarding data variability and sparsity
have yet to consider inherent geometrical properties sufficiently. The ordinary
differential equation-based geometric modeling method (ODE-RGRU) has recently
emerged as a promising strategy for modeling time-series data by intertwining a
recurrent neural network and an ODE in Riemannian space. Despite its
achievements, ODE-RGRU encounters limitations when extrapolating positive
definite symmetric metrics from incomplete samples, leading to feature reverse
occurrences that are particularly problematic, especially within the clinical
facet. Therefore, this study proposes a novel geometric learning approach that
models longitudinal MRI biomarkers and cognitive scores by combining three
modules: topological space shift, ODE-RGRU, and trajectory estimation. We have
also developed a training algorithm that integrates manifold mapping with
monotonicity constraints to reflect measurement transition irreversibility. We
verify our proposed method's efficacy by predicting clinical labels and
cognitive scores over time in regular and irregular settings. Furthermore, we
thoroughly analyze our proposed framework through an ablation study. | [
"Seungwoo Jeong",
"Wonsik Jung",
"Junghyo Sohn",
"Heung-Il Suk"
] | 2023-10-05 07:14:34 | http://arxiv.org/abs/2310.03353v1 | http://arxiv.org/pdf/2310.03353v1 | 2310.03353v1 |
An Integrated Algorithm for Robust and Imperceptible Audio Adversarial Examples | Audio adversarial examples are audio files that have been manipulated to fool
an automatic speech recognition (ASR) system, while still sounding benign to a
human listener. Most methods to generate such samples are based on a two-step
algorithm: first, a viable adversarial audio file is produced, then, this is
fine-tuned with respect to perceptibility and robustness. In this work, we
present an integrated algorithm that uses psychoacoustic models and room
impulse responses (RIR) in the generation step. The RIRs are dynamically
created by a neural network during the generation process to simulate a
physical environment to harden our examples against transformations experienced
in over-the-air attacks. We compare the different approaches in three
experiments: in a simulated environment and in a realistic over-the-air
scenario to evaluate the robustness, and in a human study to evaluate the
perceptibility. Our algorithms considering psychoacoustics only or in addition
to the robustness show an improvement in the signal-to-noise ratio (SNR) as
well as in the human perception study, at the cost of an increased word error
rate (WER). | [
"Armin Ettenhofer",
"Jan-Philipp Schulze",
"Karla Pizzi"
] | 2023-10-05 06:59:09 | http://arxiv.org/abs/2310.03349v1 | http://arxiv.org/pdf/2310.03349v1 | 2310.03349v1 |
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework | In this paper, a unified framework for exploration in reinforcement learning
(RL) is proposed based on an option-critic model. The proposed framework learns
to integrate a set of diverse exploration strategies so that the agent can
adaptively select the most effective exploration strategy over time to realize
a relevant exploration-exploitation trade-off for each given task. The
effectiveness of the proposed exploration framework is demonstrated by various
experiments in the MiniGrid and Atari environments. | [
"Woojun Kim",
"Jeonghye Kim",
"Youngchul Sung"
] | 2023-10-05 06:49:52 | http://arxiv.org/abs/2310.03342v1 | http://arxiv.org/pdf/2310.03342v1 | 2310.03342v1 |
Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs | Accurate forecasts of electricity prices are crucial for the management of
electric power systems and the development of smart applications. European
electricity prices have risen substantially and became highly volatile after
the Russian invasion of Ukraine, challenging established forecasting methods.
Here, we present a Long Short-Term Memory (LSTM) model for the
German-Luxembourg day-ahead electricity prices addressing these challenges. The
recurrent structure of the LSTM allows the model to adapt to trends, while the
joint prediction of both mean and standard deviation enables a probabilistic
prediction. Using a physics-inspired approach - superstatistics - to derive an
explanation for the statistics of prices, we show that the LSTM model
faithfully reproduces both prices and their volatility. | [
"Julius Trebbien",
"Sebastian Pütz",
"Benjamin Schäfer",
"Heidi S. Nygård",
"Leonardo Rydin Gorjão",
"Dirk Witthaut"
] | 2023-10-05 06:47:28 | http://arxiv.org/abs/2310.03339v1 | http://arxiv.org/pdf/2310.03339v1 | 2310.03339v1 |
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System | Network Intrusion Detection System (NIDS) is a key component in securing the
computer network from various cyber security threats and network attacks.
However, consider an unfortunate situation where the NIDS is itself attacked
and vulnerable more specifically, we can say, How to defend the defender?. In
Adversarial Machine Learning (AML), the malicious actors aim to fool the
Machine Learning (ML) and Deep Learning (DL) models to produce incorrect
predictions with intentionally crafted adversarial examples. These adversarial
perturbed examples have become the biggest vulnerability of ML and DL based
systems and are major obstacles to their adoption in real-time and
mission-critical applications such as NIDS. AML is an emerging research domain,
and it has become a necessity for the in-depth study of adversarial attacks and
their defence strategies to safeguard the computer network from various cyber
security threads. In this research work, we aim to cover important aspects
related to NIDS, adversarial attacks and its defence mechanism to increase the
robustness of the ML and DL based NIDS. We implemented four powerful
adversarial attack techniques, namely, Fast Gradient Sign Method (FGSM),
Jacobian Saliency Map Attack (JSMA), Projected Gradient Descent (PGD) and
Carlini & Wagner (C&W) in NIDS. We analyzed its performance in terms of various
performance metrics in detail. Furthermore, the three heuristics defence
strategies, i.e., Adversarial Training (AT), Gaussian Data Augmentation (GDA)
and High Confidence (HC), are implemented to improve the NIDS robustness under
adversarial attack situations. The complete workflow is demonstrated in
real-time network with data packet flow. This research work provides the
overall background for the researchers interested in AML and its implementation
from a computer network security point of view. | [
"Khushnaseeb Roshan",
"Aasim Zafar",
"Sheikh Burhan Ul Haque"
] | 2023-10-05 06:32:56 | http://arxiv.org/abs/2310.03334v2 | http://arxiv.org/pdf/2310.03334v2 | 2310.03334v2 |
Fine-tune Language Models to Approximate Unbiased In-context Learning | In-context learning (ICL) is an astonishing emergent ability of large
language models (LLMs). By presenting a prompt that includes multiple
input-output pairs as examples and introducing a new query input, models can
generate the corresponding output. However, the performance of models heavily
relies on the quality of the input prompt when implementing in-context
learning. Biased or imbalanced input prompts can significantly degrade the
performance of language models. To address this issue, we introduce a
reweighted algorithm called RICL (Reweighted In-context Learning). This
algorithm fine-tunes language models using an unbiased validation set to
determine the optimal weight for each input-output example to approximate
unbiased in-context learning. Furthermore, we also introduce a low-cost
reweighted algorithm, a linear optimal weight approximation algorithm called
LARICL (Linear Approximation of Reweighted In-context Learning). This algorithm
requires minimal training cost while providing effective results. We prove the
convergence of our algorithm and validate its performance through experiments
conducted on a numerical dataset. The experimental findings reveal a
substantial improvement in comparison to benchmarks including the performance
of casual prompt-based in-context learning and the performance of a classic
fine-tuning method. | [
"Timothy Chu",
"Zhao Song",
"Chiwun Yang"
] | 2023-10-05 06:16:01 | http://arxiv.org/abs/2310.03331v1 | http://arxiv.org/pdf/2310.03331v1 | 2310.03331v1 |
Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening | Conventional deep learning methods typically employ supervised learning for
drug response prediction (DRP). This entails dependence on labeled response
data from drugs for model training. However, practical applications in the
preclinical drug screening phase demand that DRP models predict responses for
novel compounds, often with unknown drug responses. This presents a challenge,
rendering supervised deep learning methods unsuitable for such scenarios. In
this paper, we propose a zero-shot learning solution for the DRP task in
preclinical drug screening. Specifically, we propose a Multi-branch
Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can
be seamlessly integrated with conventional DRP methods, learning invariant
features from the prior response data of similar drugs to enhance real-time
predictions of unlabeled compounds. We conducted experiments using the GDSCv2
and CellMiner datasets. The results demonstrate that MSDA efficiently predicts
drug responses for novel compounds, leading to a general performance
improvement of 5-10\% in the preclinical drug screening phase. The significance
of this solution resides in its potential to accelerate the drug discovery
process, improve drug candidate assessment, and facilitate the success of drug
discovery. | [
"Kun Li",
"Yong Luo",
"Xiantao Cai",
"Wenbin Hu",
"Bo Du"
