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STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models | We propose in this paper, STANLEY, a STochastic gradient ANisotropic LangEvin
dYnamics, for sampling high dimensional data. With the growing efficacy and
potential of Energy-Based modeling, also known as non-normalized probabilistic
modeling, for modeling a generative process of different natures of high
dimensional data observations, we present an end-to-end learning algorithm for
Energy-Based models (EBM) with the purpose of improving the quality of the
resulting sampled data points. While the unknown normalizing constant of EBMs
makes the training procedure intractable, resorting to Markov Chain Monte Carlo
(MCMC) is in general a viable option. Realizing what MCMC entails for the EBM
training, we propose in this paper, a novel high dimensional sampling method,
based on an anisotropic stepsize and a gradient-informed covariance matrix,
embedded into a discretized Langevin diffusion. We motivate the necessity for
an anisotropic update of the negative samples in the Markov Chain by the
nonlinearity of the backbone of the EBM, here a Convolutional Neural Network.
Our resulting method, namely STANLEY, is an optimization algorithm for training
Energy-Based models via our newly introduced MCMC method. We provide a
theoretical understanding of our sampling scheme by proving that the sampler
leads to a geometrically uniformly ergodic Markov Chain. Several image
generation experiments are provided in our paper to show the effectiveness of
our method. | [
"Belhal Karimi",
"Jianwen Xie",
"Ping Li"
] | 2023-10-19 11:55:16 | http://arxiv.org/abs/2310.12667v1 | http://arxiv.org/pdf/2310.12667v1 | 2310.12667v1 |
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models | While advanced machine learning (ML) models are deployed in numerous
real-world applications, previous works demonstrate these models have security
and privacy vulnerabilities. Various empirical research has been done in this
field. However, most of the experiments are performed on target ML models
trained by the security researchers themselves. Due to the high computational
resource requirement for training advanced models with complex architectures,
researchers generally choose to train a few target models using relatively
simple architectures on typical experiment datasets. We argue that to
understand ML models' vulnerabilities comprehensively, experiments should be
performed on a large set of models trained with various purposes (not just the
purpose of evaluating ML attacks and defenses). To this end, we propose using
publicly available models with weights from the Internet (public models) for
evaluating attacks and defenses on ML models. We establish a database, namely
SecurityNet, containing 910 annotated image classification models. We then
analyze the effectiveness of several representative attacks/defenses, including
model stealing attacks, membership inference attacks, and backdoor detection on
these public models. Our evaluation empirically shows the performance of these
attacks/defenses can vary significantly on public models compared to
self-trained models. We share SecurityNet with the research community. and
advocate researchers to perform experiments on public models to better
demonstrate their proposed methods' effectiveness in the future. | [
"Boyang Zhang",
"Zheng Li",
"Ziqing Yang",
"Xinlei He",
"Michael Backes",
"Mario Fritz",
"Yang Zhang"
] | 2023-10-19 11:49:22 | http://arxiv.org/abs/2310.12665v1 | http://arxiv.org/pdf/2310.12665v1 | 2310.12665v1 |
Knowledge from Uncertainty in Evidential Deep Learning | This work reveals an evidential signal that emerges from the uncertainty
value in Evidential Deep Learning (EDL). EDL is one example of a class of
uncertainty-aware deep learning approaches designed to provide confidence (or
epistemic uncertainty) about the current test sample. In particular for
computer vision and bidirectional encoder large language models, the
`evidential signal' arising from the Dirichlet strength in EDL can, in some
cases, discriminate between classes, which is particularly strong when using
large language models. We hypothesise that the KL regularisation term causes
EDL to couple aleatoric and epistemic uncertainty. In this paper, we
empirically investigate the correlations between misclassification and
evaluated uncertainty, and show that EDL's `evidential signal' is due to
misclassification bias. We critically evaluate EDL with other Dirichlet-based
approaches, namely Generative Evidential Neural Networks (EDL-GEN) and Prior
Networks, and show theoretically and empirically the differences between these
loss functions. We conclude that EDL's coupling of uncertainty arises from
these differences due to the use (or lack) of out-of-distribution samples
during training. | [
"Cai Davies",
"Marc Roig Vilamala",
"Alun D. Preece",
"Federico Cerutti",
"Lance M. Kaplan",
"Supriyo Chakraborty"
] | 2023-10-19 11:41:52 | http://arxiv.org/abs/2310.12663v1 | http://arxiv.org/pdf/2310.12663v1 | 2310.12663v1 |
A Use Case: Reformulating Query Rewriting as a Statistical Machine Translation Problem | One of the most important challenges for modern search engines is to retrieve
relevant web content based on user queries. In order to achieve this challenge,
search engines have a module to rewrite user queries. That is why modern web
search engines utilize some statistical and neural models used in the natural
language processing domain. Statistical machine translation is a well-known NLP
method among them. The paper proposes a query rewriting pipeline based on a
monolingual machine translation model that learns to rewrite Arabic user search
queries. This paper also describes preprocessing steps to create a mapping
between user queries and web page titles. | [
"Abdullah Can Algan",
"Emre Yürekli",
"Aykut Çayır"
] | 2023-10-19 11:37:14 | http://arxiv.org/abs/2310.13031v1 | http://arxiv.org/pdf/2310.13031v1 | 2310.13031v1 |
Gradient Descent Fails to Learn High-frequency Functions and Modular Arithmetic | Classes of target functions containing a large number of approximately
orthogonal elements are known to be hard to learn by the Statistical Query
algorithms. Recently this classical fact re-emerged in a theory of
gradient-based optimization of neural networks. In the novel framework, the
hardness of a class is usually quantified by the variance of the gradient with
respect to a random choice of a target function.
