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On Prediction Feature Assignment in the Heckman Selection Model | Under missing-not-at-random (MNAR) sample selection bias, the performance of
a prediction model is often degraded. This paper focuses on one classic
instance of MNAR sample selection bias where a subset of samples have
non-randomly missing outcomes. The Heckman selection model and its variants
have commonly been used to handle this type of sample selection bias. The
Heckman model uses two separate equations to model the prediction and selection
of samples, where the selection features include all prediction features. When
using the Heckman model, the prediction features must be properly chosen from
the set of selection features. However, choosing the proper prediction features
is a challenging task for the Heckman model. This is especially the case when
the number of selection features is large. Existing approaches that use the
Heckman model often provide a manually chosen set of prediction features. In
this paper, we propose Heckman-FA as a novel data-driven framework for
obtaining prediction features for the Heckman model. Heckman-FA first trains an
assignment function that determines whether or not a selection feature is
assigned as a prediction feature. Using the parameters of the trained function,
the framework extracts a suitable set of prediction features based on the
goodness-of-fit of the prediction model given the chosen prediction features
and the correlation between noise terms of the prediction and selection
equations. Experimental results on real-world datasets show that Heckman-FA
produces a robust regression model under MNAR sample selection bias. | [
"Huy Mai",
"Xintao Wu"
] | 2023-09-14 22:10:09 | http://arxiv.org/abs/2309.08043v1 | http://arxiv.org/pdf/2309.08043v1 | 2309.08043v1 |
Stability Analysis of Non-Linear Classifiers using Gene Regulatory Neural Network for Biological AI | The Gene Regulatory Network (GRN) of biological cells governs a number of key
functionalities that enables them to adapt and survive through different
environmental conditions. Close observation of the GRN shows that the structure
and operational principles resembles an Artificial Neural Network (ANN), which
can pave the way for the development of Biological Artificial Intelligence. In
particular, a gene's transcription and translation process resembles a
sigmoidal-like property based on transcription factor inputs. In this paper, we
develop a mathematical model of gene-perceptron using a dual-layered
transcription-translation chemical reaction model, enabling us to transform a
GRN into a Gene Regulatory Neural Network (GRNN). We perform stability analysis
for each gene-perceptron within the fully-connected GRNN sub network to
determine temporal as well as stable concentration outputs that will result in
reliable computing performance. We focus on a non-linear classifier application
for the GRNN, where we analyzed generic multi-layer GRNNs as well as E.Coli
GRNN that is derived from trans-omic experimental data. Our analysis found that
varying the parameters of the chemical reactions can allow us shift the
boundaries of the classification region, laying the platform for programmable
GRNNs that suit diverse application requirements. | [
"Adrian Ratwatte",
"Samitha Somathilaka",
"Sasitharan Balasubramaniam",
"Assaf A. Gilad"
] | 2023-09-14 21:37:38 | http://arxiv.org/abs/2310.04424v1 | http://arxiv.org/pdf/2310.04424v1 | 2310.04424v1 |
USM-SCD: Multilingual Speaker Change Detection Based on Large Pretrained Foundation Models | We introduce a multilingual speaker change detection model (USM-SCD) that can
simultaneously detect speaker turns and perform ASR for 96 languages. This
model is adapted from a speech foundation model trained on a large quantity of
supervised and unsupervised data, demonstrating the utility of fine-tuning from
a large generic foundation model for a downstream task. We analyze the
performance of this multilingual speaker change detection model through a
series of ablation studies. We show that the USM-SCD model can achieve more
than 75% average speaker change detection F1 score across a test set that
consists of data from 96 languages. On American English, the USM-SCD model can
achieve an 85.8% speaker change detection F1 score across various public and
internal test sets, beating the previous monolingual baseline model by 21%
relative. We also show that we only need to fine-tune one-quarter of the
trainable model parameters to achieve the best model performance. The USM-SCD
model exhibits state-of-the-art ASR quality compared with a strong public ASR
baseline, making it suitable to handle both tasks with negligible additional
computational cost. | [
"Guanlong Zhao",
"Yongqiang Wang",
"Jason Pelecanos",
"Yu Zhang",
"Hank Liao",
"Yiling Huang",
"Han Lu",
"Quan Wang"
] | 2023-09-14 20:46:49 | http://arxiv.org/abs/2309.08023v1 | http://arxiv.org/pdf/2309.08023v1 | 2309.08023v1 |
CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation | The use of Mutual Information (MI) as a measure to evaluate the efficiency of
cryptosystems has an extensive history. However, estimating MI between unknown
random variables in a high-dimensional space is challenging. Recent advances in
machine learning have enabled progress in estimating MI using neural networks.
This work presents a novel application of MI estimation in the field of
cryptography. We propose applying this methodology directly to estimate the MI
between plaintext and ciphertext in a chosen plaintext attack. The leaked
information, if any, from the encryption could potentially be exploited by
adversaries to compromise the computational security of the cryptosystem. We
evaluate the efficiency of our approach by empirically analyzing multiple
encryption schemes and baseline approaches. Furthermore, we extend the analysis
to novel network coding-based cryptosystems that provide individual secrecy and
study the relationship between information leakage and input distribution. | [
"Benjamin D. Kim",
"Vipindev Adat Vasudevan",
"Jongchan Woo",
"Alejandro Cohen",
"Rafael G. L. D'Oliveira",
"Thomas Stahlbuhk",
"Muriel Médard"
] | 2023-09-14 20:30:04 | http://arxiv.org/abs/2309.08019v2 | http://arxiv.org/pdf/2309.08019v2 | 2309.08019v2 |
TCGF: A unified tensorized consensus graph framework for multi-view representation learning | Multi-view learning techniques have recently gained significant attention in
the machine learning domain for their ability to leverage consistency and
complementary information across multiple views. However, there remains a lack
of sufficient research on generalized multi-view frameworks that unify existing
works into a scalable and robust learning framework, as most current works
focus on specific styles of multi-view models. Additionally, most multi-view
learning works rely heavily on specific-scale scenarios and fail to effectively
comprehend multiple scales holistically. These limitations hinder the effective
fusion of essential information from multiple views, resulting in poor
generalization. To address these limitations, this paper proposes a universal
multi-view representation learning framework named Tensorized Consensus Graph
Framework (TCGF). Specifically, it first provides a unified framework for
existing multi-view works to exploit the representations for individual view,
which aims to be suitable for arbitrary assumptions and different-scales
datasets. Then, stacks them into a tensor under alignment basics as a
high-order representation, allowing for the smooth propagation of consistency
and complementary information across all views. Moreover, TCGF proposes
learning a consensus embedding shared by adaptively collaborating all views to
uncover the essential structure of the multi-view data, which utilizes
view-consensus grouping effect to regularize the view-consensus representation.
To further facilitate related research, we provide a specific implementation of
TCGF for large-scale datasets, which can be efficiently solved by applying the
alternating optimization strategy. Experimental results conducted on seven
different-scales datasets indicate the superiority of the proposed TCGF against
existing state-of-the-art multi-view learning methods. | [
"Xiangzhu Meng",
"Wei Wei",
"Qiang Liu",
"Shu Wu",
"Liang Wang"
] | 2023-09-14 19:29:14 | http://arxiv.org/abs/2309.09987v1 | http://arxiv.org/pdf/2309.09987v1 | 2309.09987v1 |
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone | This paper presents an automated machine learning framework designed to
assist hydrologists in detecting anomalies in time series data generated by
sensors in a research watershed in the northeastern United States critical
zone. The framework specifically focuses on identifying peak-pattern anomalies,
which may arise from sensor malfunctions or natural phenomena. However, the use
of classification methods for anomaly detection poses challenges, such as the
requirement for labeled data as ground truth and the selection of the most
suitable deep learning model for the given task and dataset. To address these
challenges, our framework generates labeled datasets by injecting synthetic
peak patterns into synthetically generated time series data and incorporates an
automated hyperparameter optimization mechanism. This mechanism generates an
optimized model instance with the best architectural and training parameters
from a pool of five selected models, namely Temporal Convolutional Network
(TCN), InceptionTime, MiniRocket, Residual Networks (ResNet), and Long
Short-Term Memory (LSTM). The selection is based on the user's preferences
regarding anomaly detection accuracy and computational cost. The framework
employs Time-series Generative Adversarial Networks (TimeGAN) as the synthetic
dataset generator. The generated model instances are evaluated using a
combination of accuracy and computational cost metrics, including training time
and memory, during the anomaly detection process. Performance evaluation of the
framework was conducted using a dataset from a watershed, demonstrating
consistent selection of the most fitting model instance that satisfies the
user's preferences. | [
"Ijaz Ul Haq",
"Byung Suk Lee",
"Donna M. Rizzo",
"Julia N Perdrial"
] | 2023-09-14 19:07:50 | http://arxiv.org/abs/2309.07992v1 | http://arxiv.org/pdf/2309.07992v1 | 2309.07992v1 |
Folding Attention: Memory and Power Optimization for On-Device Transformer-based Streaming Speech Recognition | Transformer-based models excel in speech recognition. Existing efforts to
optimize Transformer inference, typically for long-context applications, center
on simplifying attention score calculations. However, streaming speech
recognition models usually process a limited number of tokens each time, making
attention score calculation less of a bottleneck. Instead, the bottleneck lies
in the linear projection layers of multi-head attention and feedforward
networks, constituting a substantial portion of the model size and contributing
significantly to computation, memory, and power usage.
To address this bottleneck, we propose folding attention, a technique
targeting these linear layers, significantly reducing model size and improving
memory and power efficiency. Experiments on on-device Transformer-based
streaming speech recognition models show that folding attention reduces model
size (and corresponding memory consumption) by up to 24% and power consumption
by up to 23%, all without compromising model accuracy or computation overhead. | [
"Yang Li",
"Liangzhen Lai",
"Yuan Shangguan",
"Forrest N. Iandola",
"Ernie Chang",
"Yangyang Shi",
"Vikas Chandra"
] | 2023-09-14 19:01:08 | http://arxiv.org/abs/2309.07988v2 | http://arxiv.org/pdf/2309.07988v2 | 2309.07988v2 |
Viewpoint Textual Inversion: Unleashing Novel View Synthesis with Pretrained 2D Diffusion Models | Text-to-image diffusion models understand spatial relationship between
objects, but do they represent the true 3D structure of the world from only 2D
supervision? We demonstrate that yes, 3D knowledge is encoded in 2D image
diffusion models like Stable Diffusion, and we show that this structure can be
exploited for 3D vision tasks. Our method, Viewpoint Neural Textual Inversion
(ViewNeTI), controls the 3D viewpoint of objects in generated images from
frozen diffusion models. We train a small neural mapper to take camera
viewpoint parameters and predict text encoder latents; the latents then
condition the diffusion generation process to produce images with the desired
camera viewpoint.
ViewNeTI naturally addresses Novel View Synthesis (NVS). By leveraging the
frozen diffusion model as a prior, we can solve NVS with very few input views;
we can even do single-view novel view synthesis. Our single-view NVS
predictions have good semantic details and photorealism compared to prior
methods. Our approach is well suited for modeling the uncertainty inherent in
sparse 3D vision problems because it can efficiently generate diverse samples.
Our view-control mechanism is general, and can even change the camera view in
images generated by user-defined prompts. | [
"James Burgess",
"Kuan-Chieh Wang",
"Serena Yeung"
] | 2023-09-14 18:52:16 | http://arxiv.org/abs/2309.07986v1 | http://arxiv.org/pdf/2309.07986v1 | 2309.07986v1 |
SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems | Membership inference attacks allow adversaries to determine whether a
particular example was contained in the model's training dataset. While
previous works have confirmed the feasibility of such attacks in various
applications, none has focused on speaker recognition (SR), a promising
voice-based biometric recognition technique. In this work, we propose SLMIA-SR,
the first membership inference attack tailored to SR. In contrast to
conventional example-level attack, our attack features speaker-level membership
inference, i.e., determining if any voices of a given speaker, either the same
as or different from the given inference voices, have been involved in the
training of a model. It is particularly useful and practical since the training
and inference voices are usually distinct, and it is also meaningful
considering the open-set nature of SR, namely, the recognition speakers were
often not present in the training data. We utilize intra-closeness and
inter-farness, two training objectives of SR, to characterize the differences
between training and non-training speakers and quantify them with two groups of
features driven by carefully-established feature engineering to mount the
attack. To improve the generalizability of our attack, we propose a novel
mixing ratio training strategy to train attack models. To enhance the attack
performance, we introduce voice chunk splitting to cope with the limited number
of inference voices and propose to train attack models dependent on the number
of inference voices. Our attack is versatile and can work in both white-box and
black-box scenarios. Additionally, we propose two novel techniques to reduce
the number of black-box queries while maintaining the attack performance.
Extensive experiments demonstrate the effectiveness of SLMIA-SR. | [
"Guangke Chen",
"Yedi Zhang",
"Fu Song"
] | 2023-09-14 18:40:28 | http://arxiv.org/abs/2309.07983v1 | http://arxiv.org/pdf/2309.07983v1 | 2309.07983v1 |
Uncertainty quantification for learned ISTA | Model-based deep learning solutions to inverse problems have attracted
increasing attention in recent years as they bridge state-of-the-art numerical
performance with interpretability. In addition, the incorporated prior domain
knowledge can make the training more efficient as the smaller number of
parameters allows the training step to be executed with smaller datasets.
Algorithm unrolling schemes stand out among these model-based learning
techniques. Despite their rapid advancement and their close connection to
traditional high-dimensional statistical methods, they lack certainty estimates
and a theory for uncertainty quantification is still elusive. This work
provides a step towards closing this gap proposing a rigorous way to obtain
confidence intervals for the LISTA estimator. | [
"Frederik Hoppe",
"Claudio Mayrink Verdun",
"Felix Krahmer",
"Hannah Laus",
"Holger Rauhut"
] | 2023-09-14 18:39:07 | http://arxiv.org/abs/2309.07982v1 | http://arxiv.org/pdf/2309.07982v1 | 2309.07982v1 |
A Data Source for Reasoning Embodied Agents | Recent progress in using machine learning models for reasoning tasks has been
driven by novel model architectures, large-scale pre-training protocols, and
dedicated reasoning datasets for fine-tuning. In this work, to further pursue
these advances, we introduce a new data generator for machine reasoning that
integrates with an embodied agent. The generated data consists of templated
text queries and answers, matched with world-states encoded into a database.
The world-states are a result of both world dynamics and the actions of the
agent. We show the results of several baseline models on instantiations of
train sets. These include pre-trained language models fine-tuned on a
text-formatted representation of the database, and graph-structured
Transformers operating on a knowledge-graph representation of the database. We
find that these models can answer some questions about the world-state, but
struggle with others. These results hint at new research directions in
designing neural reasoning models and database representations. Code to
generate the data will be released at github.com/facebookresearch/neuralmemory | [
"Jack Lanchantin",
"Sainbayar Sukhbaatar",
"Gabriel Synnaeve",
"Yuxuan Sun",
"Kavya Srinet",
"Arthur Szlam"
] | 2023-09-14 18:17:16 | http://arxiv.org/abs/2309.07974v1 | http://arxiv.org/pdf/2309.07974v1 | 2309.07974v1 |
Physically Plausible Full-Body Hand-Object Interaction Synthesis | We propose a physics-based method for synthesizing dexterous hand-object
interactions in a full-body setting. While recent advancements have addressed
specific facets of human-object interactions, a comprehensive physics-based
approach remains a challenge. Existing methods often focus on isolated segments
of the interaction process and rely on data-driven techniques that may result
in artifacts. In contrast, our proposed method embraces reinforcement learning
(RL) and physics simulation to mitigate the limitations of data-driven
approaches. Through a hierarchical framework, we first learn skill priors for
both body and hand movements in a decoupled setting. The generic skill priors
learn to decode a latent skill embedding into the motion of the underlying
part. A high-level policy then controls hand-object interactions in these
pretrained latent spaces, guided by task objectives of grasping and 3D target
trajectory following. It is trained using a novel reward function that combines
an adversarial style term with a task reward, encouraging natural motions while
fulfilling the task incentives. Our method successfully accomplishes the
complete interaction task, from approaching an object to grasping and
subsequent manipulation. We compare our approach against kinematics-based
baselines and show that it leads to more physically plausible motions. | [
"Jona Braun",
"Sammy Christen",
"Muhammed Kocabas",
"Emre Aksan",
"Otmar Hilliges"
] | 2023-09-14 17:55:18 | http://arxiv.org/abs/2309.07907v1 | http://arxiv.org/pdf/2309.07907v1 | 2309.07907v1 |
Improving physics-informed DeepONets with hard constraints | Current physics-informed (standard or operator) neural networks still rely on
accurately learning the initial conditions of the system they are solving. In
contrast, standard numerical methods evolve such initial conditions without
needing to learn these. In this study, we propose to improve current
physics-informed deep learning strategies such that initial conditions do not
need to be learned and are represented exactly in the predicted solution.