] | 2023-10-05 05:55:41 | http://arxiv.org/abs/2310.12996v1 | http://arxiv.org/pdf/2310.12996v1 | 2310.12996v1 |
Learning Concept-Based Visual Causal Transition and Symbolic Reasoning for Visual Planning | Visual planning simulates how humans make decisions to achieve desired goals
in the form of searching for visual causal transitions between an initial
visual state and a final visual goal state. It has become increasingly
important in egocentric vision with its advantages in guiding agents to perform
daily tasks in complex environments. In this paper, we propose an interpretable
and generalizable visual planning framework consisting of i) a novel
Substitution-based Concept Learner (SCL) that abstracts visual inputs into
disentangled concept representations, ii) symbol abstraction and reasoning that
performs task planning via the self-learned symbols, and iii) a Visual Causal
Transition model (ViCT) that grounds visual causal transitions to semantically
similar real-world actions. Given an initial state, we perform goal-conditioned
visual planning with a symbolic reasoning method fueled by the learned
representations and causal transitions to reach the goal state. To verify the
effectiveness of the proposed model, we collect a large-scale visual planning
dataset based on AI2-THOR, dubbed as CCTP. Extensive experiments on this
challenging dataset demonstrate the superior performance of our method in
visual task planning. Empirically, we show that our framework can generalize to
unseen task trajectories and unseen object categories. | [
"Yilue Qian",
"Peiyu Yu",
"Ying Nian Wu",
"Wei Wang",
"Lifeng Fan"
] | 2023-10-05 05:41:21 | http://arxiv.org/abs/2310.03325v1 | http://arxiv.org/pdf/2310.03325v1 | 2310.03325v1 |
Investigating the Limitation of CLIP Models: The Worst-Performing Categories | Contrastive Language-Image Pre-training (CLIP) provides a foundation model by
integrating natural language into visual concepts, enabling zero-shot
recognition on downstream tasks. It is usually expected that satisfactory
overall accuracy can be achieved across numerous domains through well-designed
textual prompts. However, we found that their performance in the worst
categories is significantly inferior to the overall performance. For example,
on ImageNet, there are a total of 10 categories with class-wise accuracy as low
as 0\%, even though the overall performance has achieved 64.1\%. This
phenomenon reveals the potential risks associated with using CLIP models,
particularly in risk-sensitive applications where specific categories hold
significant importance. To address this issue, we investigate the alignment
between the two modalities in the CLIP model and propose the Class-wise
Matching Margin (\cmm) to measure the inference confusion. \cmm\ can
effectively identify the worst-performing categories and estimate the potential
performance of the candidate prompts. We further query large language models to
enrich descriptions of worst-performing categories and build a weighted
ensemble to highlight the efficient prompts. Experimental results clearly
verify the effectiveness of our proposal, where the accuracy on the worst-10
categories on ImageNet is boosted to 5.2\%, without manual prompt engineering,
laborious optimization, or access to labeled validation data. | [
"Jie-Jing Shao",
"Jiang-Xin Shi",
"Xiao-Wen Yang",
"Lan-Zhe Guo",
"Yu-Feng Li"
] | 2023-10-05 05:37:33 | http://arxiv.org/abs/2310.03324v1 | http://arxiv.org/pdf/2310.03324v1 | 2310.03324v1 |
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graph | Foundation models (FMs) are able to leverage large volumes of unlabeled data
to demonstrate superior performance across a wide range of tasks. However, FMs
developed for biomedical domains have largely remained unimodal, i.e.,
independently trained and used for tasks on protein sequences alone, small
molecule structures alone, or clinical data alone. To overcome this limitation
of biomedical FMs, we present BioBridge, a novel parameter-efficient learning
framework, to bridge independently trained unimodal FMs to establish multimodal
behavior. BioBridge achieves it by utilizing Knowledge Graphs (KG) to learn
transformations between one unimodal FM and another without fine-tuning any
underlying unimodal FMs. Our empirical results demonstrate that BioBridge can
beat the best baseline KG embedding methods (on average by around 76.3%) in
cross-modal retrieval tasks. We also identify BioBridge demonstrates
out-of-domain generalization ability by extrapolating to unseen modalities or
relations. Additionally, we also show that BioBridge presents itself as a
general purpose retriever that can aid biomedical multimodal question answering
as well as enhance the guided generation of novel drugs. | [
"Zifeng Wang",
"Zichen Wang",
"Balasubramaniam Srinivasan",
"Vassilis N. Ioannidis",
"Huzefa Rangwala",
"Rishita Anubhai"
] | 2023-10-05 05:30:42 | http://arxiv.org/abs/2310.03320v2 | http://arxiv.org/pdf/2310.03320v2 | 2310.03320v2 |
Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction | Human motion prediction is an essential step for efficient and safe
human-robot collaboration. Current methods either purely rely on representing
the human joints in some form of neural network-based architecture or use
regression models offline to fit hyper-parameters in the hope of capturing a
model encompassing human motion. While these methods provide good initial
results, they are missing out on leveraging well-studied human body kinematic
models as well as body and scene constraints which can help boost the efficacy
of these prediction frameworks while also explicitly avoiding implausible human
joint configurations. We propose a novel human motion prediction framework that
incorporates human joint constraints and scene constraints in a Gaussian
Process Regression (GPR) model to predict human motion over a set time horizon.
This formulation is combined with an online context-aware constraints model to
leverage task-dependent motions. It is tested on a human arm kinematic model
and implemented on a human-robot collaborative setup with a UR5 robot arm to
demonstrate the real-time capability of our approach. Simulations were also
performed on datasets like HA4M and ANDY. The simulation and experimental
results demonstrate considerable improvements in a Gaussian Process framework
when these constraints are explicitly considered. | [
"Aadi Kothari",
"Tony Tohme",
"Xiaotong Zhang",
"Kamal Youcef-Toumi"
] | 2023-10-05 05:12:14 | http://arxiv.org/abs/2310.03314v1 | http://arxiv.org/pdf/2310.03314v1 | 2310.03314v1 |
Certifiably Robust Graph Contrastive Learning | Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph
representation learning method. However, it has been shown that GCL is
vulnerable to adversarial attacks on both the graph structure and node
attributes. Although empirical approaches have been proposed to enhance the
robustness of GCL, the certifiable robustness of GCL is still remain
unexplored. In this paper, we develop the first certifiably robust framework in
GCL. Specifically, we first propose a unified criteria to evaluate and certify
the robustness of GCL. We then introduce a novel technique, RES (Randomized
Edgedrop Smoothing), to ensure certifiable robustness for any GCL model, and
this certified robustness can be provably preserved in downstream tasks.
Furthermore, an effective training method is proposed for robust GCL. Extensive
experiments on real-world datasets demonstrate the effectiveness of our
proposed method in providing effective certifiable robustness and enhancing the
robustness of any GCL model. The source code of RES is available at
https://github.com/ventr1c/RES-GCL. | [
"Minhua Lin",
"Teng Xiao",
"Enyan Dai",
"Xiang Zhang",
"Suhang Wang"
] | 2023-10-05 05:00:11 | http://arxiv.org/abs/2310.03312v1 | http://arxiv.org/pdf/2310.03312v1 | 2310.03312v1 |
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses | Variational dimensionality reduction methods are known for their high
accuracy, generative abilities, and robustness. These methods have many
theoretical justifications. Here we introduce a unifying principle rooted in
information theory to rederive and generalize existing variational methods and
design new ones. We base our framework on an interpretation of the multivariate
information bottleneck, in which two Bayesian networks are traded off against
one another. We interpret the first network as an encoder graph, which
specifies what information to keep when compressing the data. We interpret the
second network as a decoder graph, which specifies a generative model for the
data. Using this framework, we rederive existing dimensionality reduction
methods such as the deep variational information bottleneck (DVIB), beta
variational auto-encoders (beta-VAE), and deep variational canonical
correlation analysis (DVCCA). The framework naturally introduces a trade-off
parameter between compression and reconstruction in the DVCCA family of
algorithms, resulting in the new beta-DVCCA family. In addition, we derive a
new variational dimensionality reduction method, deep variational symmetric
informational bottleneck (DVSIB), which simultaneously compresses two variables
to preserve information between their compressed representations. We implement
all of these algorithms and evaluate their ability to produce shared low
dimensional latent spaces on a modified noisy MNIST dataset. We show that
algorithms that are better matched to the structure of the data (beta-DVCCA and
DVSIB) produce better latent spaces as measured by classification accuracy and
the dimensionality of the latent variables. We believe that this framework can
be used to unify other multi-view representation learning algorithms.