A set of functions of the form $x\to ax \bmod p$, where $a$ is taken from
${\mathbb Z}_p$, has attracted some attention from deep learning theorists and
cryptographers recently. This class can be understood as a subset of
$p$-periodic functions on ${\mathbb Z}$ and is tightly connected with a class
of high-frequency periodic functions on the real line.
We present a mathematical analysis of limitations and challenges associated
with using gradient-based learning techniques to train a high-frequency
periodic function or modular multiplication from examples. We highlight that
the variance of the gradient is negligibly small in both cases when either a
frequency or the prime base $p$ is large. This in turn prevents such a learning
algorithm from being successful. | [
"Rustem Takhanov",
"Maxat Tezekbayev",
"Artur Pak",
"Arman Bolatov",
"Zhenisbek Assylbekov"
] | 2023-10-19 11:33:33 | http://arxiv.org/abs/2310.12660v1 | http://arxiv.org/pdf/2310.12660v1 | 2310.12660v1 |
On existence, uniqueness and scalability of adversarial robustness measures for AI classifiers | Simply-verifiable mathematical conditions for existence, uniqueness and
explicit analytical computation of minimal adversarial paths (MAP) and minimal
adversarial distances (MAD) for (locally) uniquely-invertible classifiers, for
generalized linear models (GLM), and for entropic AI (EAI) are formulated and
proven. Practical computation of MAP and MAD, their comparison and
interpretations for various classes of AI tools (for neuronal networks, boosted
random forests, GLM and EAI) are demonstrated on the common synthetic
benchmarks: on a double Swiss roll spiral and its extensions, as well as on the
two biomedical data problems (for the health insurance claim predictions, and
for the heart attack lethality classification). On biomedical applications it
is demonstrated how MAP provides unique minimal patient-specific
risk-mitigating interventions in the predefined subsets of accessible control
variables. | [
"Illia Horenko"
] | 2023-10-19 10:36:02 | http://arxiv.org/abs/2310.14421v1 | http://arxiv.org/pdf/2310.14421v1 | 2310.14421v1 |
Towards a Deep Learning-based Online Quality Prediction System for Welding Processes | The digitization of manufacturing processes enables promising applications
for machine learning-assisted quality assurance. A widely used manufacturing
process that can strongly benefit from data-driven solutions is gas metal arc
welding (GMAW). The welding process is characterized by complex cause-effect
relationships between material properties, process conditions and weld quality.
In non-laboratory environments with frequently changing process parameters,
accurate determination of weld quality by destructive testing is economically
unfeasible. Deep learning offers the potential to identify the relationships in
available process data and predict the weld quality from process observations.
In this paper, we present a concept for a deep learning based predictive
quality system in GMAW. At its core, the concept involves a pipeline consisting
of four major phases: collection and management of multi-sensor data (e.g.
current and voltage), real-time processing and feature engineering of the time
series data by means of autoencoders, training and deployment of suitable
recurrent deep learning models for quality predictions, and model evolutions
under changing process conditions using continual learning. The concept
provides the foundation for future research activities in which we will realize
an online predictive quality system for running production. | [
"Yannik Hahn",
"Robert Maack",
"Guido Buchholz",
"Marion Purrio",
"Matthias Angerhausen",
"Hasan Tercan",
"Tobias Meisen"
] | 2023-10-19 10:35:50 | http://arxiv.org/abs/2310.12632v2 | http://arxiv.org/pdf/2310.12632v2 | 2310.12632v2 |
Inverse Renormalization Group of Disordered Systems | We propose inverse renormalization group transformations to construct
approximate configurations for lattice volumes that have not yet been accessed
by supercomputers or large-scale simulations in the study of spin glasses.
Specifically, starting from lattices of volume $V=8^{3}$ in the case of the
three-dimensional Edwards-Anderson model we employ machine learning algorithms
to construct rescaled lattices up to $V'=128^{3}$, which we utilize to extract
two critical exponents. We conclude by discussing how to incorporate numerical
exactness within inverse renormalization group approaches of disordered
systems, thus opening up the opportunity to explore a sustainable and
energy-efficient generation of exact configurations for increasing lattice
volumes without the use of dedicated supercomputers. | [
"Dimitrios Bachtis"
] | 2023-10-19 10:35:41 | http://arxiv.org/abs/2310.12631v1 | http://arxiv.org/pdf/2310.12631v1 | 2310.12631v1 |
An Improved Metarounding Algorithm via Frank-Wolfe | Metarounding is an approach to convert an approximation algorithm for linear
optimization over some combinatorial classes to an online linear optimization
algorithm for the same class. We propose a new metarounding algorithm under a
natural assumption that a relax-based approximation algorithm exists for the
combinatorial class. Our algorithm is much more efficient in both theoretical
and practical aspects. | [
"Ryotaro Mitsuboshi",
"Kohei Hatano",
"Eiji Takimoto"
] | 2023-10-19 10:22:03 | http://arxiv.org/abs/2310.12629v1 | http://arxiv.org/pdf/2310.12629v1 | 2310.12629v1 |
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series | In this paper we tackle the problem of point and probabilistic forecasting by
describing a blending methodology of machine learning models that belong to
gradient boosted trees and neural networks families. These principles were
successfully applied in the recent M5 Competition on both Accuracy and
Uncertainty tracks. The keypoints of our methodology are: a) transform the task
to regression on sales for a single day b) information rich feature engineering
c) create a diverse set of state-of-the-art machine learning models and d)
carefully construct validation sets for model tuning. We argue that the
diversity of the machine learning models along with the careful selection of
validation examples, where the most important ingredients for the effectiveness
of our approach. Although forecasting data had an inherent hierarchy structure
(12 levels), none of our proposed solutions exploited that hierarchical scheme.