Moreover, this method guarantees that when a DeepONet is applied multiple times
to time step a solution, the resulting function is continuous. | [
"Rüdiger Brecht",
"Dmytro R. Popovych",
"Alex Bihlo",
"Roman O. Popovych"
] | 2023-09-14 17:48:30 | http://arxiv.org/abs/2309.07899v1 | http://arxiv.org/pdf/2309.07899v1 | 2309.07899v1 |
Choosing a Proxy Metric from Past Experiments | In many randomized experiments, the treatment effect of the long-term metric
(i.e. the primary outcome of interest) is often difficult or infeasible to
measure. Such long-term metrics are often slow to react to changes and
sufficiently noisy they are challenging to faithfully estimate in short-horizon
experiments. A common alternative is to measure several short-term proxy
metrics in the hope they closely track the long-term metric -- so they can be
used to effectively guide decision-making in the near-term. We introduce a new
statistical framework to both define and construct an optimal proxy metric for
use in a homogeneous population of randomized experiments. Our procedure first
reduces the construction of an optimal proxy metric in a given experiment to a
portfolio optimization problem which depends on the true latent treatment
effects and noise level of experiment under consideration. We then denoise the
observed treatment effects of the long-term metric and a set of proxies in a
historical corpus of randomized experiments to extract estimates of the latent
treatment effects for use in the optimization problem. One key insight derived
from our approach is that the optimal proxy metric for a given experiment is
not apriori fixed; rather it should depend on the sample size (or effective
noise level) of the randomized experiment for which it is deployed. To
instantiate and evaluate our framework, we employ our methodology in a large
corpus of randomized experiments from an industrial recommendation system and
construct proxy metrics that perform favorably relative to several baselines. | [
"Nilesh Tripuraneni",
"Lee Richardson",
"Alexander D'Amour",
"Jacopo Soriano",
"Steve Yadlowsky"
] | 2023-09-14 17:43:02 | http://arxiv.org/abs/2309.07893v1 | http://arxiv.org/pdf/2309.07893v1 | 2309.07893v1 |
A Novel Local-Global Feature Fusion Framework for Body-weight Exercise Recognition with Pressure Mapping Sensors | We present a novel local-global feature fusion framework for body-weight
exercise recognition with floor-based dynamic pressure maps. One step further
from the existing studies using deep neural networks mainly focusing on global
feature extraction, the proposed framework aims to combine local and global
features using image processing techniques and the YOLO object detection to
localize pressure profiles from different body parts and consider physical
constraints. The proposed local feature extraction method generates two sets of
high-level local features consisting of cropped pressure mapping and numerical
features such as angular orientation, location on the mat, and pressure area.
In addition, we adopt a knowledge distillation for regularization to preserve
the knowledge of the global feature extraction and improve the performance of
the exercise recognition. Our experimental results demonstrate a notable 11
percent improvement in F1 score for exercise recognition while preserving
label-specific features. | [
"Davinder Pal Singh",
"Lala Shakti Swarup Ray",
"Bo Zhou",
"Sungho Suh",
"Paul Lukowicz"
] | 2023-09-14 17:40:44 | http://arxiv.org/abs/2309.07888v1 | http://arxiv.org/pdf/2309.07888v1 | 2309.07888v1 |
Some notes concerning a generalized KMM-type optimization method for density ratio estimation | In the present paper we introduce new optimization algorithms for the task of
density ratio estimation. More precisely, we consider extending the well-known
KMM method using the construction of a suitable loss function, in order to
encompass more general situations involving the estimation of density ratio
with respect to subsets of the training data and test data, respectively. The
associated codes can be found at https://github.com/CDAlecsa/Generalized-KMM. | [
"Cristian Daniel Alecsa"
] | 2023-09-14 17:36:53 | http://arxiv.org/abs/2309.07887v1 | http://arxiv.org/pdf/2309.07887v1 | 2309.07887v1 |
Beta Diffusion | We introduce beta diffusion, a novel generative modeling method that
integrates demasking and denoising to generate data within bounded ranges.
Using scaled and shifted beta distributions, beta diffusion utilizes
multiplicative transitions over time to create both forward and reverse
diffusion processes, maintaining beta distributions in both the forward
marginals and the reverse conditionals, given the data at any point in time.
Unlike traditional diffusion-based generative models relying on additive
Gaussian noise and reweighted evidence lower bounds (ELBOs), beta diffusion is
multiplicative and optimized with KL-divergence upper bounds (KLUBs) derived
from the convexity of the KL divergence. We demonstrate that the proposed KLUBs
are more effective for optimizing beta diffusion compared to negative ELBOs,
which can also be derived as the KLUBs of the same KL divergence with its two
arguments swapped. The loss function of beta diffusion, expressed in terms of
Bregman divergence, further supports the efficacy of KLUBs for optimization.
Experimental results on both synthetic data and natural images demonstrate the
unique capabilities of beta diffusion in generative modeling of range-bounded
data and validate the effectiveness of KLUBs in optimizing diffusion models,
thereby making them valuable additions to the family of diffusion-based
generative models and the optimization techniques used to train them. | [
"Mingyuan Zhou",
"Tianqi Chen",
"Zhendong Wang",
"Huangjie Zheng"
] | 2023-09-14 17:14:26 | http://arxiv.org/abs/2309.07867v1 | http://arxiv.org/pdf/2309.07867v1 | 2309.07867v1 |
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries | Deep learning was recently successfully used in deriving symmetry
transformations that preserve important physics quantities. Being completely
agnostic, these techniques postpone the identification of the discovered
symmetries to a later stage. In this letter we propose methods for examining
and identifying the group-theoretic structure of such machine-learned
symmetries. We design loss functions which probe the subalgebra structure
either during the deep learning stage of symmetry discovery or in a subsequent
post-processing stage. We illustrate the new methods with examples from the
U(n) Lie group family, obtaining the respective subalgebra decompositions. As
an application to particle physics, we demonstrate the identification of the
residual symmetries after the spontaneous breaking of non-Abelian gauge
symmetries like SU(3) and SU(5) which are commonly used in model building. | [
"Roy T. Forestano",
"Konstantin T. Matchev",
"Katia Matcheva",
"Alexander Roman",
"Eyup B. Unlu",
"Sarunas Verner"
] | 2023-09-14 17:03:50 | http://arxiv.org/abs/2309.07860v1 | http://arxiv.org/pdf/2309.07860v1 | 2309.07860v1 |
Complex-Valued Neural Networks for Data-Driven Signal Processing and Signal Understanding | Complex-valued neural networks have emerged boasting superior modeling
performance for many tasks across the signal processing, sensing, and
communications arenas. However, developing complex-valued models currently
demands development of basic deep learning operations, such as linear or
convolution layers, as modern deep learning frameworks like PyTorch and Tensor
flow do not adequately support complex-valued neural networks. This paper
overviews a package built on PyTorch with the intention of implementing
light-weight interfaces for common complex-valued neural network operations and
architectures. Similar to natural language understanding (NLU), which as
recently made tremendous leaps towards text-based intelligence, RF Signal
Understanding (RFSU) is a promising field extending conventional signal
processing algorithms using a hybrid approach of signal mechanics-based insight
with data-driven modeling power. Notably, we include efficient implementations
for linear, convolution, and attention modules in addition to activation
functions and normalization layers such as batchnorm and layernorm.
Additionally, we include efficient implementations of manifold-based
complex-valued neural network layers that have shown tremendous promise but
remain relatively unexplored in many research contexts. Although there is an
emphasis on 1-D data tensors, due to a focus on signal processing,
communications, and radar data, many of the routines are implemented for 2-D
and 3-D data as well. Specifically, the proposed approach offers a useful set
of tools and documentation for data-driven signal processing research and
practical implementation. | [
"Josiah W. Smith"
] | 2023-09-14 16:55:28 | http://arxiv.org/abs/2309.07948v1 | http://arxiv.org/pdf/2309.07948v1 | 2309.07948v1 |
Learning to Warm-Start Fixed-Point Optimization Algorithms | We introduce a machine-learning framework to warm-start fixed-point
optimization algorithms. Our architecture consists of a neural network mapping
problem parameters to warm starts, followed by a predefined number of
fixed-point iterations. We propose two loss functions designed to either
minimize the fixed-point residual or the distance to a ground truth solution.
In this way, the neural network predicts warm starts with the end-to-end goal
of minimizing the downstream loss. An important feature of our architecture is
its flexibility, in that it can predict a warm start for fixed-point algorithms
run for any number of steps, without being limited to the number of steps it
has been trained on. We provide PAC-Bayes generalization bounds on unseen data
for common classes of fixed-point operators: contractive, linearly convergent,
and averaged. Applying this framework to well-known applications in control,
statistics, and signal processing, we observe a significant reduction in the
number of iterations and solution time required to solve these problems,
through learned warm starts. | [
"Rajiv Sambharya",
"Georgina Hall",
"Brandon Amos",
"Bartolomeo Stellato"
] | 2023-09-14 16:22:14 | http://arxiv.org/abs/2309.07835v1 | http://arxiv.org/pdf/2309.07835v1 | 2309.07835v1 |
Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis | Directed graphs are a natural model for many phenomena, in particular
scientific knowledge graphs such as molecular interaction or chemical reaction
networks that define cellular signaling relationships. In these situations,
source nodes typically have distinct biophysical properties from sinks. Due to
their ordered and unidirectional relationships, many such networks also have
hierarchical and multiscale structure. However, the majority of methods
performing node- and edge-level tasks in machine learning do not take these
properties into account, and thus have not been leveraged effectively for
scientific tasks such as cellular signaling network inference. We propose a new
framework called Directed Scattering Autoencoder (DSAE) which uses a directed
version of a geometric scattering transform, combined with the non-linear
dimensionality reduction properties of an autoencoder and the geometric
properties of the hyperbolic space to learn latent hierarchies. We show this
method outperforms numerous others on tasks such as embedding directed graphs
and learning cellular signaling networks. | [
"Aarthi Venkat",
"Joyce Chew",
"Ferran Cardoso Rodriguez",
"Christopher J. Tape",
"Michael Perlmutter",
"Smita Krishnaswamy"
] | 2023-09-14 15:59:23 | http://arxiv.org/abs/2309.07813v1 | http://arxiv.org/pdf/2309.07813v1 | 2309.07813v1 |
Text Classification of Cancer Clinical Trial Eligibility Criteria | Automatic identification of clinical trials for which a patient is eligible
is complicated by the fact that trial eligibility is stated in natural
language. A potential solution to this problem is to employ text classification
methods for common types of eligibility criteria. In this study, we focus on
seven common exclusion criteria in cancer trials: prior malignancy, human
immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness,
drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase
III cancer trials with these exclusions annotated at the trial level. We
experiment with common transformer models as well as a new pre-trained clinical
trial BERT model. Our results demonstrate the feasibility of automatically
classifying common exclusion criteria. Additionally, we demonstrate the value
of a pre-trained language model specifically for clinical trials, which yields
the highest average performance across all criteria. | [
"Yumeng Yang",
"Soumya Jayaraj",
"Ethan B Ludmir",
"Kirk Roberts"
] | 2023-09-14 15:59:16 | http://arxiv.org/abs/2309.07812v2 | http://arxiv.org/pdf/2309.07812v2 | 2309.07812v2 |
Communication Efficient Private Federated Learning Using Dithering | The task of preserving privacy while ensuring efficient communication is a
fundamental challenge in federated learning. In this work, we tackle this
challenge in the trusted aggregator model, and propose a solution that achieves
both objectives simultaneously. We show that employing a quantization scheme
based on subtractive dithering at the clients can effectively replicate the
normal noise addition process at the aggregator. This implies that we can
guarantee the same level of differential privacy against other clients while
substantially reducing the amount of communication required, as opposed to
transmitting full precision gradients and using central noise addition. We also
experimentally demonstrate that the accuracy of our proposed approach matches
that of the full precision gradient method. | [
"Burak Hasircioglu",
"Deniz Gunduz"
] | 2023-09-14 15:55:58 | http://arxiv.org/abs/2309.07809v1 | http://arxiv.org/pdf/2309.07809v1 | 2309.07809v1 |
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for Automated Driving | More research attention has recently been given to end-to-end autonomous
driving technologies where the entire driving pipeline is replaced with a
single neural network because of its simpler structure and faster inference
time. Despite this appealing approach largely reducing the components in
driving pipeline, its simplicity also leads to interpretability problems and
safety issues arXiv:2003.06404. The trained policy is not always compliant with
the traffic rules and it is also hard to discover the reason for the
misbehavior because of the lack of intermediate outputs. Meanwhile, Sensors are
also critical to autonomous driving's security and feasibility to perceive the
surrounding environment under complex driving scenarios. In this paper, we
proposed P-CSG, a novel penalty-based imitation learning approach with cross
semantics generation sensor fusion technologies to increase the overall
performance of End-to-End Autonomous Driving. We conducted an assessment of our
model's performance using the Town 05 Long benchmark, achieving an impressive
driving score improvement of over 15%. Furthermore, we conducted robustness
evaluations against adversarial attacks like FGSM and Dot attacks, revealing a
substantial increase in robustness compared to baseline models.More detailed
information, such as code-based resources, ablation studies and videos can be
found at https://hk-zh.github.io/p-csg-plus. | [
"Hongkuan Zhou",
"Aifen Sui",
"Wei Cao",
"Letian Shi"
] | 2023-09-14 15:54:56 | http://arxiv.org/abs/2309.07808v1 | http://arxiv.org/pdf/2309.07808v1 | 2309.07808v1 |
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary tasks | Effectively leveraging multimodal information from social media posts is
essential to various downstream tasks such as sentiment analysis, sarcasm
detection and hate speech classification. However, combining text and image
information is challenging because of the idiosyncratic cross-modal semantics
with hidden or complementary information present in matching image-text pairs.
In this work, we aim to directly model this by proposing the use of two
auxiliary losses jointly with the main task when fine-tuning any pre-trained
multimodal model. Image-Text Contrastive (ITC) brings image-text
representations of a post closer together and separates them from different
posts, capturing underlying dependencies. Image-Text Matching (ITM) facilitates
the understanding of semantic correspondence between images and text by
penalizing unrelated pairs. We combine these objectives with five multimodal
models, demonstrating consistent improvements across four popular social media
datasets. Furthermore, through detailed analysis, we shed light on the specific
scenarios and cases where each auxiliary task proves to be most effective. | [
"Danae Sánchez Villegas",
"Daniel Preoţiuc-Pietro",
"Nikolaos Aletras"
] | 2023-09-14 15:30:59 | http://arxiv.org/abs/2309.07794v1 | http://arxiv.org/pdf/2309.07794v1 | 2309.07794v1 |
TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis | In recent years, functional magnetic resonance imaging has emerged as a
powerful tool for investigating the human brain's functional connectivity
networks. Related studies demonstrate that functional connectivity networks in
the human brain can help to improve the efficiency of diagnosing neurological
disorders. However, there still exist two challenges that limit the progress of
functional neuroimaging. Firstly, there exists an abundance of noise and
redundant information in functional connectivity data, resulting in poor
performance. Secondly, existing brain network models have tended to prioritize
either classification performance or the interpretation of neuroscience
findings behind the learned models. To deal with these challenges, this paper
proposes a novel brain graph learning framework called Template-induced Brain
Graph Learning (TiBGL), which has both discriminative and interpretable
abilities. Motivated by the related medical findings on functional
connectivites, TiBGL proposes template-induced brain graph learning to extract
template brain graphs for all groups. The template graph can be regarded as an
augmentation process on brain networks that removes noise information and
highlights important connectivity patterns. To simultaneously support the tasks
of discrimination and interpretation, TiBGL further develops template-induced
convolutional neural network and template-induced brain interpretation
analysis. Especially, the former fuses rich information from brain graphs and
template brain graphs for brain disorder tasks, and the latter can provide
insightful connectivity patterns related to brain disorders based on template
brain graphs. Experimental results on three real-world datasets show that the
proposed TiBGL can achieve superior performance compared with nine
state-of-the-art methods and keep coherent with neuroscience findings in recent
literatures. | [
"Xiangzhu Meng",
"Wei Wei",
"Qiang Liu",
"Shu Wu",
"Liang Wang"
] | 2023-09-14 15:17:42 | http://arxiv.org/abs/2309.07947v1 | http://arxiv.org/pdf/2309.07947v1 | 2309.07947v1 |
Virchow: A Million-Slide Digital Pathology Foundation Model | Computational pathology uses artificial intelligence to enable precision
medicine and decision support systems through the analysis of whole slide
images. It has the potential to revolutionize the diagnosis and treatment of
cancer. However, a major challenge to this objective is that for many specific
computational pathology tasks the amount of data is inadequate for development.