Additionally, it provides a straightforward framework for deriving
problem-specific loss functions. | [
"Eslam Abdelaleem",
"Ilya Nemenman",
"K. Michael Martini"
] | 2023-10-05 04:59:58 | http://arxiv.org/abs/2310.03311v1 | http://arxiv.org/pdf/2310.03311v1 | 2310.03311v1 |
Functional data learning using convolutional neural networks | In this paper, we show how convolutional neural networks (CNN) can be used in
regression and classification learning problems of noisy and non-noisy
functional data. The main idea is to transform the functional data into a 28 by
28 image. We use a specific but typical architecture of a convolutional neural
network to perform all the regression exercises of parameter estimation and
functional form classification. First, we use some functional case studies of
functional data with and without random noise to showcase the strength of the
new method. In particular, we use it to estimate exponential growth and decay
rates, the bandwidths of sine and cosine functions, and the magnitudes and
widths of curve peaks. We also use it to classify the monotonicity and
curvatures of functional data, algebraic versus exponential growth, and the
number of peaks of functional data. Second, we apply the same convolutional
neural networks to Lyapunov exponent estimation in noisy and non-noisy chaotic
data, in estimating rates of disease transmission from epidemic curves, and in
detecting the similarity of drug dissolution profiles. Finally, we apply the
method to real-life data to detect Parkinson's disease patients in a
classification problem. The method, although simple, shows high accuracy and is
promising for future use in engineering and medical applications. | [
"Jose Galarza",
"Tamer Oraby"
] | 2023-10-05 04:46:52 | http://arxiv.org/abs/2310.03773v1 | http://arxiv.org/pdf/2310.03773v1 | 2310.03773v1 |
Benchmarking Large Language Models As AI Research Agents | Scientific experimentation involves an iterative process of creating
hypotheses, designing experiments, running experiments, and analyzing the
results. Can we build AI research agents to perform these long-horizon tasks?
To take a step towards building and evaluating research agents on such
open-ended decision-making tasks, we focus on the problem of machine learning
engineering: given a task description and a dataset, build a high-performing
model. In this paper, we propose MLAgentBench, a suite of ML tasks for
benchmarking AI research agents. Agents can perform actions like
reading/writing files, executing code, and inspecting outputs. With these
actions, agents could run experiments, analyze the results, and modify the code
of entire machine learning pipelines, such as data processing, architecture,
training processes, etc. The benchmark then automatically evaluates the agent's
performance objectively over various metrics related to performance and
efficiency. We also design an LLM-based research agent to automatically perform
experimentation loops in such an environment. Empirically, we find that a
GPT-4-based research agent can feasibly build compelling ML models over many
tasks in MLAgentBench, displaying highly interpretable plans and actions.
However, the success rates vary considerably; they span from almost 90\% on
well-established older datasets to as low as 10\% on recent Kaggle Challenges
-- unavailable during the LLM model's pretraining -- and even 0\% on newer
research challenges like BabyLM. Finally, we identify several key challenges
for LLM-based research agents such as long-term planning and hallucination. Our
code is released at https://github.com/snap-stanford/MLAgentBench. | [
"Qian Huang",
"Jian Vora",
"Percy Liang",
"Jure Leskovec"
] | 2023-10-05 04:06:12 | http://arxiv.org/abs/2310.03302v1 | http://arxiv.org/pdf/2310.03302v1 | 2310.03302v1 |
Learning Energy Decompositions for Partial Inference of GFlowNets | This paper studies generative flow networks (GFlowNets) to sample objects
from the Boltzmann energy distribution via a sequence of actions. In
particular, we focus on improving GFlowNet with partial inference: training
flow functions with the evaluation of the intermediate states or transitions.
To this end, the recently developed forward-looking GFlowNet reparameterizes
the flow functions based on evaluating the energy of intermediate states.
However, such an evaluation of intermediate energies may (i) be too expensive
or impossible to evaluate and (ii) even provide misleading training signals
under large energy fluctuations along the sequence of actions. To resolve this
issue, we propose learning energy decompositions for GFlowNets (LED-GFN). Our
main idea is to (i) decompose the energy of an object into learnable potential
functions defined on state transitions and (ii) reparameterize the flow
functions using the potential functions. In particular, to produce informative
local credits, we propose to regularize the potential to change smoothly over
the sequence of actions. It is also noteworthy that training GFlowNet with our
learned potential can preserve the optimal policy. We empirically verify the
superiority of LED-GFN in five problems including the generation of
unstructured and maximum independent sets, molecular graphs, and RNA sequences. | [
"Hyosoon Jang",
"Minsu Kim",
"Sungsoo Ahn"
] | 2023-10-05 04:02:36 | http://arxiv.org/abs/2310.03301v1 | http://arxiv.org/pdf/2310.03301v1 | 2310.03301v1 |
A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling | Multi-fidelity (MF) methods are gaining popularity for enhancing surrogate
modeling and design optimization by incorporating data from various
low-fidelity (LF) models. While most existing MF methods assume a fixed
dataset, adaptive sampling methods that dynamically allocate resources among
fidelity models can achieve higher efficiency in the exploring and exploiting
the design space. However, most existing MF methods rely on the hierarchical
assumption of fidelity levels or fail to capture the intercorrelation between
multiple fidelity levels and utilize it to quantify the value of the future
samples and navigate the adaptive sampling. To address this hurdle, we propose
a framework hinged on a latent embedding for different fidelity models and the
associated pre-posterior analysis to explicitly utilize their correlation for
adaptive sampling. In this framework, each infill sampling iteration includes
two steps: We first identify the location of interest with the greatest
potential improvement using the high-fidelity (HF) model, then we search for
the next sample across all fidelity levels that maximize the improvement per
unit cost at the location identified in the first step. This is made possible
by a single Latent Variable Gaussian Process (LVGP) model that maps different
fidelity models into an interpretable latent space to capture their
correlations without assuming hierarchical fidelity levels. The LVGP enables us
to assess how LF sampling candidates will affect HF response with pre-posterior
analysis and determine the next sample with the best benefit-to-cost ratio.
Through test cases, we demonstrate that the proposed method outperforms the
benchmark methods in both MF global fitting (GF) and Bayesian Optimization (BO)
problems in convergence rate and robustness. Moreover, the method offers the
flexibility to switch between GF and BO by simply changing the acquisition
function. | [
"Yi-Ping Chen",
"Liwei Wang",
"Yigitcan Comlek",
"Wei Chen"
] | 2023-10-05 03:56:09 | http://arxiv.org/abs/2310.03298v1 | http://arxiv.org/pdf/2310.03298v1 | 2310.03298v1 |
LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers | Increasing the context length of large language models (LLMs) unlocks
fundamentally new capabilities, but also significantly increases the memory
footprints of training. Previous model-parallel systems such as Megatron-LM
partition and compute different attention heads in parallel, resulting in large
communication volumes, so they cannot scale beyond the number of attention
heads, thereby hindering its adoption. In this paper, we introduce a new
approach, LightSeq, for long-context LLMs training. LightSeq has many notable
advantages. First, LightSeq partitions over the sequence dimension, hence is
agnostic to model architectures and readily applicable for models with varying
numbers of attention heads, such as Multi-Head, Multi-Query and Grouped-Query
attention. Second, LightSeq not only requires up to 4.7x less communication
than Megatron-LM on popular LLMs but also overlaps the communication with
computation. To further reduce the training time, LightSeq features a novel
gradient checkpointing scheme to bypass an forward computation for
memory-efficient attention. We evaluate LightSeq on Llama-7B and its variants
with sequence lengths from 32K to 512K. Through comprehensive experiments on
single and cross-node training, we show that LightSeq achieves up to 1.24-2.01x
end-to-end speedup, and a 2-8x longer sequence length on models with fewer
heads, compared to Megatron-LM. Codes will be available at
https://github.com/RulinShao/LightSeq. | [
"Dacheng Li",
"Rulin Shao",
"Anze Xie",
"Eric P. Xing",
"Joseph E. Gonzalez",
"Ion Stoica",
"Xuezhe Ma",
"Hao Zhang"
] | 2023-10-05 03:47:57 | http://arxiv.org/abs/2310.03294v1 | http://arxiv.org/pdf/2310.03294v1 | 2310.03294v1 |
PoseAction: Action Recognition for Patients in the Ward using Deep Learning Approaches | Real-time intelligent detection and prediction of subjects' behavior
particularly their movements or actions is critical in the ward. This approach
offers the advantage of reducing in-hospital care costs and improving the
efficiency of healthcare workers, which is especially true for scenarios at
night or during peak admission periods. Therefore, in this work, we propose
using computer vision (CV) and deep learning (DL) methods for detecting
subjects and recognizing their actions. We utilize OpenPose as an accurate
subject detector for recognizing the positions of human subjects in the video
stream. Additionally, we employ AlphAction's Asynchronous Interaction
Aggregation (AIA) network to predict the actions of detected subjects. This
integrated model, referred to as PoseAction, is proposed. At the same time, the
proposed model is further trained to predict 12 common actions in ward areas,
such as staggering, chest pain, and falling down, using medical-related video
clips from the NTU RGB+D and NTU RGB+D 120 datasets. The results demonstrate
that PoseAction achieves the highest classification mAP of 98.72% ([email protected]).