Using the proposed methodology, our team was ranked within the gold medal range
in both Accuracy and the Uncertainty track. Inference code along with already
trained models are available at
https://github.com/IoannisNasios/M5_Uncertainty_3rd_place | [
"Ioannis Nasios",
"Konstantinos Vogklis"
] | 2023-10-19 09:42:02 | http://arxiv.org/abs/2310.13029v1 | http://arxiv.org/pdf/2310.13029v1 | 2310.13029v1 |
How a student becomes a teacher: learning and forgetting through Spectral methods | In theoretical ML, the teacher-student paradigm is often employed as an
effective metaphor for real-life tuition. The above scheme proves particularly
relevant when the student network is overparameterized as compared to the
teacher network. Under these operating conditions, it is tempting to speculate
that the student ability to handle the given task could be eventually stored in
a sub-portion of the whole network. This latter should be to some extent
reminiscent of the frozen teacher structure, according to suitable metrics,
while being approximately invariant across different architectures of the
student candidate network. Unfortunately, state-of-the-art conventional
learning techniques could not help in identifying the existence of such an
invariant subnetwork, due to the inherent degree of non-convexity that
characterizes the examined problem. In this work, we take a leap forward by
proposing a radically different optimization scheme which builds on a spectral
representation of the linear transfer of information between layers. The
gradient is hence calculated with respect to both eigenvalues and eigenvectors
with negligible increase in terms of computational and complexity load, as
compared to standard training algorithms. Working in this framework, we could
isolate a stable student substructure, that mirrors the true complexity of the
teacher in terms of computing neurons, path distribution and topological
attributes. When pruning unimportant nodes of the trained student, as follows a
ranking that reflects the optimized eigenvalues, no degradation in the recorded
performance is seen above a threshold that corresponds to the effective teacher
size. The observed behavior can be pictured as a genuine second-order phase
transition that bears universality traits. | [
"Lorenzo Giambagli",
"Lorenzo Buffoni",
"Lorenzo Chicchi",
"Duccio Fanelli"
] | 2023-10-19 09:40:30 | http://arxiv.org/abs/2310.12612v1 | http://arxiv.org/pdf/2310.12612v1 | 2310.12612v1 |
Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning | Diffusion models have risen as a powerful tool in robotics due to their
flexibility and multi-modality. While some of these methods effectively address
complex problems, they often depend heavily on inference-time obstacle
detection and require additional equipment. Addressing these challenges, we
present a method that, during inference time, simultaneously generates only
reachable goals and plans motions that avoid obstacles, all from a single
visual input. Central to our approach is the novel use of a collision-avoiding
diffusion kernel for training. Through evaluations against behavior-cloning and
classical diffusion models, our framework has proven its robustness. It is
particularly effective in multi-modal environments, navigating toward goals and
avoiding unreachable ones blocked by obstacles, while ensuring collision
avoidance. | [
"Junwoo Chang",
"Hyunwoo Ryu",
"Jiwoo Kim",
"Soochul Yoo",
"Joohwan Seo",
"Nikhil Prakash",
"Jongeun Choi",
"Roberto Horowitz"
] | 2023-10-19 09:39:07 | http://arxiv.org/abs/2310.12609v1 | http://arxiv.org/pdf/2310.12609v1 | 2310.12609v1 |
Causal Similarity-Based Hierarchical Bayesian Models | The key challenge underlying machine learning is generalisation to new data.
This work studies generalisation for datasets consisting of related tasks that
may differ in causal mechanisms. For example, observational medical data for
complex diseases suffers from heterogeneity in causal mechanisms of disease
across patients, creating challenges for machine learning algorithms that need
to generalise to new patients outside of the training dataset. Common
approaches for learning supervised models with heterogeneous datasets include
learning a global model for the entire dataset, learning local models for each
tasks' data, or utilising hierarchical, meta-learning and multi-task learning
approaches to learn how to generalise from data pooled across multiple tasks.
In this paper we propose causal similarity-based hierarchical Bayesian models
to improve generalisation to new tasks by learning how to pool data from
training tasks with similar causal mechanisms. We apply this general modelling
principle to Bayesian neural networks and compare a variety of methods for
estimating causal task similarity (for both known and unknown causal models).
We demonstrate the benefits of our approach and applicability to real world
problems through a range of experiments on simulated and real data. | [
"Sophie Wharrie",
"Samuel Kaski"
] | 2023-10-19 09:03:41 | http://arxiv.org/abs/2310.12595v1 | http://arxiv.org/pdf/2310.12595v1 | 2310.12595v1 |
Time-Aware Representation Learning for Time-Sensitive Question Answering | Time is one of the crucial factors in real-world question answering (QA)
problems. However, language models have difficulty understanding the
relationships between time specifiers, such as 'after' and 'before', and
numbers, since existing QA datasets do not include sufficient time expressions.