To address this challenge, we created Virchow, a 632 million parameter deep
neural network foundation model for computational pathology. Using
self-supervised learning, Virchow is trained on 1.5 million hematoxylin and
eosin stained whole slide images from diverse tissue groups, which is orders of
magnitude more data than previous works. When evaluated on downstream tasks
including tile-level pan-cancer detection and subtyping and slide-level
biomarker prediction, Virchow outperforms state-of-the-art systems both on
internal datasets drawn from the same population as the pretraining data as
well as external public datasets. Virchow achieves 93% balanced accuracy for
pancancer tile classification, and AUCs of 0.983 for colon microsatellite
instability status prediction and 0.967 for breast CDH1 status prediction. The
gains in performance highlight the importance of pretraining on massive
pathology image datasets, suggesting pretraining on even larger datasets could
continue improving performance for many high-impact applications where limited
amounts of training data are available, such as drug outcome prediction. | [
"Eugene Vorontsov",
"Alican Bozkurt",
"Adam Casson",
"George Shaikovski",
"Michal Zelechowski",
"Siqi Liu",
"Philippe Mathieu",
"Alexander van Eck",
"Donghun Lee",
"Julian Viret",
"Eric Robert",
"Yi Kan Wang",
"Jeremy D. Kunz",
"Matthew C. H. Lee",
"Jan Bernhard",
"Ran A. Godrich",
"Gerard Oakley",
"Ewan Millar",
"Matthew Hanna",
"Juan Retamero",
"William A. Moye",
"Razik Yousfi",
"Christopher Kanan",
"David Klimstra",
"Brandon Rothrock",
"Thomas J. Fuchs"
] | 2023-09-14 15:09:35 | http://arxiv.org/abs/2309.07778v3 | http://arxiv.org/pdf/2309.07778v3 | 2309.07778v3 |
Variational Quantum Linear Solver enhanced Quantum Support Vector Machine | Quantum Support Vector Machines (QSVM) play a vital role in using quantum
resources for supervised machine learning tasks, such as classification.
However, current methods are strongly limited in terms of scalability on Noisy
Intermediate Scale Quantum (NISQ) devices. In this work, we propose a novel
approach called the Variational Quantum Linear Solver (VQLS) enhanced QSVM.
This is built upon our idea of utilizing the variational quantum linear solver
to solve system of linear equations of a least squares-SVM on a NISQ device.
The implementation of our approach is evaluated by an extensive series of
numerical experiments with the Iris dataset, which consists of three distinct
iris plant species. Based on this, we explore the practicality and
effectiveness of our algorithm by constructing a classifier capable of
classification in a feature space ranging from one to seven dimensions.
Furthermore, by strategically exploiting both classical and quantum computing
for various subroutines of our algorithm, we effectively mitigate practical
challenges associated with the implementation. These include significant
improvement in the trainability of the variational ansatz and notable
reductions in run-time for cost calculations. Based on the numerical
experiments, our approach exhibits the capability of identifying a separating
hyperplane in an 8-dimensional feature space. Moreover, it consistently
demonstrated strong performance across various instances with the same dataset. | [
"Jianming Yi",
"Kalyani Suresh",
"Ali Moghiseh",
"Norbert Wehn"
] | 2023-09-14 14:59:58 | http://arxiv.org/abs/2309.07770v1 | http://arxiv.org/pdf/2309.07770v1 | 2309.07770v1 |
PRE: Vision-Language Prompt Learning with Reparameterization Encoder | Large pre-trained vision-language models such as CLIP have demonstrated great
potential in zero-shot transferability to downstream tasks. However, to attain
optimal performance, the manual selection of prompts is necessary to improve
alignment between the downstream image distribution and the textual class
descriptions. This manual prompt engineering is the major challenge for
deploying such models in practice since it requires domain expertise and is
extremely time-consuming. To avoid non-trivial prompt engineering, recent work
Context Optimization (CoOp) introduced the concept of prompt learning to the
vision domain using learnable textual tokens. While CoOp can achieve
substantial improvements over manual prompts, its learned context is worse
generalizable to wider unseen classes within the same dataset. In this work, we
present Prompt Learning with Reparameterization Encoder (PRE) - a simple and
efficient method that enhances the generalization ability of the learnable
prompt to unseen classes while maintaining the capacity to learn Base classes.
Instead of directly optimizing the prompts, PRE employs a prompt encoder to
reparameterize the input prompt embeddings, enhancing the exploration of
task-specific knowledge from few-shot samples. Experiments and extensive
ablation studies on 8 benchmarks demonstrate that our approach is an efficient
method for prompt learning. Specifically, PRE achieves a notable enhancement of
5.60% in average accuracy on New classes and 3% in Harmonic mean compared to
CoOp in the 16-shot setting, all achieved within a good training time. | [
"Anh Pham Thi Minh"
] | 2023-09-14 14:48:01 | http://arxiv.org/abs/2309.07760v1 | http://arxiv.org/pdf/2309.07760v1 | 2309.07760v1 |
Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning | Focus in Explainable AI is shifting from explanations defined in terms of
low-level elements, such as input features, to explanations encoded in terms of
interpretable concepts learned from data. How to reliably acquire such concepts
is, however, still fundamentally unclear. An agreed-upon notion of concept
interpretability is missing, with the result that concepts used by both
post-hoc explainers and concept-based neural networks are acquired through a
variety of mutually incompatible strategies. Critically, most of these neglect
the human side of the problem: a representation is understandable only insofar
as it can be understood by the human at the receiving end. The key challenge in
Human-interpretable Representation Learning (HRL) is how to model and
operationalize this human element. In this work, we propose a mathematical
framework for acquiring interpretable representations suitable for both
post-hoc explainers and concept-based neural networks. Our formalization of HRL
builds on recent advances in causal representation learning and explicitly
models a human stakeholder as an external observer. This allows us to derive a
principled notion of alignment between the machine representation and the
vocabulary of concepts understood by the human. In doing so, we link alignment
and interpretability through a simple and intuitive name transfer game, and
clarify the relationship between alignment and a well-known property of
representations, namely disentanglment. We also show that alignment is linked
to the issue of undesirable correlations among concepts, also known as concept
leakage, and to content-style separation, all through a general
information-theoretic reformulation of these properties. Our conceptualization
aims to bridge the gap between the human and algorithmic sides of
interpretability and establish a stepping stone for new research on
human-interpretable representations. | [
"Emanuele Marconato",
"Andrea Passerini",
"Stefano Teso"
] | 2023-09-14 14:26:20 | http://arxiv.org/abs/2309.07742v1 | http://arxiv.org/pdf/2309.07742v1 | 2309.07742v1 |
Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning | We present a physics-informed machine-learning (PIML) approach for the
approximation of slow invariant manifolds (SIMs) of singularly perturbed
systems, providing functionals in an explicit form that facilitate the
construction and numerical integration of reduced order models (ROMs). The
proposed scheme solves a partial differential equation corresponding to the
invariance equation (IE) within the Geometric Singular Perturbation Theory
(GSPT) framework. For the solution of the IE, we used two neural network
structures, namely feedforward neural networks (FNNs), and random projection
neural networks (RPNNs), with symbolic differentiation for the computation of
the gradients required for the learning process. The efficiency of our PIML
method is assessed via three benchmark problems, namely the Michaelis-Menten,
the target mediated drug disposition reaction mechanism, and the 3D Sel'kov
model. We show that the proposed PIML scheme provides approximations, of
equivalent or even higher accuracy, than those provided by other traditional
GSPT-based methods, and importantly, for any practical purposes, it is not
affected by the magnitude of the perturbation parameter. This is of particular
importance, as there are many systems for which the gap between the fast and
slow timescales is not that big, but still ROMs can be constructed. A
comparison of the computational costs between symbolic, automatic and numerical
approximation of the required derivatives in the learning process is also
provided. | [
"Dimitrios G. Patsatzis",
"Gianluca Fabiani",
"Lucia Russo",
"Constantinos Siettos"
] | 2023-09-14 14:10:22 | http://arxiv.org/abs/2309.07946v1 | http://arxiv.org/pdf/2309.07946v1 | 2309.07946v1 |
Understanding Vector-Valued Neural Networks and Their Relationship with Real and Hypercomplex-Valued Neural Networks | Despite the many successful applications of deep learning models for
multidimensional signal and image processing, most traditional neural networks
process data represented by (multidimensional) arrays of real numbers. The
intercorrelation between feature channels is usually expected to be learned
from the training data, requiring numerous parameters and careful training. In
contrast, vector-valued neural networks are conceived to process arrays of
vectors and naturally consider the intercorrelation between feature channels.
Consequently, they usually have fewer parameters and often undergo more robust
training than traditional neural networks. This paper aims to present a broad
framework for vector-valued neural networks, referred to as V-nets. In this
context, hypercomplex-valued neural networks are regarded as vector-valued
models with additional algebraic properties. Furthermore, this paper explains
the relationship between vector-valued and traditional neural networks.
Precisely, a vector-valued neural network can be obtained by placing
restrictions on a real-valued model to consider the intercorrelation between
feature channels. Finally, we show how V-nets, including hypercomplex-valued
neural networks, can be implemented in current deep-learning libraries as
real-valued networks. | [
"Marcos Eduardo Valle"
] | 2023-09-14 13:48:16 | http://arxiv.org/abs/2309.07716v1 | http://arxiv.org/pdf/2309.07716v1 | 2309.07716v1 |
Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context | Financial simulators play an important role in enhancing forecasting
accuracy, managing risks, and fostering strategic financial decision-making.
Despite the development of financial market simulation methodologies, existing
frameworks often struggle with adapting to specialized simulation context. We
pinpoint the challenges as i) current financial datasets do not contain context
labels; ii) current techniques are not designed to generate financial data with
context as control, which demands greater precision compared to other
modalities; iii) the inherent difficulties in generating context-aligned,
high-fidelity data given the non-stationary, noisy nature of financial data. To
address these challenges, our contributions are: i) we proposed the Contextual
Market Dataset with market dynamics, stock ticker, and history state as
context, leveraging a market dynamics modeling method that combines linear
regression and Dynamic Time Warping clustering to extract market dynamics; ii)
we present Market-GAN, a novel architecture incorporating a Generative
Adversarial Networks (GAN) for the controllable generation with context, an
autoencoder for learning low-dimension features, and supervisors for knowledge
transfer; iii) we introduce a two-stage training scheme to ensure that
Market-GAN captures the intrinsic market distribution with multiple objectives.
In the pertaining stage, with the use of the autoencoder and supervisors, we
prepare the generator with a better initialization for the adversarial training
stage. We propose a set of holistic evaluation metrics that consider alignment,
fidelity, data usability on downstream tasks, and market facts. We evaluate
Market-GAN with the Dow Jones Industrial Average data from 2000 to 2023 and
showcase superior performance in comparison to 4 state-of-the-art time-series
generative models. | [
"Haochong Xia",
"Shuo Sun",
"Xinrun Wang",
"Bo An"
] | 2023-09-14 13:42:27 | http://arxiv.org/abs/2309.07708v1 | http://arxiv.org/pdf/2309.07708v1 | 2309.07708v1 |
Causal Entropy and Information Gain for Measuring Causal Control | Artificial intelligence models and methods commonly lack causal
interpretability. Despite the advancements in interpretable machine learning
(IML) methods, they frequently assign importance to features which lack causal
influence on the outcome variable. Selecting causally relevant features among
those identified as relevant by these methods, or even before model training,
would offer a solution. Feature selection methods utilizing information
theoretical quantities have been successful in identifying statistically
relevant features. However, the information theoretical quantities they are
based on do not incorporate causality, rendering them unsuitable for such
scenarios. To address this challenge, this article proposes information
theoretical quantities that incorporate the causal structure of the system,
which can be used to evaluate causal importance of features for some given
outcome variable. Specifically, we introduce causal versions of entropy and
mutual information, termed causal entropy and causal information gain, which
are designed to assess how much control a feature provides over the outcome
variable. These newly defined quantities capture changes in the entropy of a
variable resulting from interventions on other variables. Fundamental results
connecting these quantities to the existence of causal effects are derived. The
use of causal information gain in feature selection is demonstrated,
highlighting its superiority over standard mutual information in revealing
which features provide control over a chosen outcome variable. Our
investigation paves the way for the development of methods with improved
interpretability in domains involving causation. | [
"Francisco Nunes Ferreira Quialheiro Simoes",
"Mehdi Dastani",
"Thijs van Ommen"
] | 2023-09-14 13:25:42 | http://arxiv.org/abs/2309.07703v1 | http://arxiv.org/pdf/2309.07703v1 | 2309.07703v1 |
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems | Federated Learning (FL) has emerged as a key approach for distributed machine
learning, enhancing online personalization while ensuring user data privacy.
Instead of sending private data to a central server as in traditional
approaches, FL decentralizes computations: devices train locally and share
updates with a global server. A primary challenge in this setting is achieving
fast and accurate model training - vital for recommendation systems where
delays can compromise user engagement. This paper introduces FedFNN, an
algorithm that accelerates decentralized model training. In FL, only a subset
of users are involved in each training epoch. FedFNN employs supervised
learning to predict weight updates from unsampled users, using updates from the
sampled set. Our evaluations, using real and synthetic data, show: 1. FedFNN
achieves training speeds 5x faster than leading methods, maintaining or
improving accuracy; 2. the algorithm's performance is consistent regardless of
client cluster variations; 3. FedFNN outperforms other methods in scenarios
with limited client availability, converging more quickly. | [
"Francesco Fabbri",
"Xianghang Liu",
"Jack R. McKenzie",
"Bartlomiej Twardowski",
"Tri Kurniawan Wijaya"
] | 2023-09-14 13:18:43 | http://arxiv.org/abs/2309.08635v1 | http://arxiv.org/pdf/2309.08635v1 | 2309.08635v1 |
Tree of Uncertain Thoughts Reasoning for Large Language Models | While the recently introduced Tree of Thoughts (ToT) has heralded
advancements in allowing Large Language Models (LLMs) to reason through
foresight and backtracking for global decision-making, it has overlooked the
inherent local uncertainties in intermediate decision points or "thoughts".
These local uncertainties, intrinsic to LLMs given their potential for diverse
responses, remain a significant concern in the reasoning process. Addressing
this pivotal gap, we introduce the Tree of Uncertain Thoughts (TouT) - a
reasoning framework tailored for LLMs. Our TouT effectively leverages Monte
Carlo Dropout to quantify uncertainty scores associated with LLMs' diverse
local responses at these intermediate steps. By marrying this local uncertainty
quantification with global search algorithms, TouT enhances the model's
precision in response generation. We substantiate our approach with rigorous
experiments on two demanding planning tasks: Game of 24 and Mini Crosswords.