Additionally, this study develops an online real-time mode for action
recognition, which strongly supports the clinical translation of PoseAction.
Furthermore, using OpenPose's function for recognizing face key points, we also
implement face blurring, which is a practical solution to address the privacy
protection concerns of patients and healthcare workers. Nevertheless, the
training data for PoseAction is currently limited, particularly in terms of
label diversity. Consequently, the subsequent step involves utilizing a more
diverse dataset (including general actions) to train the model's parameters for
improved generalization. | [
"Zherui Li",
"Raye Chen-Hua Yeow"
] | 2023-10-05 03:33:35 | http://arxiv.org/abs/2310.03288v1 | http://arxiv.org/pdf/2310.03288v1 | 2310.03288v1 |
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations | Toward robust malware detection, we explore the attack surface of existing
malware detection systems. We conduct root-cause analyses of the practical
binary-level black-box adversarial malware examples. Additionally, we uncover
the sensitivity of volatile features within the detection engines and exhibit
their exploitability. Highlighting volatile information channels within the
software, we introduce three software pre-processing steps to eliminate the
attack surface, namely, padding removal, software stripping, and inter-section
information resetting. Further, to counter the emerging section injection
attacks, we propose a graph-based section-dependent information extraction
scheme for software representation. The proposed scheme leverages aggregated
information within various sections in the software to enable robust malware
detection and mitigate adversarial settings. Our experimental results show that
traditional malware detection models are ineffective against adversarial
threats. However, the attack surface can be largely reduced by eliminating the
volatile information. Therefore, we propose simple-yet-effective methods to
mitigate the impacts of binary manipulation attacks. Overall, our graph-based
malware detection scheme can accurately detect malware with an area under the
curve score of 88.32\% and a score of 88.19% under a combination of binary
manipulation attacks, exhibiting the efficiency of our proposed scheme. | [
"Ahmed Abusnaina",
"Yizhen Wang",
"Sunpreet Arora",
"Ke Wang",
"Mihai Christodorescu",
"David Mohaisen"
] | 2023-10-05 03:28:02 | http://arxiv.org/abs/2310.03285v1 | http://arxiv.org/pdf/2310.03285v1 | 2310.03285v1 |
A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions | The 5' UTR, a regulatory region at the beginning of an mRNA molecule, plays a
crucial role in regulating the translation process and impacts the protein
expression level. Language models have showcased their effectiveness in
decoding the functions of protein and genome sequences. Here, we introduced a
language model for 5' UTR, which we refer to as the UTR-LM. The UTR-LM is
pre-trained on endogenous 5' UTRs from multiple species and is further
augmented with supervised information including secondary structure and minimum
free energy. We fine-tuned the UTR-LM in a variety of downstream tasks. The
model outperformed the best-known benchmark by up to 42% for predicting the
Mean Ribosome Loading, and by up to 60% for predicting the Translation
Efficiency and the mRNA Expression Level. The model also applies to identifying
unannotated Internal Ribosome Entry Sites within the untranslated region and
improves the AUPR from 0.37 to 0.52 compared to the best baseline. Further, we
designed a library of 211 novel 5' UTRs with high predicted values of
translation efficiency and evaluated them via a wet-lab assay. Experiment
results confirmed that our top designs achieved a 32.5% increase in protein
production level relative to well-established 5' UTR optimized for
therapeutics. | [
"Yanyi Chu",
"Dan Yu",
"Yupeng Li",
"Kaixuan Huang",
"Yue Shen",
"Le Cong",
"Jason Zhang",
"Mengdi Wang"
] | 2023-10-05 03:15:01 | http://arxiv.org/abs/2310.03281v2 | http://arxiv.org/pdf/2310.03281v2 | 2310.03281v2 |
Mitigating Pilot Contamination and Enabling IoT Scalability in Massive MIMO Systems | Massive MIMO is expected to play an important role in the development of 5G
networks. This paper addresses the issue of pilot contamination and scalability
in massive MIMO systems. The current practice of reusing orthogonal pilot
sequences in adjacent cells leads to difficulty in differentiating incoming
inter- and intra-cell pilot sequences. One possible solution is to increase the
number of orthogonal pilot sequences, which results in dedicating more space of
coherence block to pilot transmission than data transmission. This, in turn,
also hinders the scalability of massive MIMO systems, particularly in
accommodating a large number of IoT devices within a cell. To overcome these
challenges, this paper devises an innovative pilot allocation scheme based on
the data transfer patterns of IoT devices. The scheme assigns orthogonal pilot
sequences to clusters of devices instead of individual devices, allowing
multiple devices to utilize the same pilot for periodically transmitting data.
Moreover, we formulate the pilot assignment problem as a graph coloring problem
and use the max k-cut graph partitioning approach to overcome the pilot
contamination in a multicell massive MIMO system. The proposed scheme
significantly improves the spectral efficiency and enables the scalability of
massive MIMO systems; for instance, by using ten orthogonal pilot sequences, we
are able to accommodate 200 devices with only a 12.5% omission rate. | [
"Muhammad Kamran Saeed",
"Ahmed E. Kamal",
"Ashfaq Khokhar"
] | 2023-10-05 03:06:09 | http://arxiv.org/abs/2310.03278v1 | http://arxiv.org/pdf/2310.03278v1 | 2310.03278v1 |
Fragment-based Pretraining and Finetuning on Molecular Graphs | Property prediction on molecular graphs is an important application of Graph
Neural Networks (GNNs). Recently, unlabeled molecular data has become abundant,
which facilitates the rapid development of self-supervised learning for GNNs in
the chemical domain. In this work, we propose pretraining GNNs at the fragment
level, which serves as a promising middle ground to overcome the limitations of
node-level and graph-level pretraining. Borrowing techniques from recent work
on principle subgraph mining, we obtain a compact vocabulary of prevalent
fragments that span a large pretraining dataset. From the extracted vocabulary,
we introduce several fragment-based contrastive and predictive pretraining
tasks. The contrastive learning task jointly pretrains two different GNNs: one
based on molecular graphs and one based on fragment graphs, which represents
high-order connectivity within molecules. By enforcing the consistency between
the fragment embedding and the aggregated embedding of the corresponding atoms
from the molecular graphs, we ensure that both embeddings capture structural
information at multiple resolutions. The structural information of the fragment
graphs is further exploited to extract auxiliary labels for the graph-level
predictive pretraining. We employ both the pretrained molecular-based and
fragment-based GNNs for downstream prediction, thus utilizing the fragment
information during finetuning. Our models advance the performances on 5 out of
8 common molecular benchmarks and improve the performances on long-range
biological benchmarks by at least 11.5%. | [
"Kha-Dinh Luong",
"Ambuj Singh"
] | 2023-10-05 03:01:09 | http://arxiv.org/abs/2310.03274v1 | http://arxiv.org/pdf/2310.03274v1 | 2310.03274v1 |
Ablation Study to Clarify the Mechanism of Object Segmentation in Multi-Object Representation Learning | Multi-object representation learning aims to represent complex real-world
visual input using the composition of multiple objects. Representation learning
methods have often used unsupervised learning to segment an input image into
individual objects and encode these objects into each latent vector. However,
it is not clear how previous methods have achieved the appropriate segmentation
of individual objects. Additionally, most of the previous methods regularize
the latent vectors using a Variational Autoencoder (VAE). Therefore, it is not
clear whether VAE regularization contributes to appropriate object
segmentation. To elucidate the mechanism of object segmentation in multi-object
representation learning, we conducted an ablation study on MONet, which is a
typical method. MONet represents multiple objects using pairs that consist of
an attention mask and the latent vector corresponding to the attention mask.