To address this issue, we propose a Time-Context aware Question Answering
(TCQA) framework. We suggest a Time-Context dependent Span Extraction (TCSE)
task, and build a time-context dependent data generation framework for model
training. Moreover, we present a metric to evaluate the time awareness of the
QA model using TCSE. The TCSE task consists of a question and four sentence
candidates classified as correct or incorrect based on time and context. The
model is trained to extract the answer span from the sentence that is both
correct in time and context. The model trained with TCQA outperforms baseline
models up to 8.5 of the F1-score in the TimeQA dataset. Our dataset and code
are available at https://github.com/sonjbin/TCQA | [
"Jungbin Son",
"Alice Oh"
] | 2023-10-19 08:48:45 | http://arxiv.org/abs/2310.12585v1 | http://arxiv.org/pdf/2310.12585v1 | 2310.12585v1 |
DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation | Great progress has been made in automatic medical image segmentation due to
powerful deep representation learning. The influence of transformer has led to
research into its variants, and large-scale replacement of traditional CNN
modules. However, such trend often overlooks the intrinsic feature extraction
capabilities of the transformer and potential refinements to both the model and
the transformer module through minor adjustments. This study proposes a novel
deep medical image segmentation framework, called DA-TransUNet, aiming to
introduce the Transformer and dual attention block into the encoder and decoder
of the traditional U-shaped architecture. Unlike prior transformer-based
solutions, our DA-TransUNet utilizes attention mechanism of transformer and
multifaceted feature extraction of DA-Block, which can efficiently combine
global, local, and multi-scale features to enhance medical image segmentation.
Meanwhile, experimental results show that a dual attention block is added
before the Transformer layer to facilitate feature extraction in the U-net
structure. Furthermore, incorporating dual attention blocks in skip connections
can enhance feature transfer to the decoder, thereby improving image
segmentation performance. Experimental results across various benchmark of
medical image segmentation reveal that DA-TransUNet significantly outperforms
the state-of-the-art methods. The codes and parameters of our model will be
publicly available at https://github.com/SUN-1024/DA-TransUnet. | [
"Guanqun Sun",
"Yizhi Pan",
"Weikun Kong",
"Zichang Xu",
"Jianhua Ma",
"Teeradaj Racharak",
"Le-Minh Nguyen",
"Junyi Xin"
] | 2023-10-19 08:25:03 | http://arxiv.org/abs/2310.12570v1 | http://arxiv.org/pdf/2310.12570v1 | 2310.12570v1 |
Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models | The fast-paced development of machine learning (ML) methods coupled with its
increasing adoption in research poses challenges for researchers without
extensive training in ML. In neuroscience, for example, ML can help understand
brain-behavior relationships, diagnose diseases, and develop biomarkers using
various data sources like magnetic resonance imaging and
electroencephalography. The primary objective of ML is to build models that can
make accurate predictions on unseen data. Researchers aim to prove the
existence of such generalizable models by evaluating performance using
techniques such as cross-validation (CV), which uses systematic subsampling to
estimate the generalization performance. Choosing a CV scheme and evaluating an
ML pipeline can be challenging and, if used improperly, can lead to
overestimated results and incorrect interpretations.
We created julearn, an open-source Python library, that allow researchers to
design and evaluate complex ML pipelines without encountering in common
pitfalls. In this manuscript, we present the rationale behind julearn's design,
its core features, and showcase three examples of previously-published research
projects that can be easily implemented using this novel library. Julearn aims
to simplify the entry into the ML world by providing an easy-to-use environment
with built in guards against some of the most common ML pitfalls. With its
design, unique features and simple interface, it poses as a useful Python-based
library for research projects. | [
"Sami Hamdan",
"Shammi More",
"Leonard Sasse",
"Vera Komeyer",
"Kaustubh R. Patil",
"Federico Raimondo"
] | 2023-10-19 08:21:12 | http://arxiv.org/abs/2310.12568v1 | http://arxiv.org/pdf/2310.12568v1 | 2310.12568v1 |
Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark | Artificial intelligence (AI) systems possess significant potential to drive
societal progress. However, their deployment often faces obstacles due to
substantial safety concerns. Safe reinforcement learning (SafeRL) emerges as a
solution to optimize policies while simultaneously adhering to multiple
constraints, thereby addressing the challenge of integrating reinforcement
learning in safety-critical scenarios. In this paper, we present an environment
suite called Safety-Gymnasium, which encompasses safety-critical tasks in both
single and multi-agent scenarios, accepting vector and vision-only input.
Additionally, we offer a library of algorithms named Safe Policy Optimization
(SafePO), comprising 16 state-of-the-art SafeRL algorithms. This comprehensive
library can serve as a validation tool for the research community. By
introducing this benchmark, we aim to facilitate the evaluation and comparison
of safety performance, thus fostering the development of reinforcement learning
for safer, more reliable, and responsible real-world applications. The website
of this project can be accessed at
https://sites.google.com/view/safety-gymnasium. | [
"Jiaming Ji",
"Borong Zhang",
"Jiayi Zhou",
"Xuehai Pan",
"Weidong Huang",
"Ruiyang Sun",
"Yiran Geng",
"Yifan Zhong",
"Juntao Dai",
"Yaodong Yang"
] | 2023-10-19 08:19:28 | http://arxiv.org/abs/2310.12567v1 | http://arxiv.org/pdf/2310.12567v1 | 2310.12567v1 |
Open-World Lifelong Graph Learning | We study the problem of lifelong graph learning in an open-world scenario,
where a model needs to deal with new tasks and potentially unknown classes. We
utilize Out-of-Distribution (OOD) detection methods to recognize new classes
and adapt existing non-graph OOD detection methods to graph data. Crucially, we
suggest performing new class detection by combining OOD detection methods with
information aggregated from the graph neighborhood. Most OOD detection methods
avoid determining a crisp threshold for deciding whether a vertex is OOD. To
tackle this problem, we propose a Weakly-supervised Relevance Feedback
(Open-WRF) method, which decreases the sensitivity to thresholds in OOD
detection. We evaluate our approach on six benchmark datasets. Our results show
that the proposed neighborhood aggregation method for OOD scores outperforms
existing methods independent of the underlying graph neural network.