The empirical evidence underscores TouT's superiority over both ToT and
chain-of-thought prompting methods. | [
"Shentong Mo",
"Miao Xin"
] | 2023-09-14 13:14:51 | http://arxiv.org/abs/2309.07694v1 | http://arxiv.org/pdf/2309.07694v1 | 2309.07694v1 |
A DenseNet-based method for decoding auditory spatial attention with EEG | Auditory spatial attention detection (ASAD) aims to decode the attended
spatial location with EEG in a multiple-speaker setting. ASAD methods are
inspired by the brain lateralization of cortical neural responses during the
processing of auditory spatial attention, and show promising performance for
the task of auditory attention decoding (AAD) with neural recordings. In the
previous ASAD methods, the spatial distribution of EEG electrodes is not fully
exploited, which may limit the performance of these methods. In the present
work, by transforming the original EEG channels into a two-dimensional (2D)
spatial topological map, the EEG data is transformed into a three-dimensional
(3D) arrangement containing spatial-temporal information. And then a 3D deep
convolutional neural network (DenseNet-3D) is used to extract temporal and
spatial features of the neural representation for the attended locations. The
results show that the proposed method achieves higher decoding accuracy than
the state-of-the-art (SOTA) method (94.4% compared to XANet's 90.6%) with
1-second decision window for the widely used KULeuven (KUL) dataset, and the
code to implement our work is available on Github:
https://github.com/xuxiran/ASAD_DenseNet | [
"Xiran Xu",
"Bo Wang",
"Yujie Yan",
"Xihong Wu",
"Jing Chen"
] | 2023-09-14 13:07:36 | http://arxiv.org/abs/2309.07690v1 | http://arxiv.org/pdf/2309.07690v1 | 2309.07690v1 |
deepFDEnet: A Novel Neural Network Architecture for Solving Fractional Differential Equations | The primary goal of this research is to propose a novel architecture for a
deep neural network that can solve fractional differential equations
accurately. A Gaussian integration rule and a $L_1$ discretization technique
are used in the proposed design. In each equation, a deep neural network is
used to approximate the unknown function. Three forms of fractional
differential equations have been examined to highlight the method's
versatility: a fractional ordinary differential equation, a fractional order
integrodifferential equation, and a fractional order partial differential
equation. The results show that the proposed architecture solves different
forms of fractional differential equations with excellent precision. | [
"Ali Nosrati Firoozsalari",
"Hassan Dana Mazraeh",
"Alireza Afzal Aghaei",
"Kourosh Parand"
] | 2023-09-14 12:58:40 | http://arxiv.org/abs/2309.07684v1 | http://arxiv.org/pdf/2309.07684v1 | 2309.07684v1 |
Benchmarking machine learning models for quantum state classification | Quantum computing is a growing field where the information is processed by
two-levels quantum states known as qubits. Current physical realizations of
qubits require a careful calibration, composed by different experiments, due to
noise and decoherence phenomena. Among the different characterization
experiments, a crucial step is to develop a model to classify the measured
state by discriminating the ground state from the excited state. In this
proceedings we benchmark multiple classification techniques applied to real
quantum devices. | [
"Edoardo Pedicillo",
"Andrea Pasquale",
"Stefano Carrazza"
] | 2023-09-14 12:45:20 | http://arxiv.org/abs/2309.07679v1 | http://arxiv.org/pdf/2309.07679v1 | 2309.07679v1 |
Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis | Open-ended learning benefits immensely from the use of symbolic methods for
goal representation as they offer ways to structure knowledge for efficient and
transferable learning. However, the existing Hierarchical Reinforcement
Learning (HRL) approaches relying on symbolic reasoning are often limited as
they require a manual goal representation. The challenge in autonomously
discovering a symbolic goal representation is that it must preserve critical
information, such as the environment dynamics. In this paper, we propose a
developmental mechanism for goal discovery via an emergent representation that
abstracts (i.e., groups together) sets of environment states that have similar
roles in the task. We introduce a Feudal HRL algorithm that concurrently learns
both the goal representation and a hierarchical policy. The algorithm uses
symbolic reachability analysis for neural networks to approximate the
transition relation among sets of states and to refine the goal representation.
We evaluate our approach on complex navigation tasks, showing the learned
representation is interpretable, transferrable and results in data efficient
learning. | [
"Mehdi Zadem",
"Sergio Mover",
"Sao Mai Nguyen"
] | 2023-09-14 12:39:26 | http://arxiv.org/abs/2309.07675v1 | http://arxiv.org/pdf/2309.07675v1 | 2309.07675v1 |
Physics-constrained robust learning of open-form PDEs from limited and noisy data | Unveiling the underlying governing equations of nonlinear dynamic systems
remains a significant challenge, especially when encountering noisy
observations and no prior knowledge available. This study proposes R-DISCOVER,
a framework designed to robustly uncover open-form partial differential
equations (PDEs) from limited and noisy data. The framework operates through
two alternating update processes: discovering and embedding. The discovering
phase employs symbolic representation and a reinforcement learning (RL)-guided
hybrid PDE generator to efficiently produce diverse open-form PDEs with tree
structures. A neural network-based predictive model fits the system response
and serves as the reward evaluator for the generated PDEs. PDEs with superior
fits are utilized to iteratively optimize the generator via the RL method and
the best-performing PDE is selected by a parameter-free stability metric. The
embedding phase integrates the initially identified PDE from the discovering
process as a physical constraint into the predictive model for robust training.
The traversal of PDE trees automates the construction of the computational
graph and the embedding process without human intervention. Numerical
experiments demonstrate our framework's capability to uncover governing
equations from nonlinear dynamic systems with limited and highly noisy data and
outperform other physics-informed neural network-based discovery methods. This
work opens new potential for exploring real-world systems with limited
understanding. | [
"Mengge Du",
"Longfeng Nie",
"Siyu Lou",
"Yuntian Chenc",
"Dongxiao Zhang"
] | 2023-09-14 12:34:42 | http://arxiv.org/abs/2309.07672v1 | http://arxiv.org/pdf/2309.07672v1 | 2309.07672v1 |
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation | In this article, we propose an approach for federated domain adaptation, a
setting where distributional shift exists among clients and some have unlabeled
data. The proposed framework, FedDaDiL, tackles the resulting challenge through
dictionary learning of empirical distributions. In our setting, clients'
distributions represent particular domains, and FedDaDiL collectively trains a
federated dictionary of empirical distributions. In particular, we build upon
the Dataset Dictionary Learning framework by designing collaborative
communication protocols and aggregation operations. The chosen protocols keep
clients' data private, thus enhancing overall privacy compared to its
centralized counterpart. We empirically demonstrate that our approach
successfully generates labeled data on the target domain with extensive
experiments on (i) Caltech-Office, (ii) TEP, and (iii) CWRU benchmarks.
Furthermore, we compare our method to its centralized counterpart and other
benchmarks in federated domain adaptation. | [
"Fabiola Espinosa Castellon",
"Eduardo Fernandes Montesuma",
"Fred Ngolè Mboula",
"Aurélien Mayoue",
"Antoine Souloumiac",
"Cédric Gouy-Pallier"
] | 2023-09-14 12:34:22 | http://arxiv.org/abs/2309.07670v1 | http://arxiv.org/pdf/2309.07670v1 | 2309.07670v1 |
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning | In this paper, we consider the intersection of two problems in machine
learning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD).
On the one hand, the first considers adapting multiple heterogeneous labeled
source domains to an unlabeled target domain. On the other hand, the second
attacks the problem of synthesizing a small summary containing all the
information about the datasets. We thus consider a new problem called MSDA-DD.
To solve it, we adapt previous works in the MSDA literature, such as
Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD
method Distribution Matching. We thoroughly experiment with this novel problem
on four benchmarks (Caltech-Office 10, Tennessee-Eastman Process, Continuous
Stirred Tank Reactor, and Case Western Reserve University), where we show that,
even with as little as 1 sample per class, one achieves state-of-the-art
adaptation performance. | [
"Eduardo Fernandes Montesuma",
"Fred Ngolè Mboula",
"Antoine Souloumiac"
] | 2023-09-14 12:29:41 | http://arxiv.org/abs/2309.07666v1 | http://arxiv.org/pdf/2309.07666v1 | 2309.07666v1 |
Dataset Size Dependence of Rate-Distortion Curve and Threshold of Posterior Collapse in Linear VAE | In the Variational Autoencoder (VAE), the variational posterior often aligns
closely with the prior, which is known as posterior collapse and hinders the
quality of representation learning. To mitigate this problem, an adjustable
hyperparameter beta has been introduced in the VAE. This paper presents a
closed-form expression to assess the relationship between the beta in VAE, the
dataset size, the posterior collapse, and the rate-distortion curve by
analyzing a minimal VAE in a high-dimensional limit. These results clarify that
a long plateau in the generalization error emerges with a relatively larger
beta. As the beta increases, the length of the plateau extends and then becomes
infinite beyond a certain beta threshold. This implies that the choice of beta,
unlike the usual regularization parameters, can induce posterior collapse
regardless of the dataset size. Thus, beta is a risky parameter that requires
careful tuning. Furthermore, considering the dataset-size dependence on the
rate-distortion curve, a relatively large dataset is required to obtain a
rate-distortion curve with high rates. Extensive numerical experiments support
our analysis. | [
"Yuma Ichikawa",
"Koji Hukushima"
] | 2023-09-14 12:27:17 | http://arxiv.org/abs/2309.07663v1 | http://arxiv.org/pdf/2309.07663v1 | 2309.07663v1 |
Feature Engineering in Learning-to-Rank for Community Question Answering Task | Community question answering (CQA) forums are Internet-based platforms where
users ask questions about a topic and other expert users try to provide
solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer,
StackExchange exist with a lot of user-generated data. These data are leveraged
in automated CQA ranking systems where similar questions (and answers) are
presented in response to the query of the user. In this work, we empirically
investigate a few aspects of this domain. Firstly, in addition to traditional
features like TF-IDF, BM25 etc., we introduce a BERT-based feature that
captures the semantic similarity between the question and answer. Secondly,
most of the existing research works have focused on features extracted only
from the question part; features extracted from answers have not been explored
extensively. We combine both types of features in a linear fashion. Thirdly,
using our proposed concepts, we conduct an empirical investigation with
different rank-learning algorithms, some of which have not been used so far in
CQA domain. On three standard CQA datasets, our proposed framework achieves
state-of-the-art performance. We also analyze importance of the features we use
in our investigation. This work is expected to guide the practitioners to
select a better set of features for the CQA retrieval task. | [
"Nafis Sajid",
"Md Rashidul Hasan",
"Muhammad Ibrahim"
] | 2023-09-14 11:18:26 | http://arxiv.org/abs/2309.07610v1 | http://arxiv.org/pdf/2309.07610v1 | 2309.07610v1 |
Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation | The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and
challenging task that is important in many practical applications. Classical
model-based approaches to this problem require an accurate model to capture how
robot motions affect the deformation of the DLO. Nowadays, data-driven models
offer the best tradeoff between quality and computation time. This paper
analyzes several learning-based 3D models of the DLO and proposes a new one
based on the Transformer architecture that achieves superior accuracy, even on
the DLOs of different lengths, thanks to the proposed scaling method. Moreover,
we introduce a data augmentation technique, which improves the prediction
performance of almost all considered DLO data-driven models. Thanks to this
technique, even a simple Multilayer Perceptron (MLP) achieves close to
state-of-the-art performance while being significantly faster to evaluate. In
the experiments, we compare the performance of the learning-based 3D models of
the DLO on several challenging datasets quantitatively and demonstrate their
applicability in the task of shaping a DLO. | [
"Piotr Kicki",
"Michał Bidziński",
"Krzysztof Walas"
] | 2023-09-14 11:17:43 | http://arxiv.org/abs/2309.07609v1 | http://arxiv.org/pdf/2309.07609v1 | 2309.07609v1 |
Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec? | Recently sequential recommendations and next-item prediction task has become
increasingly popular in the field of recommender systems. Currently, two
state-of-the-art baselines are Transformer-based models SASRec and BERT4Rec.
Over the past few years, there have been quite a few publications comparing
these two algorithms and proposing new state-of-the-art models. In most of the
publications, BERT4Rec achieves better performance than SASRec. But BERT4Rec
uses cross-entropy over softmax for all items, while SASRec uses negative
sampling and calculates binary cross-entropy loss for one positive and one
negative item. In our work, we show that if both models are trained with the
same loss, which is used by BERT4Rec, then SASRec will significantly outperform
BERT4Rec both in terms of quality and training speed. In addition, we show that
SASRec could be effectively trained with negative sampling and still outperform
BERT4Rec, but the number of negative examples should be much larger than one. | [
"Anton Klenitskiy",
"Alexey Vasilev"
] | 2023-09-14 11:07:10 | http://arxiv.org/abs/2309.07602v1 | http://arxiv.org/pdf/2309.07602v1 | 2309.07602v1 |
Detecting Misinformation with LLM-Predicted Credibility Signals and Weak Supervision | Credibility signals represent a wide range of heuristics that are typically
used by journalists and fact-checkers to assess the veracity of online content.
Automating the task of credibility signal extraction, however, is very
challenging as it requires high-accuracy signal-specific extractors to be
trained, while there are currently no sufficiently large datasets annotated
with all credibility signals. This paper investigates whether large language
models (LLMs) can be prompted effectively with a set of 18 credibility signals
to produce weak labels for each signal. We then aggregate these potentially
noisy labels using weak supervision in order to predict content veracity. We
demonstrate that our approach, which combines zero-shot LLM credibility signal
labeling and weak supervision, outperforms state-of-the-art classifiers on two
misinformation datasets without using any ground-truth labels for training. We
also analyse the contribution of the individual credibility signals towards
predicting content veracity, which provides new valuable insights into their
role in misinformation detection. | [
"João A. Leite",
"Olesya Razuvayevskaya",
"Kalina Bontcheva",
"Carolina Scarton"
] | 2023-09-14 11:06:51 | http://arxiv.org/abs/2309.07601v1 | http://arxiv.org/pdf/2309.07601v1 | 2309.07601v1 |
Statistically Valid Variable Importance Assessment through Conditional Permutations | Variable importance assessment has become a crucial step in machine-learning
applications when using complex learners, such as deep neural networks, on
large-scale data. Removal-based importance assessment is currently the
reference approach, particularly when statistical guarantees are sought to
justify variable inclusion. It is often implemented with variable permutation
schemes. On the flip side, these approaches risk misidentifying unimportant
variables as important in the presence of correlations among covariates. Here
we develop a systematic approach for studying Conditional Permutation
Importance (CPI) that is model agnostic and computationally lean, as well as
reusable benchmarks of state-of-the-art variable importance estimators. We show
theoretically and empirically that $\textit{CPI}$ overcomes the limitations of
standard permutation importance by providing accurate type-I error control.
When used with a deep neural network, $\textit{CPI}$ consistently showed top
accuracy across benchmarks. An empirical benchmark on real-world data analysis
in a large-scale medical dataset showed that $\textit{CPI}$ provides a more
parsimonious selection of statistically significant variables. Our results
suggest that $\textit{CPI}$ can be readily used as drop-in replacement for
permutation-based methods. | [
"Ahmad Chamma",
"Denis A. Engemann",
"Bertrand Thirion"
] | 2023-09-14 10:53:36 | http://arxiv.org/abs/2309.07593v1 | http://arxiv.org/pdf/2309.07593v1 | 2309.07593v1 |
Structure-Preserving Transformers for Sequences of SPD Matrices | In recent years, Transformer-based auto-attention mechanisms have been
successfully applied to the analysis of a variety of context-reliant data
types, from texts to images and beyond, including data from non-Euclidean
geometries. In this paper, we present such a mechanism, designed to classify
sequences of Symmetric Positive Definite matrices while preserving their
Riemannian geometry throughout the analysis. We apply our method to automatic
sleep staging on timeseries of EEG-derived covariance matrices from a standard
dataset, obtaining high levels of stage-wise performance. | [
"Mathieu Seraphim",
"Alexis Lechervy",
"Florian Yger",
"Luc Brun",
"Olivier Etard"
] | 2023-09-14 10:23:43 | http://arxiv.org/abs/2309.07579v3 | http://arxiv.org/pdf/2309.07579v3 | 2309.07579v3 |
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning | We present a novel approach to address the challenge of generalization in
offline reinforcement learning (RL), where the agent learns from a fixed
dataset without any additional interaction with the environment. Specifically,
we aim to improve the agent's ability to generalize to out-of-distribution
goals. To achieve this, we propose to learn a dynamics model and check if it is
equivariant with respect to a fixed type of transformation, namely translations
in the state space. We then use an entropy regularizer to increase the
equivariant set and augment the dataset with the resulting transformed samples.