Each latent vector is encoded from the input image and attention mask. Then,
the component image and attention mask are decoded from each latent vector. The
loss function of MONet consists of 1) the sum of reconstruction losses between
the input image and decoded component image, 2) the VAE regularization loss of
the latent vector, and 3) the reconstruction loss of the attention mask to
explicitly encode shape information. We conducted an ablation study on these
three loss functions to investigate the effect on segmentation performance. Our
results showed that the VAE regularization loss did not affect segmentation
performance and the others losses did affect it. Based on this result, we
hypothesize that it is important to maximize the attention mask of the image
region best represented by a single latent vector corresponding to the
attention mask. We confirmed this hypothesis by evaluating a new loss function
with the same mechanism as the hypothesis. | [
"Takayuki Komatsu",
"Yoshiyuki Ohmura",
"Yasuo Kuniyoshi"
] | 2023-10-05 02:59:48 | http://arxiv.org/abs/2310.03273v1 | http://arxiv.org/pdf/2310.03273v1 | 2310.03273v1 |
Network Alignment with Transferable Graph Autoencoders | Network alignment is the task of establishing one-to-one correspondences
between the nodes of different graphs and finds a plethora of applications in
high-impact domains. However, this task is known to be NP-hard in its general
form, and existing algorithms do not scale up as the size of the graphs
increases. To tackle both challenges we propose a novel generalized graph
autoencoder architecture, designed to extract powerful and robust node
embeddings, that are tailored to the alignment task. We prove that the
generated embeddings are associated with the eigenvalues and eigenvectors of
the graphs and can achieve more accurate alignment compared to classical
spectral methods. Our proposed framework also leverages transfer learning and
data augmentation to achieve efficient network alignment at a very large scale
without retraining. Extensive experiments on both network and sub-network
alignment with real-world graphs provide corroborating evidence supporting the
effectiveness and scalability of the proposed approach. | [
"Jiashu He",
"Charilaos I. Kanatsoulis",
"Alejandro Ribeiro"
] | 2023-10-05 02:58:29 | http://arxiv.org/abs/2310.03272v1 | http://arxiv.org/pdf/2310.03272v1 | 2310.03272v1 |
Investigating Alternative Feature Extraction Pipelines For Clinical Note Phenotyping | A common practice in the medical industry is the use of clinical notes, which
consist of detailed patient observations. However, electronic health record
systems frequently do not contain these observations in a structured format,
rendering patient information challenging to assess and evaluate automatically.
Using computational systems for the extraction of medical attributes offers
many applications, including longitudinal analysis of patients, risk
assessment, and hospital evaluation. Recent work has constructed successful
methods for phenotyping: extracting medical attributes from clinical notes.
BERT-based models can be used to transform clinical notes into a series of
representations, which are then condensed into a single document representation
based on their CLS embeddings and passed into an LSTM (Mulyar et al., 2020).
Though this pipeline yields a considerable performance improvement over
previous results, it requires extensive convergence time. This method also does
not allow for predicting attributes not yet identified in clinical notes.
Considering the wide variety of medical attributes that may be present in a
clinical note, we propose an alternative pipeline utilizing ScispaCy (Neumann
et al., 2019) for the extraction of common diseases. We then train various
supervised learning models to associate the presence of these conditions with
patient attributes. Finally, we replicate a ClinicalBERT (Alsentzer et al.,
2019) and LSTM-based approach for purposes of comparison. We find that
alternative methods moderately underperform the replicated LSTM approach. Yet,
considering a complex tradeoff between accuracy and runtime, in addition to the
fact that the alternative approach also allows for the detection of medical
conditions that are not already present in a clinical note, its usage may be
considered as a supplement to established methods. | [
"Neil Daniel"
] | 2023-10-05 02:51:51 | http://arxiv.org/abs/2310.03772v1 | http://arxiv.org/pdf/2310.03772v1 | 2310.03772v1 |
UniPredict: Large Language Models are Universal Tabular Predictors | Tabular data prediction is a fundamental machine learning task for many
applications. Existing methods predominantly employ discriminative modeling and
operate under the assumption of a fixed target column, necessitating
re-training for every new predictive task. Inspired by the generative power of
large language models (LLMs), this paper exploits the idea of building
universal tabular data predictors based on generative modeling, namely
UniPredict. Here, we show that scaling up an LLM to extensive tabular datasets
with the capability of comprehending diverse tabular inputs and predicting for
target variables following the input instructions. Specifically, we train a
single LLM on an aggregation of 169 tabular datasets with diverse targets and
compare its performance against baselines that are trained on each dataset
separately. We observe this versatile UniPredict model demonstrates an
advantage over other models, ranging from 5.4% to 13.4%, when compared with the
best tree-boosting baseline and the best neural network baseline, respectively.
We further test UniPredict in few-shot learning settings on another 62 tabular
datasets. Our method achieves strong performance in quickly adapting to new
tasks, where our method outperforms XGBoost over 100% on the low-resource setup
and shows a significant margin over all baselines. We envision that UniPredict
sheds light on developing a universal tabular data prediction system that
learns from data at scale and serves a wide range of prediction tasks. | [
"Ruiyu Wang",
"Zifeng Wang",
"Jimeng Sun"
] | 2023-10-05 02:37:09 | http://arxiv.org/abs/2310.03266v1 | http://arxiv.org/pdf/2310.03266v1 | 2310.03266v1 |
Detecting Electricity Service Equity Issues with Transfer Counterfactual Learning on Large-Scale Outage Datasets | Energy justice is a growing area of interest in interdisciplinary energy
research. However, identifying systematic biases in the energy sector remains
challenging due to confounding variables, intricate heterogeneity in treatment
effects, and limited data availability. To address these challenges, we
introduce a novel approach for counterfactual causal analysis centered on
energy justice. We use subgroup analysis to manage diverse factors and leverage
the idea of transfer learning to mitigate data scarcity in each subgroup. In
our numerical analysis, we apply our method to a large-scale customer-level
power outage data set and investigate the counterfactual effect of demographic
factors, such as income and age of the population, on power outage durations.
Our results indicate that low-income and elderly-populated areas consistently
experience longer power outages, regardless of weather conditions. This points
to existing biases in the power system and highlights the need for focused
improvements in areas with economic challenges. | [
"Song Wei",
"Xiangrui Kong",
"Sarah A Huestis-Mitchell",
"Shixiang Zhu",
"Yao Xie",
"Alinson Santos Xavier",
"Feng Qiu"
] | 2023-10-05 02:22:16 | http://arxiv.org/abs/2310.03258v1 | http://arxiv.org/pdf/2310.03258v1 | 2310.03258v1 |
Molecule Design by Latent Prompt Transformer | This paper proposes a latent prompt Transformer model for solving challenging
optimization problems such as molecule design, where the goal is to find
molecules with optimal values of a target chemical or biological property that
can be computed by an existing software. Our proposed model consists of three
components. (1) A latent vector whose prior distribution is modeled by a Unet
transformation of a Gaussian white noise vector. (2) A molecule generation
model that generates the string-based representation of molecule conditional on
the latent vector in (1). We adopt the causal Transformer model that takes the
latent vector in (1) as prompt. (3) A property prediction model that predicts
the value of the target property of a molecule based on a non-linear regression
on the latent vector in (1). We call the proposed model the latent prompt
Transformer model. After initial training of the model on existing molecules
and their property values, we then gradually shift the model distribution
towards the region that supports desired values of the target property for the
purpose of molecule design. Our experiments show that our proposed model
achieves state of the art performances on several benchmark molecule design
tasks. | [
"Deqian Kong",
"Yuhao Huang",
"Jianwen Xie",
"Ying Nian Wu"
] | 2023-10-05 02:09:51 | http://arxiv.org/abs/2310.03253v1 | http://arxiv.org/pdf/2310.03253v1 | 2310.03253v1 |
Sparse Deep Learning for Time Series Data: Theory and Applications | Sparse deep learning has become a popular technique for improving the
performance of deep neural networks in areas such as uncertainty
quantification, variable selection, and large-scale network compression.
However, most existing research has focused on problems where the observations
are independent and identically distributed (i.i.d.), and there has been little
work on the problems where the observations are dependent, such as time series
data and sequential data in natural language processing. This paper aims to
address this gap by studying the theory for sparse deep learning with dependent
data. We show that sparse recurrent neural networks (RNNs) can be consistently
estimated, and their predictions are asymptotically normally distributed under
appropriate assumptions, enabling the prediction uncertainty to be correctly
quantified. Our numerical results show that sparse deep learning outperforms
state-of-the-art methods, such as conformal predictions, in prediction
uncertainty quantification for time series data. Furthermore, our results
indicate that the proposed method can consistently identify the autoregressive
order for time series data and outperform existing methods in large-scale model
compression. Our proposed method has important practical implications in fields
such as finance, healthcare, and energy, where both accurate point estimates
and prediction uncertainty quantification are of concern. | [
"Mingxuan Zhang",
"Yan Sun",
"Faming Liang"
] | 2023-10-05 01:26:13 | http://arxiv.org/abs/2310.03243v1 | http://arxiv.org/pdf/2310.03243v1 | 2310.03243v1 |
Relational Convolutional Networks: A framework for learning representations of hierarchical relations | A maturing area of research in deep learning is the development of
architectures that can learn explicit representations of relational features.