Furthermore, we demonstrate that our Open-WRF method is more robust to
threshold selection and analyze the influence of graph neighborhood on OOD
detection. The aggregation and threshold methods are compatible with arbitrary
graph neural networks and OOD detection methods, making our approach versatile
and applicable to many real-world applications. | [
"Marcel Hoffmann",
"Lukas Galke",
"Ansgar Scherp"
] | 2023-10-19 08:18:10 | http://arxiv.org/abs/2310.12565v1 | http://arxiv.org/pdf/2310.12565v1 | 2310.12565v1 |
Approximate information maximization for bandit games | Entropy maximization and free energy minimization are general physical
principles for modeling the dynamics of various physical systems. Notable
examples include modeling decision-making within the brain using the
free-energy principle, optimizing the accuracy-complexity trade-off when
accessing hidden variables with the information bottleneck principle (Tishby et
al., 2000), and navigation in random environments using information
maximization (Vergassola et al., 2007). Built on this principle, we propose a
new class of bandit algorithms that maximize an approximation to the
information of a key variable within the system. To this end, we develop an
approximated analytical physics-based representation of an entropy to forecast
the information gain of each action and greedily choose the one with the
largest information gain. This method yields strong performances in classical
bandit settings. Motivated by its empirical success, we prove its asymptotic
optimality for the two-armed bandit problem with Gaussian rewards. Owing to its
ability to encompass the system's properties in a global physical functional,
this approach can be efficiently adapted to more complex bandit settings,
calling for further investigation of information maximization approaches for
multi-armed bandit problems. | [
"Alex Barbier--Chebbah",
"Christian L. Vestergaard",
"Jean-Baptiste Masson",
"Etienne Boursier"
] | 2023-10-19 08:15:03 | http://arxiv.org/abs/2310.12563v1 | http://arxiv.org/pdf/2310.12563v1 | 2310.12563v1 |
Fast Model Debias with Machine Unlearning | Recent discoveries have revealed that deep neural networks might behave in a
biased manner in many real-world scenarios. For instance, deep networks trained
on a large-scale face recognition dataset CelebA tend to predict blonde hair
for females and black hair for males. Such biases not only jeopardize the
robustness of models but also perpetuate and amplify social biases, which is
especially concerning for automated decision-making processes in healthcare,
recruitment, etc., as they could exacerbate unfair economic and social
inequalities among different groups. Existing debiasing methods suffer from
high costs in bias labeling or model re-training, while also exhibiting a
deficiency in terms of elucidating the origins of biases within the model. To
this respect, we propose a fast model debiasing framework (FMD) which offers an
efficient approach to identify, evaluate and remove biases inherent in trained
models. The FMD identifies biased attributes through an explicit counterfactual
concept and quantifies the influence of data samples with influence functions.
Moreover, we design a machine unlearning-based strategy to efficiently and
effectively remove the bias in a trained model with a small counterfactual
dataset. Experiments on the Colored MNIST, CelebA, and Adult Income datasets
along with experiments with large language models demonstrate that our method
achieves superior or competing accuracies compared with state-of-the-art
methods while attaining significantly fewer biases and requiring much less
debiasing cost. Notably, our method requires only a small external dataset and
updating a minimal amount of model parameters, without the requirement of
access to training data that may be too large or unavailable in practice. | [
"Ruizhe Chen",
"Jianfei Yang",
"Huimin Xiong",
"Jianhong Bai",
"Tianxiang Hu",
"Jin Hao",
"Yang Feng",
"Joey Tianyi Zhou",
"Jian Wu",
"Zuozhu Liu"
] | 2023-10-19 08:10:57 | http://arxiv.org/abs/2310.12560v1 | http://arxiv.org/pdf/2310.12560v1 | 2310.12560v1 |
Explanation-Based Training with Differentiable Insertion/Deletion Metric-Aware Regularizers | The quality of explanations for the predictions of complex machine learning
predictors is often measured using insertion and deletion metrics, which assess
the faithfulness of the explanations, i.e., how correctly the explanations
reflect the predictor's behavior. To improve the faithfulness, we propose
insertion/deletion metric-aware explanation-based optimization (ID-ExpO), which
optimizes differentiable predictors to improve both insertion and deletion
scores of the explanations while keeping their predictive accuracy. Since the
original insertion and deletion metrics are indifferentiable with respect to
the explanations and directly unavailable for gradient-based optimization, we
extend the metrics to be differentiable and use them to formalize insertion and
deletion metric-based regularizers. The experimental results on image and
tabular datasets show that the deep neural networks-based predictors fine-tuned
using ID-ExpO enable popular post-hoc explainers to produce more faithful and
easy-to-interpret explanations while keeping high predictive accuracy. | [
"Yuya Yoshikawa",
"Tomoharu Iwata"
] | 2023-10-19 08:02:40 | http://arxiv.org/abs/2310.12553v2 | http://arxiv.org/pdf/2310.12553v2 | 2310.12553v2 |
PGA: Personalizing Grasping Agents with Single Human-Robot Interaction | Language-Conditioned Robotic Grasping (LCRG) aims to develop robots that
ground and grasp objects based on natural language instructions. While robots
capable of recognizing personal objects like "my wallet" can interact more
naturally with non-expert users, current LCRG systems primarily limit robots to
understanding only generic expressions. To this end, we introduce a task
scenario GraspMine with a novel dataset that aims to locate and grasp personal
objects given personal indicators via learning from a single human-robot
interaction. To address GraspMine, we propose Personalized Grasping Agent
(PGA), that learns personal objects by propagating user-given information
through a Reminiscence-a collection of raw images from the user's environment.