Finally, we learn a new policy offline based on the augmented dataset, with an
off-the-shelf offline RL algorithm. Our experimental results demonstrate that
our approach can greatly improve the test performance of the policy on the
considered environments. | [
"Cristina Pinneri",
"Sarah Bechtle",
"Markus Wulfmeier",
"Arunkumar Byravan",
"Jingwei Zhang",
"William F. Whitney",
"Martin Riedmiller"
] | 2023-09-14 10:22:33 | http://arxiv.org/abs/2309.07578v1 | http://arxiv.org/pdf/2309.07578v1 | 2309.07578v1 |
Masked Generative Modeling with Enhanced Sampling Scheme | This paper presents a novel sampling scheme for masked non-autoregressive
generative modeling. We identify the limitations of TimeVQVAE, MaskGIT, and
Token-Critic in their sampling processes, and propose Enhanced Sampling Scheme
(ESS) to overcome these limitations. ESS explicitly ensures both sample
diversity and fidelity, and consists of three stages: Naive Iterative Decoding,
Critical Reverse Sampling, and Critical Resampling. ESS starts by sampling a
token set using the naive iterative decoding as proposed in MaskGIT, ensuring
sample diversity. Then, the token set undergoes the critical reverse sampling,
masking tokens leading to unrealistic samples. After that, critical resampling
reconstructs masked tokens until the final sampling step is reached to ensure
high fidelity. Critical resampling uses confidence scores obtained from a
self-Token-Critic to better measure the realism of sampled tokens, while
critical reverse sampling uses the structure of the quantized latent vector
space to discover unrealistic sample paths. We demonstrate significant
performance gains of ESS in both unconditional sampling and class-conditional
sampling using all the 128 datasets in the UCR Time Series archive. | [
"Daesoo Lee",
"Erlend Aune",
"Sara Malacarne"
] | 2023-09-14 09:42:13 | http://arxiv.org/abs/2309.07945v1 | http://arxiv.org/pdf/2309.07945v1 | 2309.07945v1 |
Naturalistic Robot Arm Trajectory Generation via Representation Learning | The integration of manipulator robots in household environments suggests a
need for more predictable and human-like robot motion. This holds especially
true for wheelchair-mounted assistive robots that can support the independence
of people with paralysis. One method of generating naturalistic motion
trajectories is via the imitation of human demonstrators. This paper explores a
self-supervised imitation learning method using an autoregressive
spatio-temporal graph neural network for an assistive drinking task. We address
learning from diverse human motion trajectory data that were captured via
wearable IMU sensors on a human arm as the action-free task demonstrations.
Observed arm motion data from several participants is used to generate natural
and functional drinking motion trajectories for a UR5e robot arm. | [
"Jayjun Lee",
"Adam J. Spiers"
] | 2023-09-14 09:26:03 | http://arxiv.org/abs/2309.07550v1 | http://arxiv.org/pdf/2309.07550v1 | 2309.07550v1 |
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering | This paper aims at the algorithmic/theoretical core of reinforcement learning
(RL) by introducing the novel class of proximal Bellman mappings. These
mappings are defined in reproducing kernel Hilbert spaces (RKHSs), to benefit
from the rich approximation properties and inner product of RKHSs, they are
shown to belong to the powerful Hilbertian family of (firmly) nonexpansive
mappings, regardless of the values of their discount factors, and possess ample
degrees of design freedom to even reproduce attributes of the classical Bellman
mappings and to pave the way for novel RL designs. An approximate
policy-iteration scheme is built on the proposed class of mappings to solve the
problem of selecting online, at every time instance, the "optimal" exponent $p$
in a $p$-norm loss to combat outliers in linear adaptive filtering, without
training data and any knowledge on the statistical properties of the outliers.
Numerical tests on synthetic data showcase the superior performance of the
proposed framework over several non-RL and kernel-based RL schemes. | [
"Yuki Akiyama",
"Konstantinos Slavakis"
] | 2023-09-14 09:20:21 | http://arxiv.org/abs/2309.07548v1 | http://arxiv.org/pdf/2309.07548v1 | 2309.07548v1 |
VerilogEval: Evaluating Large Language Models for Verilog Code Generation | The increasing popularity of large language models (LLMs) has paved the way
for their application in diverse domains. This paper proposes a benchmarking
framework tailored specifically for evaluating LLM performance in the context
of Verilog code generation for hardware design and verification. We present a
comprehensive evaluation dataset consisting of 156 problems from the Verilog
instructional website HDLBits. The evaluation set consists of a diverse set of
Verilog code generation tasks, ranging from simple combinational circuits to
complex finite state machines. The Verilog code completions can be
automatically tested for functional correctness by comparing the transient
simulation outputs of the generated design with a golden solution. We also
demonstrate that the Verilog code generation capability of pretrained language
models could be improved with supervised fine-tuning by bootstrapping with LLM
generated synthetic problem-code pairs. | [
"Mingjie Liu",
"Nathaniel Pinckney",
"Brucek Khailany",
"Haoxing Ren"
] | 2023-09-14 09:15:34 | http://arxiv.org/abs/2309.07544v1 | http://arxiv.org/pdf/2309.07544v1 | 2309.07544v1 |
Adaptive approximation of monotone functions | We study the classical problem of approximating a non-decreasing function $f:
\mathcal{X} \to \mathcal{Y}$ in $L^p(\mu)$ norm by sequentially querying its
values, for known compact real intervals $\mathcal{X}$, $\mathcal{Y}$ and a
known probability measure $\mu$ on $\cX$. For any function~$f$ we characterize
the minimum number of evaluations of $f$ that algorithms need to guarantee an
approximation $\hat{f}$ with an $L^p(\mu)$ error below $\epsilon$ after
stopping. Unlike worst-case results that hold uniformly over all $f$, our
complexity measure is dependent on each specific function $f$. To address this
problem, we introduce GreedyBox, a generalization of an algorithm originally
proposed by Novak (1992) for numerical integration. We prove that GreedyBox
achieves an optimal sample complexity for any function $f$, up to logarithmic
factors. Additionally, we uncover results regarding piecewise-smooth functions.
Perhaps as expected, the $L^p(\mu)$ error of GreedyBox decreases much faster
for piecewise-$C^2$ functions than predicted by the algorithm (without any
knowledge on the smoothness of $f$). A simple modification even achieves
optimal minimax approximation rates for such functions, which we compute
explicitly. In particular, our findings highlight multiple performance gaps
between adaptive and non-adaptive algorithms, smooth and piecewise-smooth
functions, as well as monotone or non-monotone functions. Finally, we provide
numerical experiments to support our theoretical results. | [
"Pierre Gaillard",
"Sébastien Gerchinovitz",
"Étienne de Montbrun"
] | 2023-09-14 08:56:31 | http://arxiv.org/abs/2309.07530v1 | http://arxiv.org/pdf/2309.07530v1 | 2309.07530v1 |
Learning Beyond Similarities: Incorporating Dissimilarities between Positive Pairs in Self-Supervised Time Series Learning | By identifying similarities between successive inputs, Self-Supervised
Learning (SSL) methods for time series analysis have demonstrated their
effectiveness in encoding the inherent static characteristics of temporal data.
However, an exclusive emphasis on similarities might result in representations
that overlook the dynamic attributes critical for modeling cardiovascular
diseases within a confined subject cohort. Introducing Distilled Encoding
Beyond Similarities (DEBS), this paper pioneers an SSL approach that transcends
mere similarities by integrating dissimilarities among positive pairs. The
framework is applied to electrocardiogram (ECG) signals, leading to a notable
enhancement of +10\% in the detection accuracy of Atrial Fibrillation (AFib)
across diverse subjects. DEBS underscores the potential of attaining a more
refined representation by encoding the dynamic characteristics of time series
data, tapping into dissimilarities during the optimization process. Broadly,
the strategy delineated in this study holds the promise of unearthing novel
avenues for advancing SSL methodologies tailored to temporal data. | [
"Adrian Atienza",
"Jakob Bardram",
"Sadasivan Puthusserypady"
] | 2023-09-14 08:49:35 | http://arxiv.org/abs/2309.07526v1 | http://arxiv.org/pdf/2309.07526v1 | 2309.07526v1 |
Massively-Parallel Heat Map Sorting and Applications To Explainable Clustering | Given a set of points labeled with $k$ labels, we introduce the heat map
sorting problem as reordering and merging the points and dimensions while
preserving the clusters (labels). A cluster is preserved if it remains
connected, i.e., if it is not split into several clusters and no two clusters
are merged.
We prove the problem is NP-hard and we give a fixed-parameter algorithm with
a constant number of rounds in the massively parallel computation model, where
each machine has a sublinear memory and the total memory of the machines is
linear. We give an approximation algorithm for a NP-hard special case of the
problem. We empirically compare our algorithm with k-means and density-based
clustering (DBSCAN) using a dimensionality reduction via locality-sensitive
hashing on several directed and undirected graphs of email and computer
networks. | [
"Sepideh Aghamolaei",
"Mohammad Ghodsi"
] | 2023-09-14 07:53:52 | http://arxiv.org/abs/2309.07486v1 | http://arxiv.org/pdf/2309.07486v1 | 2309.07486v1 |
Improved Auto-Encoding using Deterministic Projected Belief Networks | In this paper, we exploit the unique properties of a deterministic projected
belief network (D-PBN) to take full advantage of trainable compound activation
functions (TCAs). A D-PBN is a type of auto-encoder that operates by "backing
up" through a feed-forward neural network. TCAs are activation functions with
complex monotonic-increasing shapes that change the distribution of the data so
that the linear transformation that follows is more effective. Because a D-PBN
operates by "backing up", the TCAs are inverted in the reconstruction process,
restoring the original distribution of the data, thus taking advantage of a
given TCA in both analysis and reconstruction. In this paper, we show that a
D-PBN auto-encoder with TCAs can significantly out-perform standard
auto-encoders including variational auto-encoders. | [
"Paul M Baggenstoss"
] | 2023-09-14 07:40:10 | http://arxiv.org/abs/2309.07481v1 | http://arxiv.org/pdf/2309.07481v1 | 2309.07481v1 |
Direct Text to Speech Translation System using Acoustic Units | This paper proposes a direct text to speech translation system using discrete
acoustic units. This framework employs text in different source languages as
input to generate speech in the target language without the need for text
transcriptions in this language. Motivated by the success of acoustic units in
previous works for direct speech to speech translation systems, we use the same
pipeline to extract the acoustic units using a speech encoder combined with a
clustering algorithm. Once units are obtained, an encoder-decoder architecture
is trained to predict them. Then a vocoder generates speech from units. Our
approach for direct text to speech translation was tested on the new CVSS
corpus with two different text mBART models employed as initialisation. The
systems presented report competitive performance for most of the language pairs
evaluated. Besides, results show a remarkable improvement when initialising our
proposed architecture with a model pre-trained with more languages. | [
"Victoria Mingote",
"Pablo Gimeno",
"Luis Vicente",
"Sameer Khurana",
"Antoine Laurent",
"Jarod Duret"
] | 2023-09-14 07:35:14 | http://arxiv.org/abs/2309.07478v1 | http://arxiv.org/pdf/2309.07478v1 | 2309.07478v1 |
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection | The widespread integration of Internet of Things (IoT) devices across all
facets of life has ushered in an era of interconnectedness, creating new
avenues for cybersecurity challenges and underscoring the need for robust
intrusion detection systems. However, traditional security systems are designed
with a closed-world perspective and often face challenges in dealing with the
ever-evolving threat landscape, where new and unfamiliar attacks are constantly
emerging. In this paper, we introduce a framework aimed at mitigating the open
set recognition (OSR) problem in the realm of Network Intrusion Detection
Systems (NIDS) tailored for IoT environments. Our framework capitalizes on
image-based representations of packet-level data, extracting spatial and
temporal patterns from network traffic. Additionally, we integrate stacking and
sub-clustering techniques, enabling the identification of unknown attacks by
effectively modeling the complex and diverse nature of benign behavior. The
empirical results prominently underscore the framework's efficacy, boasting an
impressive 88\% detection rate for previously unseen attacks when compared
against existing approaches and recent advancements. Future work will perform
extensive experimentation across various openness levels and attack scenarios,
further strengthening the adaptability and performance of our proposed solution
in safeguarding IoT environments. | [
"Yasir Ali Farrukh",
"Syed Wali",
"Irfan Khan",
"Nathaniel D. Bastian"
] | 2023-09-14 06:41:45 | http://arxiv.org/abs/2309.07461v2 | http://arxiv.org/pdf/2309.07461v2 | 2309.07461v2 |
SC-MAD: Mixtures of Higher-order Networks for Data Augmentation | The myriad complex systems with multiway interactions motivate the extension
of graph-based pairwise connections to higher-order relations. In particular,
the simplicial complex has inspired generalizations of graph neural networks
(GNNs) to simplicial complex-based models. Learning on such systems requires
large amounts of data, which can be expensive or impossible to obtain. We
propose data augmentation of simplicial complexes through both linear and
nonlinear mixup mechanisms that return mixtures of existing labeled samples. In
addition to traditional pairwise mixup, we present a convex clustering mixup
approach for a data-driven relationship among several simplicial complexes. We
theoretically demonstrate that the resultant synthetic simplicial complexes
interpolate among existing data with respect to homomorphism densities. Our
method is demonstrated on both synthetic and real-world datasets for simplicial
complex classification. | [
"Madeline Navarro",
"Santiago Segarra"
] | 2023-09-14 06:25:39 | http://arxiv.org/abs/2309.07453v1 | http://arxiv.org/pdf/2309.07453v1 | 2309.07453v1 |
Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network? | A rising trend in theoretical deep learning is to understand why deep
learning works through Neural Tangent Kernel (NTK) [jgh18], a kernel method
that is equivalent to using gradient descent to train a multi-layer
infinitely-wide neural network. NTK is a major step forward in the theoretical
deep learning because it allows researchers to use traditional mathematical
tools to analyze properties of deep neural networks and to explain various
neural network techniques from a theoretical view. A natural extension of NTK
on graph learning is \textit{Graph Neural Tangent Kernel (GNTK)}, and
researchers have already provide GNTK formulation for graph-level regression
and show empirically that this kernel method can achieve similar accuracy as
GNNs on various bioinformatics datasets [dhs+19]. The remaining question now is
whether solving GNTK regression is equivalent to training an infinite-wide
multi-layer GNN using gradient descent. In this paper, we provide three new
theoretical results. First, we formally prove this equivalence for graph-level
regression. Second, we present the first GNTK formulation for node-level
regression. Finally, we prove the equivalence for node-level regression. | [
"Lianke Qin",
"Zhao Song",
"Baocheng Sun"
] | 2023-09-14 06:24:33 | http://arxiv.org/abs/2309.07452v1 | http://arxiv.org/pdf/2309.07452v1 | 2309.07452v1 |
TensorFlow Chaotic Prediction and Blow Up | Predicting the dynamics of chaotic systems is one of the most challenging
tasks for neural networks, and machine learning in general. Here we aim to
predict the spatiotemporal chaotic dynamics of a high-dimensional non-linear
system. In our attempt we use the TensorFlow library, representing the state of
the art for deep neural networks training and prediction. While our results are
encouraging, and show that the dynamics of the considered system can be
predicted for short time, we also indirectly discovered an unexpected and
undesirable behavior of the TensorFlow library. More specifically, the longer
term prediction of the system's chaotic behavior quickly deteriorates and blows
up due to the nondeterministic behavior of the TensorFlow library. Here we
provide numerical evidence of the short time prediction ability, and of the
longer term predictability blow up. | [
"M. Andrecut"
] | 2023-09-14 06:22:48 | http://arxiv.org/abs/2309.07450v1 | http://arxiv.org/pdf/2309.07450v1 | 2309.07450v1 |
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection | The effectiveness of network intrusion detection systems, predominantly based
on machine learning, are highly influenced by the dataset they are trained on.
Ensuring an accurate reflection of the multifaceted nature of benign and
malicious traffic in these datasets is essential for creating models capable of
recognizing and responding to a wide array of intrusion patterns. However,
existing datasets often fall short, lacking the necessary diversity and
alignment with the contemporary network environment, thereby limiting the
effectiveness of intrusion detection. This paper introduces TII-SSRC-23, a
novel and comprehensive dataset designed to overcome these challenges.