In this paper, we focus on the problem of learning representations of
hierarchical relations, proposing an architectural framework we call
"relational convolutional networks". Given a sequence of objects, a
"multi-dimensional inner product relation" module produces a relation tensor
describing all pairwise relations. A "relational convolution" layer then
transforms the relation tensor into a sequence of new objects, each describing
the relations within some group of objects at the previous layer. Graphlet
filters, analogous to filters in convolutional neural networks, represent a
template of relations against which the relation tensor is compared at each
grouping. Repeating this yields representations of higher-order, hierarchical
relations. We present the motivation and details of the architecture, together
with a set of experiments to demonstrate how relational convolutional networks
can provide an effective framework for modeling relational tasks that have
hierarchical structure. | [
"Awni Altabaa",
"John Lafferty"
] | 2023-10-05 01:22:50 | http://arxiv.org/abs/2310.03240v1 | http://arxiv.org/pdf/2310.03240v1 | 2310.03240v1 |
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization | This paper investigates new families of compositional optimization problems,
called $\underline{\bf n}$on-$\underline{\bf s}$mooth $\underline{\bf
w}$eakly-$\underline{\bf c}$onvex $\underline{\bf f}$inite-sum $\underline{\bf
c}$oupled $\underline{\bf c}$ompositional $\underline{\bf o}$ptimization (NSWC
FCCO). There has been a growing interest in FCCO due to its wide-ranging
applications in machine learning and AI, as well as its ability to address the
shortcomings of stochastic algorithms based on empirical risk minimization.
However, current research on FCCO presumes that both the inner and outer
functions are smooth, limiting their potential to tackle a more diverse set of
problems. Our research expands on this area by examining non-smooth
weakly-convex FCCO, where the outer function is weakly convex and
non-decreasing, and the inner function is weakly-convex. We analyze a
single-loop algorithm and establish its complexity for finding an
$\epsilon$-stationary point of the Moreau envelop of the objective function.
Additionally, we also extend the algorithm to solving novel non-smooth
weakly-convex tri-level finite-sum coupled compositional optimization problems,
which feature a nested arrangement of three functions. Lastly, we explore the
applications of our algorithms in deep learning for two-way partial AUC
maximization and multi-instance two-way partial AUC maximization, using
empirical studies to showcase the effectiveness of the proposed algorithms. | [
"Quanqi Hu",
"Dixian Zhu",
"Tianbao Yang"
] | 2023-10-05 01:01:09 | http://arxiv.org/abs/2310.03234v1 | http://arxiv.org/pdf/2310.03234v1 | 2310.03234v1 |
Observatory: Characterizing Embeddings of Relational Tables | Language models and specialized table embedding models have recently
demonstrated strong performance on many tasks over tabular data. Researchers
and practitioners are keen to leverage these models in many new application
contexts; but limited understanding of the strengths and weaknesses of these
models, and the table representations they generate, makes the process of
finding a suitable model for a given task reliant on trial and error. There is
an urgent need to gain a comprehensive understanding of these models to
minimize inefficiency and failures in downstream usage.
To address this need, we propose Observatory, a formal framework to
systematically analyze embedding representations of relational tables.
Motivated both by invariants of the relational data model and by statistical
considerations regarding data distributions, we define eight primitive
properties, and corresponding measures to quantitatively characterize table
embeddings for these properties. Based on these properties, we define an
extensible framework to evaluate language and table embedding models. We
collect and synthesize a suite of datasets and use Observatory to analyze seven
such models. Our analysis provides insights into the strengths and weaknesses
of learned representations over tables. We find, for example, that some models
are sensitive to table structure such as column order, that functional
dependencies are rarely reflected in embeddings, and that specialized table
embedding models have relatively lower sample fidelity. Such insights help
researchers and practitioners better anticipate model behaviors and select
appropriate models for their downstream tasks, while guiding researchers in the
development of new models. | [
"Tianji Cong",
"Madelon Hulsebos",
"Zhenjie Sun",
"Paul Groth",
"H. V. Jagadish"
] | 2023-10-05 00:58:45 | http://arxiv.org/abs/2310.07736v1 | http://arxiv.org/pdf/2310.07736v1 | 2310.07736v1 |
History Matching for Geological Carbon Storage using Data-Space Inversion with Spatio-Temporal Data Parameterization | History matching based on monitoring data will enable uncertainty reduction,
and thus improved aquifer management, in industrial-scale carbon storage
operations. In traditional model-based data assimilation, geomodel parameters
are modified to force agreement between flow simulation results and
observations. In data-space inversion (DSI), history-matched quantities of
interest, e.g., posterior pressure and saturation fields conditioned to
observations, are inferred directly, without constructing posterior geomodels.
This is accomplished efficiently using a set of O(1000) prior simulation
results, data parameterization, and posterior sampling within a Bayesian
setting. In this study, we develop and implement (in DSI) a deep-learning-based
parameterization to represent spatio-temporal pressure and CO2 saturation
fields at a set of time steps. The new parameterization uses an adversarial
autoencoder (AAE) for dimension reduction and a convolutional long short-term
memory (convLSTM) network to represent the spatial distribution and temporal
evolution of the pressure and saturation fields. This parameterization is used
with an ensemble smoother with multiple data assimilation (ESMDA) in the DSI
framework to enable posterior predictions. A realistic 3D system characterized
by prior geological realizations drawn from a range of geological scenarios is
considered. A local grid refinement procedure is introduced to estimate the
error covariance term that appears in the history matching formulation.
Extensive history matching results are presented for various quantities, for
multiple synthetic true models. Substantial uncertainty reduction in posterior
pressure and saturation fields is achieved in all cases. The framework is
applied to efficiently provide posterior predictions for a range of error
covariance specifications. Such an assessment would be expensive using a
model-based approach. | [
"Su Jiang",
"Louis J. Durlofsky"
] | 2023-10-05 00:50:06 | http://arxiv.org/abs/2310.03228v1 | http://arxiv.org/pdf/2310.03228v1 | 2310.03228v1 |
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms | Safe exploration is essential for the practical use of reinforcement learning
(RL) in many real-world scenarios. In this paper, we present a generalized safe
exploration (GSE) problem as a unified formulation of common safe exploration
problems. We then propose a solution of the GSE problem in the form of a
meta-algorithm for safe exploration, MASE, which combines an unconstrained RL
algorithm with an uncertainty quantifier to guarantee safety in the current
episode while properly penalizing unsafe explorations before actual safety
violation to discourage them in future episodes. The advantage of MASE is that
we can optimize a policy while guaranteeing with a high probability that no
safety constraint will be violated under proper assumptions. Specifically, we
present two variants of MASE with different constructions of the uncertainty
quantifier: one based on generalized linear models with theoretical guarantees
of safety and near-optimality, and another that combines a Gaussian process to
ensure safety with a deep RL algorithm to maximize the reward. Finally, we
demonstrate that our proposed algorithm achieves better performance than
state-of-the-art algorithms on grid-world and Safety Gym benchmarks without
violating any safety constraints, even during training. | [
"Akifumi Wachi",
"Wataru Hashimoto",
"Xun Shen",
"Kazumune Hashimoto"
] | 2023-10-05 00:47:09 | http://arxiv.org/abs/2310.03225v1 | http://arxiv.org/pdf/2310.03225v1 | 2310.03225v1 |
TacoGFN: Target Conditioned GFlowNet for Structure-Based Drug Design | We seek to automate the generation of drug-like compounds conditioned to
specific protein pocket targets. Most current methods approximate the
protein-molecule distribution of a finite dataset and, therefore struggle to
generate molecules with significant binding improvement over the training
dataset. We instead frame the pocket-conditioned molecular generation task as
an RL problem and develop TacoGFN, a target conditional Generative Flow Network
model. Our method is explicitly encouraged to generate molecules with desired
properties as opposed to fitting on a pre-existing data distribution. To this
end, we develop transformer-based docking score prediction to speed up docking
score computation and propose TacoGFN to explore molecule space efficiently.
Furthermore, we incorporate several rounds of active learning where generated
samples are queried using a docking oracle to improve the docking score
prediction. This approach allows us to accurately explore as much of the
molecule landscape as we can afford computationally. Empirically, molecules
generated using TacoGFN and its variants significantly outperform all baseline
methods across every property (Docking score, QED, SA, Lipinski), while being
orders of magnitude faster. | [
"Tony Shen",
"Mohit Pandey",
"Martin Ester"
] | 2023-10-05 00:45:04 | http://arxiv.org/abs/2310.03223v1 | http://arxiv.org/pdf/2310.03223v1 | 2310.03223v1 |
Know2BIO: A Comprehensive Dual-View Benchmark for Evolving Biomedical Knowledge Graphs | Knowledge graphs (KGs) have emerged as a powerful framework for representing
and integrating complex biomedical information. However, assembling KGs from
diverse sources remains a significant challenge in several aspects, including
entity alignment, scalability, and the need for continuous updates to keep pace
with scientific advancements. Moreover, the representative power of KGs is
often limited by the scarcity of multi-modal data integration. To overcome
these challenges, we propose Know2BIO, a general-purpose heterogeneous KG
benchmark for the biomedical domain. Know2BIO integrates data from 30 diverse
sources, capturing intricate relationships across 11 biomedical categories. It
currently consists of ~219,000 nodes and ~6,200,000 edges. Know2BIO is capable
of user-directed automated updating to reflect the latest knowledge in
biomedical science. Furthermore, Know2BIO is accompanied by multi-modal data:
node features including text descriptions, protein and compound sequences and
structures, enabling the utilization of emerging natural language processing
methods and multi-modal data integration strategies. We evaluate KG
representation models on Know2BIO, demonstrating its effectiveness as a
benchmark for KG representation learning in the biomedical field. Data and
source code of Know2BIO are available at
https://github.com/Yijia-Xiao/Know2BIO/. | [
"Yijia Xiao",
"Dylan Steinecke",
"Alexander Russell Pelletier",
"Yushi Bai",
"Peipei Ping",
"Wei Wang"
] | 2023-10-05 00:34:56 | http://arxiv.org/abs/2310.03221v1 | http://arxiv.org/pdf/2310.03221v1 | 2310.03221v1 |
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC | Latent space Energy-Based Models (EBMs), also known as energy-based priors,
have drawn growing interests in the field of generative modeling due to its
flexibility in the formulation and strong modeling power of the latent space.