Specifically, PGA acquires personal object information by a user presenting a
personal object with its associated indicator, followed by PGA inspecting the
object by rotating it. Based on the acquired information, PGA pseudo-labels
objects in the Reminiscence by our proposed label propagation algorithm.
Harnessing the information acquired from the interactions and the
pseudo-labeled objects in the Reminiscence, PGA adapts the object grounding
model to grasp personal objects. Experiments on GraspMine show that PGA
significantly outperforms baseline methods both in offline and online settings,
signifying its effectiveness and personalization applicability on real-world
scenarios. Finally, qualitative analysis shows the effectiveness of PGA through
a detailed investigation of results in each phase. | [
"Junghyun Kim",
"Gi-Cheon Kang",
"Jaein Kim",
"Seoyun Yang",
"Minjoon Jung",
"Byoung-Tak Zhang"
] | 2023-10-19 07:54:30 | http://arxiv.org/abs/2310.12547v1 | http://arxiv.org/pdf/2310.12547v1 | 2310.12547v1 |
Neural Likelihood Approximation for Integer Valued Time Series Data | Stochastic processes defined on integer valued state spaces are popular
within the physical and biological sciences. These models are necessary for
capturing the dynamics of small systems where the individual nature of the
populations cannot be ignored and stochastic effects are important. The
inference of the parameters of such models, from time series data, is difficult
due to intractability of the likelihood; current methods, based on simulations
of the underlying model, can be so computationally expensive as to be
prohibitive. In this paper we construct a neural likelihood approximation for
integer valued time series data using causal convolutions, which allows us to
evaluate the likelihood of the whole time series in parallel. We demonstrate
our method by performing inference on a number of ecological and
epidemiological models, showing that we can accurately approximate the true
posterior while achieving significant computational speed ups in situations
where current methods struggle. | [
"Luke O'Loughlin",
"John Maclean",
"Andrew Black"
] | 2023-10-19 07:51:39 | http://arxiv.org/abs/2310.12544v1 | http://arxiv.org/pdf/2310.12544v1 | 2310.12544v1 |
Be Bayesian by Attachments to Catch More Uncertainty | Bayesian Neural Networks (BNNs) have become one of the promising approaches
for uncertainty estimation due to the solid theorical foundations. However, the
performance of BNNs is affected by the ability of catching uncertainty. Instead
of only seeking the distribution of neural network weights by in-distribution
(ID) data, in this paper, we propose a new Bayesian Neural Network with an
Attached structure (ABNN) to catch more uncertainty from out-of-distribution
(OOD) data. We first construct a mathematical description for the uncertainty
of OOD data according to the prior distribution, and then develop an attached
Bayesian structure to integrate the uncertainty of OOD data into the backbone
network. ABNN is composed of an expectation module and several distribution
modules. The expectation module is a backbone deep network which focuses on the
original task, and the distribution modules are mini Bayesian structures which
serve as attachments of the backbone. In particular, the distribution modules
aim at extracting the uncertainty from both ID and OOD data. We further provide
theoretical analysis for the convergence of ABNN, and experimentally validate
its superiority by comparing with some state-of-the-art uncertainty estimation
methods Code will be made available. | [
"Shiyu Shen",
"Bin Pan",
"Tianyang Shi",
"Tao Li",
"Zhenwei Shi"
] | 2023-10-19 07:28:39 | http://arxiv.org/abs/2310.13027v1 | http://arxiv.org/pdf/2310.13027v1 | 2310.13027v1 |
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers | Machine learning has rapidly become a tool of choice for the astronomical
community. It is being applied across a wide range of wavelengths and problems,
from the classification of transients to neural network emulators of
cosmological simulations, and is shifting paradigms about how we generate and
report scientific results. At the same time, this class of method comes with
its own set of best practices, challenges, and drawbacks, which, at present,
are often reported on incompletely in the astrophysical literature. With this
paper, we aim to provide a primer to the astronomical community, including
authors, reviewers, and editors, on how to implement machine learning models
and report their results in a way that ensures the accuracy of the results,
reproducibility of the findings, and usefulness of the method. | [
"D. Huppenkothen",
"M. Ntampaka",
"M. Ho",
"M. Fouesneau",
"B. Nord",
"J. E. G. Peek",
"M. Walmsley",
"J. F. Wu",
"C. Avestruz",
"T. Buck",
"M. Brescia",
"D. P. Finkbeiner",
"A. D. Goulding",
"T. Kacprzak",
"P. Melchior",
"M. Pasquato",
"N. Ramachandra",
"Y. -S. Ting",
"G. van de Ven",
"S. Villar",
"V. A. Villar",
"E. Zinger"
] | 2023-10-19 07:04:36 | http://arxiv.org/abs/2310.12528v1 | http://arxiv.org/pdf/2310.12528v1 | 2310.12528v1 |
Testing the Consistency of Performance Scores Reported for Binary Classification Problems | Binary classification is a fundamental task in machine learning, with
applications spanning various scientific domains. Whether scientists are
conducting fundamental research or refining practical applications, they
typically assess and rank classification techniques based on performance
metrics such as accuracy, sensitivity, and specificity. However, reported
performance scores may not always serve as a reliable basis for research
ranking. This can be attributed to undisclosed or unconventional practices
related to cross-validation, typographical errors, and other factors. In a
given experimental setup, with a specific number of positive and negative test
items, most performance scores can assume specific, interrelated values. In
this paper, we introduce numerical techniques to assess the consistency of
reported performance scores and the assumed experimental setup. Importantly,
the proposed approach does not rely on statistical inference but uses numerical
methods to identify inconsistencies with certainty. Through three different
applications related to medicine, we demonstrate how the proposed techniques
can effectively detect inconsistencies, thereby safeguarding the integrity of
research fields. To benefit the scientific community, we have made the
consistency tests available in an open-source Python package. | [
"Attila Fazekas",
"György Kovács"
] | 2023-10-19 07:04:29 | http://arxiv.org/abs/2310.12527v1 | http://arxiv.org/pdf/2310.12527v1 | 2310.12527v1 |
Parallel Bayesian Optimization Using Satisficing Thompson Sampling for Time-Sensitive Black-Box Optimization | Bayesian optimization (BO) is widely used for black-box optimization
problems, and have been shown to perform well in various real-world tasks.