Comprising a diverse range of traffic types and subtypes, our dataset is a
robust and versatile tool for the research community. Additionally, we conduct
a feature importance analysis, providing vital insights into critical features
for intrusion detection tasks. Through extensive experimentation, we also
establish firm baselines for supervised and unsupervised intrusion detection
methodologies using our dataset, further contributing to the advancement and
adaptability of intrusion detection models in the rapidly changing landscape of
network security. Our dataset is available at
https://kaggle.com/datasets/daniaherzalla/tii-ssrc-23. | [
"Dania Herzalla",
"Willian T. Lunardi",
"Martin Andreoni Lopez"
] | 2023-09-14 05:23:36 | http://arxiv.org/abs/2310.10661v1 | http://arxiv.org/pdf/2310.10661v1 | 2310.10661v1 |
Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023 | Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive,
emotional, and behavioral changes. Symptoms of SZ include hallucinations,
illusions, delusions, lack of motivation, and difficulties in concentration.
Diagnosing SZ involves employing various tools, including clinical interviews,
physical examinations, psychological evaluations, the Diagnostic and
Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques.
Electroencephalography (EEG) recording is a significant functional neuroimaging
modality that provides valuable insights into brain function during SZ.
However, EEG signal analysis poses challenges for neurologists and scientists
due to the presence of artifacts, long-term recordings, and the utilization of
multiple channels. To address these challenges, researchers have introduced
artificial intelligence (AI) techniques, encompassing conventional machine
learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This
study reviews papers focused on SZ diagnosis utilizing EEG signals and AI
methods. The introduction section provides a comprehensive explanation of SZ
diagnosis methods and intervention techniques. Subsequently, review papers in
this field are discussed, followed by an introduction to the AI methods
employed for SZ diagnosis and a summary of relevant papers presented in tabular
form. Additionally, this study reports on the most significant challenges
encountered in SZ diagnosis, as identified through a review of papers in this
field. Future directions to overcome these challenges are also addressed. The
discussion section examines the specific details of each paper, culminating in
the presentation of conclusions and findings. | [
"Mahboobeh Jafari",
"Delaram Sadeghi",
"Afshin Shoeibi",
"Hamid Alinejad-Rokny",
"Amin Beheshti",
"David López García",
"Zhaolin Chen",
"U. Rajendra Acharya",
"Juan M. Gorriz"
] | 2023-09-14 04:55:34 | http://arxiv.org/abs/2309.12202v1 | http://arxiv.org/pdf/2309.12202v1 | 2309.12202v1 |
A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time | Large language models (LLMs) have played a pivotal role in revolutionizing
various facets of our daily existence. Solving attention regression is a
fundamental task in optimizing LLMs. In this work, we focus on giving a
provable guarantee for the one-layer attention network objective function
$L(X,Y) = \sum_{j_0 = 1}^n \sum_{i_0 = 1}^d ( \langle \langle \exp(
\mathsf{A}_{j_0} x ) , {\bf 1}_n \rangle^{-1} \exp( \mathsf{A}_{j_0} x ), A_{3}
Y_{*,i_0} \rangle - b_{j_0,i_0} )^2$. Here $\mathsf{A} \in \mathbb{R}^{n^2
\times d^2}$ is Kronecker product between $A_1 \in \mathbb{R}^{n \times d}$ and
$A_2 \in \mathbb{R}^{n \times d}$. $A_3$ is a matrix in $\mathbb{R}^{n \times
d}$, $\mathsf{A}_{j_0} \in \mathbb{R}^{n \times d^2}$ is the $j_0$-th block of
$\mathsf{A}$. The $X, Y \in \mathbb{R}^{d \times d}$ are variables we want to
learn. $B \in \mathbb{R}^{n \times d}$ and $b_{j_0,i_0} \in \mathbb{R}$ is one
entry at $j_0$-th row and $i_0$-th column of $B$, $Y_{*,i_0} \in \mathbb{R}^d$
is the $i_0$-column vector of $Y$, and $x \in \mathbb{R}^{d^2}$ is the
vectorization of $X$.
In a multi-layer LLM network, the matrix $B \in \mathbb{R}^{n \times d}$ can
be viewed as the output of a layer, and $A_1= A_2 = A_3 \in \mathbb{R}^{n
\times d}$ can be viewed as the input of a layer. The matrix version of $x$ can
be viewed as $QK^\top$ and $Y$ can be viewed as $V$. We provide an iterative
greedy algorithm to train loss function $L(X,Y)$ up $\epsilon$ that runs in
$\widetilde{O}( ({\cal T}_{\mathrm{mat}}(n,n,d) + {\cal
T}_{\mathrm{mat}}(n,d,d) + d^{2\omega}) \log(1/\epsilon) )$ time. Here ${\cal
T}_{\mathrm{mat}}(a,b,c)$ denotes the time of multiplying $a \times b$ matrix
another $b \times c$ matrix, and $\omega\approx 2.37$ denotes the exponent of
matrix multiplication. | [
"Yeqi Gao",
"Zhao Song",
"Weixin Wang",
"Junze Yin"
] | 2023-09-14 04:23:40 | http://arxiv.org/abs/2309.07418v1 | http://arxiv.org/pdf/2309.07418v1 | 2309.07418v1 |
Advancing Regular Language Reasoning in Linear Recurrent Neural Networks | In recent studies, linear recurrent neural networks (LRNNs) have achieved
Transformer-level performance in natural language modeling and long-range
modeling while offering rapid parallel training and constant inference costs.
With the resurged interest in LRNNs, we study whether they can learn the hidden
rules in training sequences, such as the grammatical structures of regular
language. We theoretically analyze some existing LRNNs and discover their
limitations on regular language. Motivated by the analysis, we propose a new
LRNN equipped with a block-diagonal and input-dependent transition matrix.
Experiments suggest that the proposed model is the only LRNN that can perform
length extrapolation on regular language tasks such as Sum, Even Pair, and
Modular Arithmetic. | [
"Ting-Han Fan",
"Ta-Chung Chi",
"Alexander I. Rudnicky"
] | 2023-09-14 03:36:01 | http://arxiv.org/abs/2309.07412v1 | http://arxiv.org/pdf/2309.07412v1 | 2309.07412v1 |
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy | Label scarcity in a graph is frequently encountered in real-world
applications due to the high cost of data labeling. To this end,
semi-supervised domain adaptation (SSDA) on graphs aims to leverage the
knowledge of a labeled source graph to aid in node classification on a target
graph with limited labels. SSDA tasks need to overcome the domain gap between
the source and target graphs. However, to date, this challenging research
problem has yet to be formally considered by the existing approaches designed
for cross-graph node classification. To tackle the SSDA problem on graphs, a
novel method called SemiGCL is proposed, which benefits from graph contrastive
learning and minimax entropy training. SemiGCL generates informative node
representations by contrasting the representations learned from a graph's local
and global views. Additionally, SemiGCL is adversarially optimized with the
entropy loss of unlabeled target nodes to reduce domain divergence.
Experimental results on benchmark datasets demonstrate that SemiGCL outperforms
the state-of-the-art baselines on the SSDA tasks. | [
"Jiaren Xiao",
"Quanyu Dai",
"Xiao Shen",
"Xiaochen Xie",
"Jing Dai",
"James Lam",
"Ka-Wai Kwok"
] | 2023-09-14 03:15:57 | http://arxiv.org/abs/2309.07402v1 | http://arxiv.org/pdf/2309.07402v1 | 2309.07402v1 |
Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks | We propose a decoder-only language model, \textit{VoxtLM}, that can perform
four tasks: speech recognition, speech synthesis, text generation, and speech
continuation. VoxtLM integrates text vocabulary with discrete speech tokens
from self-supervised speech features and uses special tokens to enable
multitask learning. Compared to a single-task model, VoxtLM exhibits a
significant improvement in speech synthesis, with improvements in both speech
intelligibility from 28.9 to 5.6 and objective quality from 2.68 to 3.90.
VoxtLM also improves speech generation and speech recognition performance over
the single-task counterpart. VoxtLM is trained with publicly available data and
training recipes and model checkpoints will be open-sourced to make fully
reproducible work. | [
"Soumi Maiti",
"Yifan Peng",
"Shukjae Choi",
"Jee-weon Jung",
"Xuankai Chang",
"Shinji Watanabe"
] | 2023-09-14 03:13:18 | http://arxiv.org/abs/2309.07937v2 | http://arxiv.org/pdf/2309.07937v2 | 2309.07937v2 |
Semantic Adversarial Attacks via Diffusion Models | Traditional adversarial attacks concentrate on manipulating clean examples in
the pixel space by adding adversarial perturbations. By contrast, semantic
adversarial attacks focus on changing semantic attributes of clean examples,
such as color, context, and features, which are more feasible in the real
world. In this paper, we propose a framework to quickly generate a semantic
adversarial attack by leveraging recent diffusion models since semantic
information is included in the latent space of well-trained diffusion models.
Then there are two variants of this framework: 1) the Semantic Transformation
(ST) approach fine-tunes the latent space of the generated image and/or the
diffusion model itself; 2) the Latent Masking (LM) approach masks the latent
space with another target image and local backpropagation-based interpretation
methods. Additionally, the ST approach can be applied in either white-box or
black-box settings. Extensive experiments are conducted on CelebA-HQ and AFHQ
datasets, and our framework demonstrates great fidelity, generalizability, and
transferability compared to other baselines. Our approaches achieve
approximately 100% attack success rate in multiple settings with the best FID
as 36.61. Code is available at
https://github.com/steven202/semantic_adv_via_dm. | [
"Chenan Wang",
"Jinhao Duan",
"Chaowei Xiao",
"Edward Kim",
"Matthew Stamm",
"Kaidi Xu"
] | 2023-09-14 02:57:48 | http://arxiv.org/abs/2309.07398v1 | http://arxiv.org/pdf/2309.07398v1 | 2309.07398v1 |
EnCodecMAE: Leveraging neural codecs for universal audio representation learning | The goal of universal audio representation learning is to obtain foundational
models that can be used for a variety of downstream tasks involving speech,
music or environmental sounds. To approach this problem, methods inspired by
self-supervised models from NLP, like BERT, are often used and adapted to
audio. These models rely on the discrete nature of text, hence adopting this
type of approach for audio processing requires either a change in the learning
objective or mapping the audio signal to a set of discrete classes. In this
work, we explore the use of EnCodec, a neural audio codec, to generate discrete
targets for learning an universal audio model based on a masked autoencoder
(MAE). We evaluate this approach, which we call EncodecMAE, on a wide range of
audio tasks spanning speech, music and environmental sounds, achieving
performances comparable or better than leading audio representation models. | [
"Leonardo Pepino",
"Pablo Riera",
"Luciana Ferrer"
] | 2023-09-14 02:21:53 | http://arxiv.org/abs/2309.07391v1 | http://arxiv.org/pdf/2309.07391v1 | 2309.07391v1 |
Rates of Convergence in Certain Native Spaces of Approximations used in Reinforcement Learning | This paper studies convergence rates for some value function approximations
that arise in a collection of reproducing kernel Hilbert spaces (RKHS)
$H(\Omega)$. By casting an optimal control problem in a specific class of
native spaces, strong rates of convergence are derived for the operator
equation that enables offline approximations that appear in policy iteration.
Explicit upper bounds on error in value function approximations are derived in
terms of power function $\Pwr_{H,N}$ for the space of finite dimensional
approximants $H_N$ in the native space $H(\Omega)$. These bounds are geometric
in nature and refine some well-known, now classical results concerning
convergence of approximations of value functions. | [
"Ali Bouland",
"Shengyuan Niu",
"Sai Tej Paruchuri",
"Andrew Kurdila",
"John Burns",
"Eugenio Schuster"
] | 2023-09-14 02:02:08 | http://arxiv.org/abs/2309.07383v2 | http://arxiv.org/pdf/2309.07383v2 | 2309.07383v2 |
Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate Optimization Problems | In this paper, we introduce a new heuristics for global optimization in
scenarios where extensive evaluations of the cost function are expensive,
inaccessible, or even prohibitive. The method, which we call
Landscape-Sketch-and-Step (LSS), combines Machine Learning, Stochastic
Optimization, and Reinforcement Learning techniques, relying on historical
information from previously sampled points to make judicious choices of
parameter values where the cost function should be evaluated at. Unlike
optimization by Replica Exchange Monte Carlo methods, the number of evaluations
of the cost function required in this approach is comparable to that used by
Simulated Annealing, quality that is especially important in contexts like
high-throughput computing or high-performance computing tasks, where
evaluations are either computationally expensive or take a long time to be
performed. The method also differs from standard Surrogate Optimization
techniques, for it does not construct a surrogate model that aims at
approximating or reconstructing the objective function. We illustrate our
method by applying it to low dimensional optimization problems (dimensions 1,
2, 4, and 8) that mimick known difficulties of minimization on rugged energy
landscapes often seen in Condensed Matter Physics, where cost functions are
rugged and plagued with local minima. When compared to classical Simulated
Annealing, the LSS shows an effective acceleration of the optimization process. | [
"Rafael Monteiro",
"Kartik Sau"
] | 2023-09-14 01:53:45 | http://arxiv.org/abs/2309.07936v3 | http://arxiv.org/pdf/2309.07936v3 | 2309.07936v3 |
Beta quantile regression for robust estimation of uncertainty in the presence of outliers | Quantile Regression (QR) can be used to estimate aleatoric uncertainty in
deep neural networks and can generate prediction intervals. Quantifying
uncertainty is particularly important in critical applications such as clinical
diagnosis, where a realistic assessment of uncertainty is essential in
determining disease status and planning the appropriate treatment. The most
common application of quantile regression models is in cases where the
parametric likelihood cannot be specified. Although quantile regression is
quite robust to outlier response observations, it can be sensitive to outlier
covariate observations (features). Outlier features can compromise the
performance of deep learning regression problems such as style translation,
image reconstruction, and deep anomaly detection, potentially leading to
misleading conclusions. To address this problem, we propose a robust solution
for quantile regression that incorporates concepts from robust divergence. We
compare the performance of our proposed method with (i) least trimmed quantile
regression and (ii) robust regression based on the regularization of
case-specific parameters in a simple real dataset in the presence of outlier.
These methods have not been applied in a deep learning framework. We also
demonstrate the applicability of the proposed method by applying it to a
medical imaging translation task using diffusion models. | [
"Haleh Akrami",
"Omar Zamzam",
"Anand Joshi",
"Sergul Aydore",
"Richard Leahy"
] | 2023-09-14 01:18:57 | http://arxiv.org/abs/2309.07374v1 | http://arxiv.org/pdf/2309.07374v1 | 2309.07374v1 |
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing | We consider a decentralized formulation of the active hypothesis testing
(AHT) problem, where multiple agents gather noisy observations from the
environment with the purpose of identifying the correct hypothesis. At each
time step, agents have the option to select a sampling action. These different
actions result in observations drawn from various distributions, each
associated with a specific hypothesis. The agents collaborate to accomplish the
task, where message exchanges between agents are allowed over a rate-limited
communications channel. The objective is to devise a multi-agent policy that
minimizes the Bayes risk. This risk comprises both the cost of sampling and the
joint terminal cost incurred by the agents upon making a hypothesis
declaration. Deriving optimal structured policies for AHT problems is generally
mathematically intractable, even in the context of a single agent. As a result,
recent efforts have turned to deep learning methodologies to address these
problems, which have exhibited significant success in single-agent learning
scenarios. In this paper, we tackle the multi-agent AHT formulation by
introducing a novel algorithm rooted in the framework of deep multi-agent
reinforcement learning. This algorithm, named Multi-Agent Reinforcement
Learning for AHT (MARLA), operates at each time step by having each agent map
its state to an action (sampling rule or stopping rule) using a trained deep
neural network with the goal of minimizing the Bayes risk. We present a
comprehensive set of experimental results that effectively showcase the agents'
ability to learn collaborative strategies and enhance performance using MARLA.