However, the common practice of learning latent space EBMs with non-convergent
short-run MCMC for prior and posterior sampling is hindering the model from
further progress; the degenerate MCMC sampling quality in practice often leads
to degraded generation quality and instability in training, especially with
highly multi-modal and/or high-dimensional target distributions. To remedy this
sampling issue, in this paper we introduce a simple but effective
diffusion-based amortization method for long-run MCMC sampling and develop a
novel learning algorithm for the latent space EBM based on it. We provide
theoretical evidence that the learned amortization of MCMC is a valid long-run
MCMC sampler. Experiments on several image modeling benchmark datasets
demonstrate the superior performance of our method compared with strong
counterparts | [
"Peiyu Yu",
"Yaxuan Zhu",
"Sirui Xie",
"Xiaojian Ma",
"Ruiqi Gao",
"Song-Chun Zhu",
"Ying Nian Wu"
] | 2023-10-05 00:23:34 | http://arxiv.org/abs/2310.03218v1 | http://arxiv.org/pdf/2310.03218v1 | 2310.03218v1 |
Formal and Practical Elements for the Certification of Machine Learning Systems | Over the past decade, machine learning has demonstrated impressive results,
often surpassing human capabilities in sensing tasks relevant to autonomous
flight. Unlike traditional aerospace software, the parameters of machine
learning models are not hand-coded nor derived from physics but learned from
data. They are automatically adjusted during a training phase, and their values
do not usually correspond to physical requirements. As a result, requirements
cannot be directly traced to lines of code, hindering the current bottom-up
aerospace certification paradigm. This paper attempts to address this gap by 1)
demystifying the inner workings and processes to build machine learning models,
2) formally establishing theoretical guarantees given by those processes, and
3) complementing these formal elements with practical considerations to develop
a complete certification argument for safety-critical machine learning systems.
Based on a scalable statistical verifier, our proposed framework is
model-agnostic and tool-independent, making it adaptable to many use cases in
the industry. We demonstrate results on a widespread application in autonomous
flight: vision-based landing. | [
"Jean-Guillaume Durand",
"Arthur Dubois",
"Robert J. Moss"
] | 2023-10-05 00:20:59 | http://arxiv.org/abs/2310.03217v1 | http://arxiv.org/pdf/2310.03217v1 | 2310.03217v1 |
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer | Data-driven modeling can suffer from a constant demand for data, leading to
reduced accuracy and impractical for engineering applications due to the high
cost and scarcity of information. To address this challenge, we propose a
progressive reduced order modeling framework that minimizes data cravings and
enhances data-driven modeling's practicality. Our approach selectively
transfers knowledge from previously trained models through gates, similar to
how humans selectively use valuable knowledge while ignoring unuseful
information. By filtering relevant information from previous models, we can
create a surrogate model with minimal turnaround time and a smaller training
set that can still achieve high accuracy. We have tested our framework in
several cases, including transport in porous media, gravity-driven flow, and
finite deformation in hyperelastic materials. Our results illustrate that
retaining information from previous models and utilizing a valuable portion of
that knowledge can significantly improve the accuracy of the current model. We
have demonstrated the importance of progressive knowledge transfer and its
impact on model accuracy with reduced training samples. For instance, our
framework with four parent models outperforms the no-parent counterpart trained
on data nine times larger. Our research unlocks data-driven modeling's
potential for practical engineering applications by mitigating the data
scarcity issue. Our proposed framework is a significant step toward more
efficient and cost-effective data-driven modeling, fostering advancements
across various fields. | [
"Teeratorn Kadeethum",
"Daniel O'Malley",
"Youngsoo Choi",
"Hari S. Viswanathan",
"Hongkyu Yoon"
] | 2023-10-04 23:50:14 | http://arxiv.org/abs/2310.03770v1 | http://arxiv.org/pdf/2310.03770v1 | 2310.03770v1 |
PDR-CapsNet: an Energy-Efficient Parallel Approach to Dynamic Routing in Capsule Networks | Convolutional Neural Networks (CNNs) have produced state-of-the-art results
for image classification tasks. However, they are limited in their ability to
handle rotational and viewpoint variations due to information loss in
max-pooling layers. Capsule Networks (CapsNets) employ a
computationally-expensive iterative process referred to as dynamic routing to
address these issues. CapsNets, however, often fall short on complex datasets
and require more computational resources than CNNs. To overcome these
challenges, we introduce the Parallel Dynamic Routing CapsNet (PDR-CapsNet), a
deeper and more energy-efficient alternative to CapsNet that offers superior
performance, less energy consumption, and lower overfitting rates. By
leveraging a parallelization strategy, PDR-CapsNet mitigates the computational
complexity of CapsNet and increases throughput, efficiently using hardware
resources. As a result, we achieve 83.55\% accuracy while requiring 87.26\%
fewer parameters, 32.27\% and 47.40\% fewer MACs, and Flops, achieving 3x
faster inference and 7.29J less energy consumption on a 2080Ti GPU with 11GB
VRAM compared to CapsNet and for the CIFAR-10 dataset. | [
"Samaneh Javadinia",
"Amirali Baniasadi"
] | 2023-10-04 23:38:09 | http://arxiv.org/abs/2310.03212v1 | http://arxiv.org/pdf/2310.03212v1 | 2310.03212v1 |
Regret Analysis of Distributed Online Control for LTI Systems with Adversarial Disturbances | This paper addresses the distributed online control problem over a network of
linear time-invariant (LTI) systems (with possibly unknown dynamics) in the
presence of adversarial perturbations. There exists a global network cost that
is characterized by a time-varying convex function, which evolves in an
adversarial manner and is sequentially and partially observed by local agents.