However, most of the existing BO methods aim to learn the optimal solution,
which may become infeasible when the parameter space is extremely large or the
problem is time-sensitive. In these contexts, switching to a satisficing
solution that requires less information can result in better performance. In
this work, we focus on time-sensitive black-box optimization problems and
propose satisficing Thompson sampling-based parallel Bayesian optimization
(STS-PBO) approaches, including synchronous and asynchronous versions. We shift
the target from an optimal solution to a satisficing solution that is easier to
learn. The rate-distortion theory is introduced to construct a loss function
that balances the amount of information that needs to be learned with
sub-optimality, and the Blahut-Arimoto algorithm is adopted to compute the
target solution that reaches the minimum information rate under the distortion
limit at each step. Both discounted and undiscounted Bayesian cumulative regret
bounds are theoretically derived for the proposed STS-PBO approaches. The
effectiveness of the proposed methods is demonstrated on a fast-charging design
problem of Lithium-ion batteries. The results are accordant with theoretical
analyses, and show that our STS-PBO methods outperform both sequential
counterparts and parallel BO with traditional Thompson sampling in both
synchronous and asynchronous settings. | [
"Xiaobin Song",
"Benben Jiang"
] | 2023-10-19 07:03:51 | http://arxiv.org/abs/2310.12526v1 | http://arxiv.org/pdf/2310.12526v1 | 2310.12526v1 |
Named Entity Recognition for Monitoring Plant Health Threats in Tweets: a ChouBERT Approach | An important application scenario of precision agriculture is detecting and
measuring crop health threats using sensors and data analysis techniques.
However, the textual data are still under-explored among the existing solutions
due to the lack of labelled data and fine-grained semantic resources. Recent
research suggests that the increasing connectivity of farmers and the emergence
of online farming communities make social media like Twitter a participatory
platform for detecting unfamiliar plant health events if we can extract
essential information from unstructured textual data. ChouBERT is a French
pre-trained language model that can identify Tweets concerning observations of
plant health issues with generalizability on unseen natural hazards. This paper
tackles the lack of labelled data by further studying ChouBERT's know-how on
token-level annotation tasks over small labeled sets. | [
"Shufan Jiang",
"Rafael Angarita",
"Stéphane Cormier",
"Francis Rousseaux"
] | 2023-10-19 06:54:55 | http://arxiv.org/abs/2310.12522v1 | http://arxiv.org/pdf/2310.12522v1 | 2310.12522v1 |
Automatic Hallucination Assessment for Aligned Large Language Models via Transferable Adversarial Attacks | Although remarkable progress has been achieved in preventing large language
model (LLM) hallucinations using instruction tuning and retrieval augmentation,
it remains challenging to measure the reliability of LLMs using human-crafted
evaluation data which is not available for many tasks and domains and could
suffer from data leakage. Inspired by adversarial machine learning, this paper
aims to develop a method of automatically generating evaluation data by
appropriately modifying existing data on which LLMs behave faithfully.
Specifically, this paper presents AutoDebug, an LLM-based framework to use
prompting chaining to generate transferable adversarial attacks in the form of
question-answering examples. We seek to understand the extent to which these
examples trigger the hallucination behaviors of LLMs.
We implement AutoDebug using ChatGPT and evaluate the resulting two variants
of a popular open-domain question-answering dataset, Natural Questions (NQ), on
a collection of open-source and proprietary LLMs under various prompting
settings. Our generated evaluation data is human-readable and, as we show,
humans can answer these modified questions well. Nevertheless, we observe
pronounced accuracy drops across multiple LLMs including GPT-4. Our
experimental results show that LLMs are likely to hallucinate in two categories
of question-answering scenarios where (1) there are conflicts between knowledge
given in the prompt and their parametric knowledge, or (2) the knowledge
expressed in the prompt is complex. Finally, we find that the adversarial
examples generated by our method are transferable across all considered LLMs.