Furthermore, we demonstrate the superiority of MARLA over single-agent learning
approaches. Finally, we provide an open-source implementation of the MARLA
framework, for the benefit of researchers and developers in related domains. | [
"Hadar Szostak",
"Kobi Cohen"
] | 2023-09-14 01:18:04 | http://arxiv.org/abs/2309.08477v1 | http://arxiv.org/pdf/2309.08477v1 | 2309.08477v1 |
The kernel-balanced equation for deep neural networks | Deep neural networks have shown many fruitful applications in this decade. A
network can get the generalized function through training with a finite
dataset. The degree of generalization is a realization of the proximity scale
in the data space. Specifically, the scale is not clear if the dataset is
complicated. Here we consider a network for the distribution estimation of the
dataset. We show the estimation is unstable and the instability depends on the
data density and training duration. We derive the kernel-balanced equation,
which gives a short phenomenological description of the solution. The equation
tells us the reason for the instability and the mechanism of the scale. The
network outputs a local average of the dataset as a prediction and the scale of
averaging is determined along the equation. The scale gradually decreases along
training and finally results in instability in our case. | [
"Kenichi Nakazato"
] | 2023-09-14 01:00:05 | http://arxiv.org/abs/2309.07367v1 | http://arxiv.org/pdf/2309.07367v1 | 2309.07367v1 |
Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing | Key challenges in running a retail business include how to select products to
present to consumers (the assortment problem), and how to price products (the
pricing problem) to maximize revenue or profit. Instead of considering these
problems in isolation, we propose a joint approach to assortment-pricing based
on contextual bandits. Our model is doubly high-dimensional, in that both
context vectors and actions are allowed to take values in high-dimensional
spaces. In order to circumvent the curse of dimensionality, we propose a simple
yet flexible model that captures the interactions between covariates and
actions via a (near) low-rank representation matrix. The resulting class of
models is reasonably expressive while remaining interpretable through latent
factors, and includes various structured linear bandit and pricing models as
particular cases. We propose a computationally tractable procedure that
combines an exploration/exploitation protocol with an efficient low-rank matrix
estimator, and we prove bounds on its regret. Simulation results show that this
method has lower regret than state-of-the-art methods applied to various
standard bandit and pricing models. Real-world case studies on the
assortment-pricing problem, from an industry-leading instant noodles company to
an emerging beauty start-up, underscore the gains achievable using our method.
In each case, we show at least three-fold gains in revenue or profit by our
bandit method, as well as the interpretability of the latent factor models that
are learned. | [
"Junhui Cai",
"Ran Chen",
"Martin J. Wainwright",
"Linda Zhao"
] | 2023-09-14 00:45:36 | http://arxiv.org/abs/2309.08634v1 | http://arxiv.org/pdf/2309.08634v1 | 2309.08634v1 |
Hodge-Aware Contrastive Learning | Simplicial complexes prove effective in modeling data with multiway
dependencies, such as data defined along the edges of networks or within other
higher-order structures. Their spectrum can be decomposed into three
interpretable subspaces via the Hodge decomposition, resulting foundational in
numerous applications. We leverage this decomposition to develop a contrastive
self-supervised learning approach for processing simplicial data and generating
embeddings that encapsulate specific spectral information.Specifically, we
encode the pertinent data invariances through simplicial neural networks and
devise augmentations that yield positive contrastive examples with suitable
spectral properties for downstream tasks. Additionally, we reweight the
significance of negative examples in the contrastive loss, considering the
similarity of their Hodge components to the anchor. By encouraging a stronger
separation among less similar instances, we obtain an embedding space that
reflects the spectral properties of the data. The numerical results on two
standard edge flow classification tasks show a superior performance even when
compared to supervised learning techniques. Our findings underscore the
importance of adopting a spectral perspective for contrastive learning with
higher-order data. | [
"Alexander Möllers",
"Alexander Immer",
"Vincent Fortuin",
"Elvin Isufi"
] | 2023-09-14 00:40:07 | http://arxiv.org/abs/2309.07364v1 | http://arxiv.org/pdf/2309.07364v1 | 2309.07364v1 |
Tackling the dimensions in imaging genetics with CLUB-PLS | A major challenge in imaging genetics and similar fields is to link
high-dimensional data in one domain, e.g., genetic data, to high dimensional
data in a second domain, e.g., brain imaging data. The standard approach in the
area are mass univariate analyses across genetic factors and imaging
phenotypes. That entails executing one genome-wide association study (GWAS) for
each pre-defined imaging measure. Although this approach has been tremendously
successful, one shortcoming is that phenotypes must be pre-defined.
Consequently, effects that are not confined to pre-selected regions of interest
or that reflect larger brain-wide patterns can easily be missed. In this work
we introduce a Partial Least Squares (PLS)-based framework, which we term
Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in
both domains as well as with large sample sizes. One key factor of the
framework is to use cluster bootstrap to provide robust statistics for single
input features in both domains. We applied CLUB-PLS to investigating the
genetic basis of surface area and cortical thickness in a sample of 33,000
subjects from the UK Biobank. We found 107 genome-wide significant
locus-phenotype pairs that are linked to 386 different genes. We found that a
vast majority of these loci could be technically validated at a high rate:
using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85
locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold. | [
"Andre Altmann",
"Ana C Lawry Aguila",
"Neda Jahanshad",
"Paul M Thompson",
"Marco Lorenzi"
] | 2023-09-13 23:27:45 | http://arxiv.org/abs/2309.07352v2 | http://arxiv.org/pdf/2309.07352v2 | 2309.07352v2 |
Efficient Learning of PDEs via Taylor Expansion and Sparse Decomposition into Value and Fourier Domains | Accelerating the learning of Partial Differential Equations (PDEs) from
experimental data will speed up the pace of scientific discovery. Previous
randomized algorithms exploit sparsity in PDE updates for acceleration. However
such methods are applicable to a limited class of decomposable PDEs, which have
sparse features in the value domain. We propose Reel, which accelerates the
learning of PDEs via random projection and has much broader applicability. Reel
exploits the sparsity by decomposing dense updates into sparse ones in both the
value and frequency domains. This decomposition enables efficient learning when
the source of the updates consists of gradually changing terms across large
areas (sparse in the frequency domain) in addition to a few rapid updates
concentrated in a small set of "interfacial" regions (sparse in the value
domain). Random projection is then applied to compress the sparse signals for
learning. To expand the model applicability, Taylor series expansion is used in
Reel to approximate the nonlinear PDE updates with polynomials in the
decomposable form. Theoretically, we derive a constant factor approximation
between the projected loss function and the original one with poly-logarithmic
number of projected dimensions. Experimentally, we provide empirical evidence
that our proposed Reel can lead to faster learning of PDE models (70-98%
reduction in training time when the data is compressed to 1% of its original
size) with comparable quality as the non-compressed models. | [
"Md Nasim",
"Yexiang Xue"
] | 2023-09-13 22:48:30 | http://arxiv.org/abs/2309.07344v1 | http://arxiv.org/pdf/2309.07344v1 | 2309.07344v1 |
Efficient quantum recurrent reinforcement learning via quantum reservoir computing | Quantum reinforcement learning (QRL) has emerged as a framework to solve
sequential decision-making tasks, showcasing empirical quantum advantages. A
notable development is through quantum recurrent neural networks (QRNNs) for
memory-intensive tasks such as partially observable environments. However, QRL
models incorporating QRNN encounter challenges such as inefficient training of
QRL with QRNN, given that the computation of gradients in QRNN is both
computationally expensive and time-consuming. This work presents a novel
approach to address this challenge by constructing QRL agents utilizing
QRNN-based reservoirs, specifically employing quantum long short-term memory
(QLSTM). QLSTM parameters are randomly initialized and fixed without training.
The model is trained using the asynchronous advantage actor-aritic (A3C)
algorithm. Through numerical simulations, we validate the efficacy of our
QLSTM-Reservoir RL framework. Its performance is assessed on standard
benchmarks, demonstrating comparable results to a fully trained QLSTM RL model
with identical architecture and training settings. | [
"Samuel Yen-Chi Chen"
] | 2023-09-13 22:18:38 | http://arxiv.org/abs/2309.07339v1 | http://arxiv.org/pdf/2309.07339v1 | 2309.07339v1 |
Reliability-based cleaning of noisy training labels with inductive conformal prediction in multi-modal biomedical data mining | Accurately labeling biomedical data presents a challenge. Traditional
semi-supervised learning methods often under-utilize available unlabeled data.
To address this, we propose a novel reliability-based training data cleaning
method employing inductive conformal prediction (ICP). This method capitalizes
on a small set of accurately labeled training data and leverages ICP-calculated
reliability metrics to rectify mislabeled data and outliers within vast
quantities of noisy training data. The efficacy of the method is validated
across three classification tasks within distinct modalities: filtering
drug-induced-liver-injury (DILI) literature with title and abstract, predicting
ICU admission of COVID-19 patients through CT radiomics and electronic health
records, and subtyping breast cancer using RNA-sequencing data. Varying levels
of noise to the training labels were introduced through label permutation.
Results show significant enhancements in classification performance: accuracy
enhancement in 86 out of 96 DILI experiments (up to 11.4%), AUROC and AUPRC
enhancements in all 48 COVID-19 experiments (up to 23.8% and 69.8%), and
accuracy and macro-average F1 score improvements in 47 out of 48 RNA-sequencing
experiments (up to 74.6% and 89.0%). Our method offers the potential to
substantially boost classification performance in multi-modal biomedical
machine learning tasks. Importantly, it accomplishes this without necessitating
an excessive volume of meticulously curated training data. | [
"Xianghao Zhan",
"Qinmei Xu",
"Yuanning Zheng",
"Guangming Lu",
"Olivier Gevaert"
] | 2023-09-13 22:04:50 | http://arxiv.org/abs/2309.07332v1 | http://arxiv.org/pdf/2309.07332v1 | 2309.07332v1 |
Racing Control Variable Genetic Programming for Symbolic Regression | Symbolic regression, as one of the most crucial tasks in AI for science,
discovers governing equations from experimental data. Popular approaches based
on genetic programming, Monte Carlo tree search, or deep reinforcement learning
learn symbolic regression from a fixed dataset. They require massive datasets
and long training time especially when learning complex equations involving
many variables. Recently, Control Variable Genetic Programming (CVGP) has been
introduced which accelerates the regression process by discovering equations
from designed control variable experiments. However, the set of experiments is
fixed a-priori in CVGP and we observe that sub-optimal selection of experiment
schedules delay the discovery process significantly. To overcome this
limitation, we propose Racing Control Variable Genetic Programming
(Racing-CVGP), which carries out multiple experiment schedules simultaneously.
A selection scheme similar to that used in selecting good symbolic equations in
the genetic programming process is implemented to ensure that promising
experiment schedules eventually win over the average ones. The unfavorable
schedules are terminated early to save time for the promising ones. We evaluate
Racing-CVGP on several synthetic and real-world datasets corresponding to true
physics laws. We demonstrate that Racing-CVGP outperforms CVGP and a series of
symbolic regressors which discover equations from fixed datasets. | [
"Nan Jiang",
"Yexiang Xue"
] | 2023-09-13 21:38:06 | http://arxiv.org/abs/2309.07934v1 | http://arxiv.org/pdf/2309.07934v1 | 2309.07934v1 |
Traveling Words: A Geometric Interpretation of Transformers | Transformers have significantly advanced the field of natural language
processing, but comprehending their internal mechanisms remains a challenge. In
this paper, we introduce a novel geometric perspective that elucidates the
inner mechanisms of transformer operations. Our primary contribution is
illustrating how layer normalization confines the latent features to a
hyper-sphere, subsequently enabling attention to mold the semantic
representation of words on this surface. This geometric viewpoint seamlessly
connects established properties such as iterative refinement and contextual
embeddings. We validate our insights by probing a pre-trained 124M parameter
GPT-2 model. Our findings reveal clear query-key attention patterns in early
layers and build upon prior observations regarding the subject-specific nature
of attention heads at deeper layers. Harnessing these geometric insights, we
present an intuitive understanding of transformers, depicting them as processes
that model the trajectory of word particles along the hyper-sphere. | [
"Raul Molina"
] | 2023-09-13 21:01:03 | http://arxiv.org/abs/2309.07315v2 | http://arxiv.org/pdf/2309.07315v2 | 2309.07315v2 |
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition | Objective: The objective of the study is to efficiently increase the
expressivity of surface electromyography-based (sEMG) gesture recognition
systems. Approach: We use a problem transformation approach, in which actions
were subset into two biomechanically independent components - a set of wrist
directions and a set of finger modifiers. To maintain fast calibration time, we
train models for each component using only individual gestures, and extrapolate
to the full product space of combination gestures by generating synthetic data.
We collected a supervised dataset with high-confidence ground truth labels in
which subjects performed combination gestures while holding a joystick, and
conducted experiments to analyze the impact of model architectures, classifier
algorithms, and synthetic data generation strategies on the performance of the
proposed approach. Main Results: We found that a problem transformation
approach using a parallel model architecture in combination with a non-linear
classifier, along with restricted synthetic data generation, shows promise in
increasing the expressivity of sEMG-based gestures with a short calibration
time. Significance: sEMG-based gesture recognition has applications in
human-computer interaction, virtual reality, and the control of robotic and
prosthetic devices. Existing approaches require exhaustive model calibration.
The proposed approach increases expressivity without requiring users to
demonstrate all combination gesture classes. Our results may be extended to
larger gesture vocabularies and more complicated model architectures. | [
"Niklas Smedemark-Margulies",
"Yunus Bicer",
"Elifnur Sunger",
"Stephanie Naufel",
"Tales Imbiriba",
"Eugene Tunik",
"Deniz Erdoğmuş",
"Mathew Yarossi"
] | 2023-09-13 20:21:41 | http://arxiv.org/abs/2309.12217v1 | http://arxiv.org/pdf/2309.12217v1 | 2309.12217v1 |
User Training with Error Augmentation for Electromyogram-based Gesture Classification | We designed and tested a system for real-time control of a user interface by
extracting surface electromyographic (sEMG) activity from eight electrodes in a
wrist-band configuration. sEMG data were streamed into a machine-learning
algorithm that classified hand gestures in real-time. After an initial model
calibration, participants were presented with one of three types of feedback
during a human-learning stage: veridical feedback, in which predicted
probabilities from the gesture classification algorithm were displayed without
alteration, modified feedback, in which we applied a hidden augmentation of
error to these probabilities, and no feedback. User performance was then
evaluated in a series of minigames, in which subjects were required to use
eight gestures to manipulate their game avatar to complete a task. Experimental
results indicated that, relative to baseline, the modified feedback condition
led to significantly improved accuracy and improved gesture class separation.
These findings suggest that real-time feedback in a gamified user interface
with manipulation of feedback may enable intuitive, rapid, and accurate task
acquisition for sEMG-based gesture recognition applications. | [
"Yunus Bicer",
"Niklas Smedemark-Margulies",
"Basak Celik",
"Elifnur Sunger",
"Ryan Orendorff",
"Stephanie Naufel",
"Tales Imbiriba",
"Deniz Erdo{ğ}mu{ş}",
"Eugene Tunik",
"Mathew Yarossi"
] | 2023-09-13 20:15:25 | http://arxiv.org/abs/2309.07289v1 | http://arxiv.org/pdf/2309.07289v1 | 2309.07289v1 |
Unbiased Face Synthesis With Diffusion Models: Are We There Yet? | Text-to-image diffusion models have achieved widespread popularity due to
their unprecedented image generation capability. In particular, their ability
to synthesize and modify human faces has spurred research into using generated
face images in both training data augmentation and model performance
assessments. In this paper, we study the efficacy and shortcomings of
generative models in the context of face generation. Utilizing a combination of
qualitative and quantitative measures, including embedding-based metrics and
user studies, we present a framework to audit the characteristics of generated
faces conditioned on a set of social attributes. We applied our framework on
faces generated through state-of-the-art text-to-image diffusion models. We
identify several limitations of face image generation that include faithfulness
to the text prompt, demographic disparities, and distributional shifts.