The goal of each agent is to generate a control sequence that can compete with
the best centralized control policy in hindsight, which has access to the
global cost. This problem is formulated as a regret minimization. For the case
of known dynamics, we propose a fully distributed disturbance feedback
controller that guarantees a regret bound of $O(\sqrt{T}\log T)$, where $T$ is
the time horizon. For the unknown dynamics case, we design a distributed
explore-then-commit approach, where in the exploration phase all agents jointly
learn the system dynamics, and in the learning phase our proposed control
algorithm is applied using each agent system estimate. We establish a regret
bound of $O(T^{2/3} \text{poly}(\log T))$ for this setting. | [
"Ting-Jui Chang",
"Shahin Shahrampour"
] | 2023-10-04 23:24:39 | http://arxiv.org/abs/2310.03206v1 | http://arxiv.org/pdf/2310.03206v1 | 2310.03206v1 |
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions | Machine scheduling aims to optimize job assignments to machines while
adhering to manufacturing rules and job specifications. This optimization leads
to reduced operational costs, improved customer demand fulfillment, and
enhanced production efficiency. However, machine scheduling remains a
challenging combinatorial problem due to its NP-hard nature. Deep Reinforcement
Learning (DRL), a key component of artificial general intelligence, has shown
promise in various domains like gaming and robotics. Researchers have explored
applying DRL to machine scheduling problems since 1995. This paper offers a
comprehensive review and comparison of DRL-based approaches, highlighting their
methodology, applications, advantages, and limitations. It categorizes these
approaches based on computational components: conventional neural networks,
encoder-decoder architectures, graph neural networks, and metaheuristic
algorithms. Our review concludes that DRL-based methods outperform exact
solvers, heuristics, and tabular reinforcement learning algorithms in terms of
computation speed and generating near-global optimal solutions. These DRL-based
approaches have been successfully applied to static and dynamic scheduling
across diverse machine environments and job characteristics. However, DRL-based
schedulers face limitations in handling complex operational constraints,
configurable multi-objective optimization, generalization, scalability,
interpretability, and robustness. Addressing these challenges will be a crucial
focus for future research in this field. This paper serves as a valuable
resource for researchers to assess the current state of DRL-based machine
scheduling and identify research gaps. It also aids experts and practitioners
in selecting the appropriate DRL approach for production scheduling. | [
"Maziyar Khadivi",
"Todd Charter",
"Marjan Yaghoubi",
"Masoud Jalayer",
"Maryam Ahang",
"Ardeshir Shojaeinasab",
"Homayoun Najjaran"
] | 2023-10-04 22:45:09 | http://arxiv.org/abs/2310.03195v1 | http://arxiv.org/pdf/2310.03195v1 | 2310.03195v1 |
ProGO: Probabilistic Global Optimizer | In the field of global optimization, many existing algorithms face challenges
posed by non-convex target functions and high computational complexity or
unavailability of gradient information. These limitations, exacerbated by
sensitivity to initial conditions, often lead to suboptimal solutions or failed
convergence. This is true even for Metaheuristic algorithms designed to
amalgamate different optimization techniques to improve their efficiency and
robustness. To address these challenges, we develop a sequence of
multidimensional integration-based methods that we show to converge to the
global optima under some mild regularity conditions. Our probabilistic approach
does not require the use of gradients and is underpinned by a mathematically
rigorous convergence framework anchored in the nuanced properties of nascent
optima distribution. In order to alleviate the problem of multidimensional
integration, we develop a latent slice sampler that enjoys a geometric rate of
convergence in generating samples from the nascent optima distribution, which
is used to approximate the global optima. The proposed Probabilistic Global
Optimizer (ProGO) provides a scalable unified framework to approximate the
global optima of any continuous function defined on a domain of arbitrary
dimension. Empirical illustrations of ProGO across a variety of popular
non-convex test functions (having finite global optima) reveal that the
proposed algorithm outperforms, by order of magnitude, many existing
state-of-the-art methods, including gradient-based, zeroth-order gradient-free,
and some Bayesian Optimization methods, in term regret value and speed of
convergence. It is, however, to be noted that our approach may not be suitable
for functions that are expensive to compute. | [
"Xinyu Zhang",
"Sujit Ghosh"
] | 2023-10-04 22:23:40 | http://arxiv.org/abs/2310.04457v2 | http://arxiv.org/pdf/2310.04457v2 | 2310.04457v2 |
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models | Medical image classification is a critical problem for healthcare, with the
potential to alleviate the workload of doctors and facilitate diagnoses of
patients. However, two challenges arise when deploying deep learning models to
real-world healthcare applications. First, neural models tend to learn spurious
correlations instead of desired features, which could fall short when
generalizing to new domains (e.g., patients with different ages). Second, these
black-box models lack interpretability. When making diagnostic predictions, it
is important to understand why a model makes a decision for trustworthy and
safety considerations. In this paper, to address these two limitations, we
propose a new paradigm to build robust and interpretable medical image
classifiers with natural language concepts. Specifically, we first query
clinical concepts from GPT-4, then transform latent image features into
explicit concepts with a vision-language model. We systematically evaluate our
method on eight medical image classification datasets to verify its
effectiveness. On challenging datasets with strong confounding factors, our
method can mitigate spurious correlations thus substantially outperform
standard visual encoders and other baselines. Finally, we show how
classification with a small number of concepts brings a level of
interpretability for understanding model decisions through case studies in real
medical data. | [
"An Yan",
"Yu Wang",
"Yiwu Zhong",
"Zexue He",
"Petros Karypis",
"Zihan Wang",
"Chengyu Dong",
"Amilcare Gentili",
"Chun-Nan Hsu",
"Jingbo Shang",
"Julian McAuley"
] | 2023-10-04 21:57:09 | http://arxiv.org/abs/2310.03182v1 | http://arxiv.org/pdf/2310.03182v1 | 2310.03182v1 |
Digital Ethics in Federated Learning | The Internet of Things (IoT) consistently generates vast amounts of data,
sparking increasing concern over the protection of data privacy and the
limitation of data misuse. Federated learning (FL) facilitates collaborative
capabilities among multiple parties by sharing machine learning (ML) model
parameters instead of raw user data, and it has recently gained significant
attention for its potential in privacy preservation and learning efficiency
enhancement. In this paper, we highlight the digital ethics concerns that arise
when human-centric devices serve as clients in FL. More specifically,
challenges of game dynamics, fairness, incentive, and continuity arise in FL
due to differences in perspectives and objectives between clients and the
server. We analyze these challenges and their solutions from the perspectives
of both the client and the server, and through the viewpoints of centralized
and decentralized FL. Finally, we explore the opportunities in FL for
human-centric IoT as directions for future development. | [
"Liangqi Yuan",
"Ziran Wang",
"Christopher G. Brinton"
] | 2023-10-04 21:48:35 | http://arxiv.org/abs/2310.03178v2 | http://arxiv.org/pdf/2310.03178v2 | 2310.03178v2 |
Test Case Recommendations with Distributed Representation of Code Syntactic Features | Frequent modifications of unit test cases are inevitable due to software's
continuous underlying changes in source code, design, and requirements. Since
manually maintaining software test suites is tedious, timely, and costly,
automating the process of generation and maintenance of test units will
significantly impact the effectiveness and efficiency of software testing
processes.
To this end, we propose an automated approach which exploits both structural
and semantic properties of source code methods and test cases to recommend the
most relevant and useful unit tests to the developers. The proposed approach
initially trains a neural network to transform method-level source code, as
well as unit tests, into distributed representations (embedded vectors) while
preserving the importance of the structure in the code. Retrieving the semantic
and structural properties of a given method, the approach computes cosine
similarity between the method's embedding and the previously-embedded training
instances. Further, according to the similarity scores between the embedding
vectors, the model identifies the closest methods of embedding and the
associated unit tests as the most similar recommendations.
The results on the Methods2Test dataset showed that, while there is no
guarantee to have similar relevant test cases for the group of similar methods,
the proposed approach extracts the most similar existing test cases for a given
method in the dataset, and evaluations show that recommended test cases
decrease the developers' effort to generating expected test cases. | [
"Mosab Rezaei",
"Hamed Alhoori",
"Mona Rahimi"
] | 2023-10-04 21:42:01 | http://arxiv.org/abs/2310.03174v1 | http://arxiv.org/pdf/2310.03174v1 | 2310.03174v1 |
Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors | Machine-learning phishing webpage detectors (ML-PWD) have been shown to
suffer from adversarial manipulations of the HTML code of the input webpage.
Nevertheless, the attacks recently proposed have demonstrated limited
effectiveness due to their lack of optimizing the usage of the adopted
manipulations, and they focus solely on specific elements of the HTML code. In
this work, we overcome these limitations by first designing a novel set of
fine-grained manipulations which allow to modify the HTML code of the input
phishing webpage without compromising its maliciousness and visual appearance,
i.e., the manipulations are functionality- and rendering-preserving by design.
We then select which manipulations should be applied to bypass the target
detector by a query-efficient black-box optimization algorithm. Our experiments
show that our attacks are able to raze to the ground the performance of current
state-of-the-art ML-PWD using just 30 queries, thus overcoming the weaker
attacks developed in previous work, and enabling a much fairer robustness
evaluation of ML-PWD. | [
"Biagio Montaruli",
"Luca Demetrio",
"Maura Pintor",
"Luca Compagna",
"Davide Balzarotti",
"Battista Biggio"
] | 2023-10-04 21:20:44 | http://arxiv.org/abs/2310.03166v2 | http://arxiv.org/pdf/2310.03166v2 | 2310.03166v2 |
Enhancing Accuracy in Deep Learning Using Random Matrix Theory | In this study, we explore the applications of random matrix theory (RMT) in
the training of deep neural networks (DNNs), focusing on layer pruning to
simplify DNN architecture and loss landscape. RMT, recently used to address
overfitting in deep learning, enables the examination of DNN's weight layer
spectra. We use these techniques to optimally determine the number of singular
values to be removed from the weight layers of a DNN during training via
singular value decomposition (SVD). This process aids in DNN simplification and
accuracy enhancement, as evidenced by training simple DNN models on the MNIST
and Fashion MNIST datasets.
Our method can be applied to any fully connected or convolutional layer of a
pretrained DNN, decreasing the layer's parameters and simplifying the DNN
architecture while preserving or even enhancing the model's accuracy. By
discarding small singular values based on RMT criteria, the accuracy of the
test set remains consistent, facilitating more efficient DNN training without
compromising performance.
We provide both theoretical and empirical evidence supporting our claim that
the elimination of small singular values based on RMT does not negatively
impact the DNN's accuracy. Our results offer valuable insights into the
practical application of RMT for the creation of more efficient and accurate
deep-learning models. | [
"Leonid Berlyand",
"Etienne Sandier",
"Yitzchak Shmalo",
"Lei Zhang"
] | 2023-10-04 21:17:31 | http://arxiv.org/abs/2310.03165v1 | http://arxiv.org/pdf/2310.03165v1 | 2310.03165v1 |
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