Furthermore, we present an analytical model that provides insights into how
training data selection contributes to the performance of generative models. | [
"Harrison Rosenberg",
"Shimaa Ahmed",
"Guruprasad V Ramesh",
"Ramya Korlakai Vinayak",
"Kassem Fawaz"
] | 2023-09-13 19:33:26 | http://arxiv.org/abs/2309.07277v1 | http://arxiv.org/pdf/2309.07277v1 | 2309.07277v1 |
Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach | The open radio access network (O-RAN) architecture supports intelligent
network control algorithms as one of its core capabilities. Data-driven
applications incorporate such algorithms to optimize radio access network (RAN)
functions via RAN intelligent controllers (RICs). Deep reinforcement learning
(DRL) algorithms are among the main approaches adopted in the O-RAN literature
to solve dynamic radio resource management problems. However, despite the
benefits introduced by the O-RAN RICs, the practical adoption of DRL algorithms
in real network deployments falls behind. This is primarily due to the slow
convergence and unstable performance exhibited by DRL agents upon deployment
and when encountering previously unseen network conditions. In this paper, we
address these challenges by proposing transfer learning (TL) as a core
component of the training and deployment workflows for the DRL-based
closed-loop control of O-RAN functionalities. To this end, we propose and
design a hybrid TL-aided approach that leverages the advantages of both policy
reuse and distillation TL methods to provide safe and accelerated convergence
in DRL-based O-RAN slicing. We conduct a thorough experiment that accommodates
multiple services, including real VR gaming traffic to reflect practical
scenarios of O-RAN slicing. We also propose and implement policy reuse and
distillation-aided DRL and non-TL-aided DRL as three separate baselines. The
proposed hybrid approach shows at least: 7.7% and 20.7% improvements in the
average initial reward value and the percentage of converged scenarios, and a
64.6% decrease in reward variance while maintaining fast convergence and
enhancing the generalizability compared with the baselines. | [
"Ahmad M. Nagib",
"Hatem Abou-Zeid",
"Hossam S. Hassanein"
] | 2023-09-13 18:58:34 | http://arxiv.org/abs/2309.07265v2 | http://arxiv.org/pdf/2309.07265v2 | 2309.07265v2 |
Simultaneous inference for generalized linear models with unmeasured confounders | Tens of thousands of simultaneous hypothesis tests are routinely performed in
genomic studies to identify differentially expressed genes. However, due to
unmeasured confounders, many standard statistical approaches may be
substantially biased. This paper investigates the large-scale hypothesis
testing problem for multivariate generalized linear models in the presence of
confounding effects. Under arbitrary confounding mechanisms, we propose a
unified statistical estimation and inference framework that harnesses
orthogonal structures and integrates linear projections into three key stages.
It begins by disentangling marginal and uncorrelated confounding effects to
recover the latent coefficients. Subsequently, latent factors and primary
effects are jointly estimated through lasso-type optimization. Finally, we
incorporate projected and weighted bias-correction steps for hypothesis
testing. Theoretically, we establish the identification conditions of various
effects and non-asymptotic error bounds. We show effective Type-I error control
of asymptotic $z$-tests as sample and response sizes approach infinity.
Numerical experiments demonstrate that the proposed method controls the false
discovery rate by the Benjamini-Hochberg procedure and is more powerful than
alternative methods. By comparing single-cell RNA-seq counts from two groups of
samples, we demonstrate the suitability of adjusting confounding effects when
significant covariates are absent from the model. | [
"Jin-Hong Du",
"Larry Wasserman",
"Kathryn Roeder"
] | 2023-09-13 18:53:11 | http://arxiv.org/abs/2309.07261v2 | http://arxiv.org/pdf/2309.07261v2 | 2309.07261v2 |
All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks | Variational algorithms require architectures that naturally constrain the
optimisation space to run efficiently. In geometric quantum machine learning,
one achieves this by encoding group structure into parameterised quantum
circuits to include the symmetries of a problem as an inductive bias. However,
constructing such circuits is challenging as a concrete guiding principle has
yet to emerge. In this paper, we propose the use of spin networks, a form of
directed tensor network invariant under a group transformation, to devise SU(2)
equivariant quantum circuit ans\"atze -- circuits possessing spin rotation
symmetry. By changing to the basis that block diagonalises SU(2) group action,
these networks provide a natural building block for constructing parameterised
equivariant quantum circuits. We prove that our construction is mathematically
equivalent to other known constructions, such as those based on twirling and
generalised permutations, but more direct to implement on quantum hardware. The
efficacy of our constructed circuits is tested by solving the ground state
problem of SU(2) symmetric Heisenberg models on the one-dimensional triangular
lattice and on the Kagome lattice. Our results highlight that our equivariant
circuits boost the performance of quantum variational algorithms, indicating
broader applicability to other real-world problems. | [
"Richard D. P. East",
"Guillermo Alonso-Linaje",
"Chae-Yeun Park"
] | 2023-09-13 18:38:41 | http://arxiv.org/abs/2309.07250v1 | http://arxiv.org/pdf/2309.07250v1 | 2309.07250v1 |
Autotuning Apache TVM-based Scientific Applications Using Bayesian Optimization | Apache TVM (Tensor Virtual Machine), an open source machine learning compiler
framework designed to optimize computations across various hardware platforms,
provides an opportunity to improve the performance of dense matrix
factorizations such as LU (Lower Upper) decomposition and Cholesky
decomposition on GPUs and AI (Artificial Intelligence) accelerators. In this
paper, we propose a new TVM autotuning framework using Bayesian Optimization
and use the TVM tensor expression language to implement linear algebra kernels
such as LU, Cholesky, and 3mm. We use these scientific computation kernels to
evaluate the effectiveness of our methods on a GPU cluster, called Swing, at
Argonne National Laboratory. We compare the proposed autotuning framework with
the TVM autotuning framework AutoTVM with four tuners and find that our
framework outperforms AutoTVM in most cases. | [
"Xingfu Wu",
"Praveen Paramasivam",
"Valerie Taylor"
] | 2023-09-13 18:15:58 | http://arxiv.org/abs/2309.07235v1 | http://arxiv.org/pdf/2309.07235v1 | 2309.07235v1 |
EarthPT: a foundation model for Earth Observation | We introduce EarthPT -- an Earth Observation (EO) pretrained transformer.
EarthPT is a 700 million parameter decoding transformer foundation model
trained in an autoregressive self-supervised manner and developed specifically
with EO use-cases in mind. We demonstrate that EarthPT is an effective
forecaster that can accurately predict future pixel-level surface reflectances
across the 400-2300 nm range well into the future. For example, forecasts of
the evolution of the Normalised Difference Vegetation Index (NDVI) have a
typical error of approximately 0.05 (over a natural range of -1 -> 1) at the
pixel level over a five month test set horizon, out-performing simple
phase-folded models based on historical averaging. We also demonstrate that
embeddings learnt by EarthPT hold semantically meaningful information and could
be exploited for downstream tasks such as highly granular, dynamic land use
classification. Excitingly, we note that the abundance of EO data provides us
with -- in theory -- quadrillions of training tokens. Therefore, if we assume
that EarthPT follows neural scaling laws akin to those derived for Large
Language Models (LLMs), there is currently no data-imposed limit to scaling
EarthPT and other similar `Large Observation Models.' | [
"Michael J. Smith",
"Luke Fleming",
"James E. Geach"
] | 2023-09-13 18:00:00 | http://arxiv.org/abs/2309.07207v1 | http://arxiv.org/pdf/2309.07207v1 | 2309.07207v1 |
Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics | Multi-modal large language models (MLLMs) are trained based on large language
models (LLM), with an enhanced capability to comprehend multi-modal inputs and
generate textual responses. While they excel in multi-modal tasks, the pure NLP
abilities of MLLMs are often underestimated and left untested. In this study,
we get out of the box and unveil an intriguing characteristic of MLLMs -- our
preliminary results suggest that visual instruction tuning, a prevailing
strategy for transitioning LLMs into MLLMs, unexpectedly and interestingly
helps models attain both improved truthfulness and ethical alignment in the
pure NLP context. For example, a visual-instruction-tuned LLaMA2 7B model
surpasses the performance of the LLaMA2-chat 7B model, fine-tuned with over one
million human annotations, on TruthfulQA-mc and Ethics benchmarks. Further
analysis reveals that the improved alignment can be attributed to the superior
instruction quality inherent to visual-text data. In releasing our code at
github.com/UCSC-VLAA/Sight-Beyond-Text, we aspire to foster further exploration
into the intrinsic value of visual-text synergies and, in a broader scope,
multi-modal interactions in alignment research. | [
"Haoqin Tu",
"Bingchen Zhao",
"Chen Wei",
"Cihang Xie"
] | 2023-09-13 17:57:21 | http://arxiv.org/abs/2309.07120v1 | http://arxiv.org/pdf/2309.07120v1 | 2309.07120v1 |
PILOT: A Pre-Trained Model-Based Continual Learning Toolbox | While traditional machine learning can effectively tackle a wide range of
problems, it primarily operates within a closed-world setting, which presents
limitations when dealing with streaming data. As a solution, incremental
learning emerges to address real-world scenarios involving new data's arrival.
Recently, pre-training has made significant advancements and garnered the
attention of numerous researchers. The strong performance of these pre-trained
models (PTMs) presents a promising avenue for developing continual learning
algorithms that can effectively adapt to real-world scenarios. Consequently,
exploring the utilization of PTMs in incremental learning has become essential.
This paper introduces a pre-trained model-based continual learning toolbox
known as PILOT. On the one hand, PILOT implements some state-of-the-art
class-incremental learning algorithms based on pre-trained models, such as L2P,
DualPrompt, and CODA-Prompt. On the other hand, PILOT also fits typical
class-incremental learning algorithms (e.g., DER, FOSTER, and MEMO) within the
context of pre-trained models to evaluate their effectiveness. | [
"Hai-Long Sun",
"Da-Wei Zhou",
"Han-Jia Ye",
"De-Chuan Zhan"
] | 2023-09-13 17:55:11 | http://arxiv.org/abs/2309.07117v1 | http://arxiv.org/pdf/2309.07117v1 | 2309.07117v1 |
Weakly-Supervised Multi-Task Learning for Audio-Visual Speaker Verification | In this paper, we present a methodology for achieving robust multimodal
person representations optimized for open-set audio-visual speaker
verification. Distance Metric Learning (DML) approaches have typically
dominated this problem space, owing to strong performance on new and unseen
classes. In our work, we explored multitask learning techniques to further
boost performance of the DML approach and show that an auxiliary task with weak
labels can increase the compactness of the learned speaker representation. We
also extend the Generalized end-to-end loss (GE2E) to multimodal inputs and
demonstrate that it can achieve competitive performance in an audio-visual
space. Finally, we introduce a non-synchronous audio-visual sampling random
strategy during training time that has shown to improve generalization. Our
network achieves state of the art performance for speaker verification,
reporting 0.244%, 0.252%, 0.441% Equal Error Rate (EER) on the three official
trial lists of VoxCeleb1-O/E/H, which is to our knowledge, the best published
results on VoxCeleb1-E and VoxCeleb1-H. | [
"Anith Selvakumar",
"Homa Fashandi"
] | 2023-09-13 17:45:41 | http://arxiv.org/abs/2309.07115v1 | http://arxiv.org/pdf/2309.07115v1 | 2309.07115v1 |
Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology | Deep neural network models can learn clinically relevant features from
millions of histopathology images. However generating high-quality annotations
to train such models for each hospital, each cancer type, and each diagnostic
task is prohibitively laborious. On the other hand, terabytes of training data
-- while lacking reliable annotations -- are readily available in the public
domain in some cases. In this work, we explore how these large datasets can be
consciously utilized to pre-train deep networks to encode informative
representations. We then fine-tune our pre-trained models on a fraction of
annotated training data to perform specific downstream tasks. We show that our
approach can reach the state-of-the-art (SOTA) for patch-level classification
with only 1-10% randomly selected annotations compared to other SOTA
approaches. Moreover, we propose an uncertainty-aware loss function, to
quantify the model confidence during inference. Quantified uncertainty helps
experts select the best instances to label for further training. Our
uncertainty-aware labeling reaches the SOTA with significantly fewer
annotations compared to random labeling. Last, we demonstrate how our
pre-trained encoders can surpass current SOTA for whole-slide image
classification with weak supervision. Our work lays the foundation for data and
task-agnostic pre-trained deep networks with quantified uncertainty. | [
"Nirhoshan Sivaroopan",
"Chamuditha Jayanga",
"Chalani Ekanayake",
"Hasindri Watawana",
"Jathurshan Pradeepkumar",
"Mithunjha Anandakumar",
"Ranga Rodrigo",
"Chamira U. S. Edussooriya",
"Dushan N. Wadduwage"
] | 2023-09-13 17:37:19 | http://arxiv.org/abs/2309.07113v1 | http://arxiv.org/pdf/2309.07113v1 | 2309.07113v1 |
Data Augmentation via Subgroup Mixup for Improving Fairness | In this work, we propose data augmentation via pairwise mixup across
subgroups to improve group fairness. Many real-world applications of machine
learning systems exhibit biases across certain groups due to
under-representation or training data that reflects societal biases. Inspired
by the successes of mixup for improving classification performance, we develop
a pairwise mixup scheme to augment training data and encourage fair and
accurate decision boundaries for all subgroups. Data augmentation for group
fairness allows us to add new samples of underrepresented groups to balance
subpopulations. Furthermore, our method allows us to use the generalization
ability of mixup to improve both fairness and accuracy. We compare our proposed
mixup to existing data augmentation and bias mitigation approaches on both
synthetic simulations and real-world benchmark fair classification data,
demonstrating that we are able to achieve fair outcomes with robust if not
improved accuracy. | [
"Madeline Navarro",
"Camille Little",
"Genevera I. Allen",
"Santiago Segarra"
] | 2023-09-13 17:32:21 | http://arxiv.org/abs/2309.07110v1 | http://arxiv.org/pdf/2309.07110v1 | 2309.07110v1 |
Characterizing Speed Performance of Multi-Agent Reinforcement Learning | Multi-Agent Reinforcement Learning (MARL) has achieved significant success in
large-scale AI systems and big-data applications such as smart grids,
surveillance, etc. Existing advancements in MARL algorithms focus on improving
the rewards obtained by introducing various mechanisms for inter-agent
cooperation. However, these optimizations are usually compute- and
memory-intensive, thus leading to suboptimal speed performance in end-to-end
training time. In this work, we analyze the speed performance (i.e.,
latency-bounded throughput) as the key metric in MARL implementations.
Specifically, we first introduce a taxonomy of MARL algorithms from an
acceleration perspective categorized by (1) training scheme and (2)
communication method. Using our taxonomy, we identify three state-of-the-art
MARL algorithms - Multi-Agent Deep Deterministic Policy Gradient (MADDPG),
Target-oriented Multi-agent Communication and Cooperation (ToM2C), and
Networked Multi-Agent RL (NeurComm) - as target benchmark algorithms, and
provide a systematic analysis of their performance bottlenecks on a homogeneous
multi-core CPU platform. We justify the need for MARL latency-bounded
throughput to be a key performance metric in future literature while also
addressing opportunities for parallelization and acceleration. | [
"Samuel Wiggins",
"Yuan Meng",
"Rajgopal Kannan",
"Viktor Prasanna"
] | 2023-09-13 17:26:36 | http://arxiv.org/abs/2309.07108v1 | http://arxiv.org/pdf/2309.07108v1 | 2309.07108v1 |
Mitigating Group Bias in Federated Learning for Heterogeneous Devices | Federated Learning is emerging as a privacy-preserving model training
approach in distributed edge applications. As such, most edge deployments are
heterogeneous in nature i.e., their sensing capabilities and environments vary
across deployments. This edge heterogeneity violates the independence and
identical distribution (IID) property of local data across clients and produces
biased global models i.e. models that contribute to unfair decision-making and
discrimination against a particular community or a group. Existing bias
mitigation techniques only focus on bias generated from label heterogeneity in
non-IID data without accounting for domain variations due to feature
heterogeneity and do not address global group-fairness property.
Our work proposes a group-fair FL framework that minimizes group-bias while
preserving privacy and without resource utilization overhead. Our main idea is
to leverage average conditional probabilities to compute a cross-domain group
\textit{importance weights} derived from heterogeneous training data to
optimize the performance of the worst-performing group using a modified
multiplicative weights update method. Additionally, we propose regularization
techniques to minimize the difference between the worst and best-performing
groups while making sure through our thresholding mechanism to strike a balance
between bias reduction and group performance degradation. Our evaluation of
human emotion recognition and image classification benchmarks assesses the fair
decision-making of our framework in real-world heterogeneous settings. | [
"Khotso Selialia",
"Yasra Chandio",
"Fatima M. Anwar"
] | 2023-09-13 16:53:48 | http://arxiv.org/abs/2309.07085v1 | http://arxiv.org/pdf/2309.07085v1 | 2309.07085v1 |
Subsets and Splits