The examples generated by a small model can be used to debug a much larger
model, making our approach cost-effective. | [
"Xiaodong Yu",
"Hao Cheng",
"Xiaodong Liu",
"Dan Roth",
"Jianfeng Gao"
] | 2023-10-19 06:37:32 | http://arxiv.org/abs/2310.12516v1 | http://arxiv.org/pdf/2310.12516v1 | 2310.12516v1 |
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt | Continual pre-training has been urgent for adapting a pre-trained model to a
multitude of domains and tasks in the fast-evolving world. In practice, a
continually pre-trained model is expected to demonstrate not only greater
capacity when fine-tuned on pre-trained domains but also a non-decreasing
performance on unseen ones. In this work, we first investigate such anytime
fine-tuning effectiveness of existing continual pre-training approaches,
concluding with unanimously decreased performance on unseen domains. To this
end, we propose a prompt-guided continual pre-training method, where we train a
hypernetwork to generate domain-specific prompts by both agreement and
disagreement losses. The agreement loss maximally preserves the generalization
of a pre-trained model to new domains, and the disagreement one guards the
exclusiveness of the generated hidden states for each domain. Remarkably,
prompts by the hypernetwork alleviate the domain identity when fine-tuning and
promote knowledge transfer across domains. Our method achieved improvements of
3.57% and 3.4% on two real-world datasets (including domain shift and temporal
shift), respectively, demonstrating its efficacy. | [
"Gangwei Jiang",
"Caigao Jiang",
"Siqiao Xue",
"James Y. Zhang",
"Jun Zhou",
"Defu Lian",
"Ying Wei"
] | 2023-10-19 06:34:40 | http://arxiv.org/abs/2310.13024v1 | http://arxiv.org/pdf/2310.13024v1 | 2310.13024v1 |
WeaveNet for Approximating Two-sided Matching Problems | Matching, a task to optimally assign limited resources under constraints, is
a fundamental technology for society. The task potentially has various
objectives, conditions, and constraints; however, the efficient neural network
architecture for matching is underexplored. This paper proposes a novel graph
neural network (GNN), \textit{WeaveNet}, designed for bipartite graphs. Since a
bipartite graph is generally dense, general GNN architectures lose node-wise
information by over-smoothing when deeply stacked. Such a phenomenon is
undesirable for solving matching problems. WeaveNet avoids it by preserving
edge-wise information while passing messages densely to reach a better
solution. To evaluate the model, we approximated one of the \textit{strongly
NP-hard} problems, \textit{fair stable matching}. Despite its inherent
difficulties and the network's general purpose design, our model reached a
comparative performance with state-of-the-art algorithms specially designed for
stable matching for small numbers of agents. | [
"Shusaku Sone",
"Jiaxin Ma",
"Atsushi Hashimoto",
"Naoya Chiba",
"Yoshitaka Ushiku"
] | 2023-10-19 06:32:12 | http://arxiv.org/abs/2310.12515v1 | http://arxiv.org/pdf/2310.12515v1 | 2310.12515v1 |
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation | With evolving data regulations, machine unlearning (MU) has become an
important tool for fostering trust and safety in today's AI models. However,
existing MU methods focusing on data and/or weight perspectives often grapple
with limitations in unlearning accuracy, stability, and cross-domain
applicability. To address these challenges, we introduce the concept of 'weight
saliency' in MU, drawing parallels with input saliency in model explanation.
This innovation directs MU's attention toward specific model weights rather
than the entire model, improving effectiveness and efficiency. The resultant
method that we call saliency unlearning (SalUn) narrows the performance gap
with 'exact' unlearning (model retraining from scratch after removing the
forgetting dataset). To the best of our knowledge, SalUn is the first
principled MU approach adaptable enough to effectively erase the influence of
forgetting data, classes, or concepts in both image classification and
generation. For example, SalUn yields a stability advantage in high-variance
random data forgetting, e.g., with a 0.2% gap compared to exact unlearning on
the CIFAR-10 dataset. Moreover, in preventing conditional diffusion models from
generating harmful images, SalUn achieves nearly 100% unlearning accuracy,
outperforming current state-of-the-art baselines like Erased Stable Diffusion
and Forget-Me-Not. | [
"Chongyu Fan",
"Jiancheng Liu",
"Yihua Zhang",
"Dennis Wei",
"Eric Wong",
"Sijia Liu"
] | 2023-10-19 06:17:17 | http://arxiv.org/abs/2310.12508v1 | http://arxiv.org/pdf/2310.12508v1 | 2310.12508v1 |
Attack Prompt Generation for Red Teaming and Defending Large Language Models | Large language models (LLMs) are susceptible to red teaming attacks, which
can induce LLMs to generate harmful content. Previous research constructs
attack prompts via manual or automatic methods, which have their own
limitations on construction cost and quality. To address these issues, we
propose an integrated approach that combines manual and automatic methods to
economically generate high-quality attack prompts. Specifically, considering
the impressive capabilities of newly emerged LLMs, we propose an attack
framework to instruct LLMs to mimic human-generated prompts through in-context
learning. Furthermore, we propose a defense framework that fine-tunes victim
LLMs through iterative interactions with the attack framework to enhance their
safety against red teaming attacks. Extensive experiments on different LLMs
validate the effectiveness of our proposed attack and defense frameworks.
Additionally, we release a series of attack prompts datasets named SAP with
varying sizes, facilitating the safety evaluation and enhancement of more LLMs.
Our code and dataset is available on https://github.com/Aatrox103/SAP . | [
"Boyi Deng",
"Wenjie Wang",
"Fuli Feng",
"Yang Deng",
"Qifan Wang",
"Xiangnan He"
] | 2023-10-19 06:15:05 | http://arxiv.org/abs/2310.12505v1 | http://arxiv.org/pdf/2310.12505v1 | 2310.12505v1 |
American Option Pricing using Self-Attention GRU and Shapley Value Interpretation | Options, serving as a crucial financial instrument, are used by investors to
manage and mitigate their investment risks within the securities market.
Precisely predicting the present price of an option enables investors to make
informed and efficient decisions. In this paper, we propose a machine learning
method for forecasting the prices of SPY (ETF) option based on gated recurrent
unit (GRU) and self-attention mechanism. We first partitioned the raw dataset
into 15 subsets according to moneyness and days to maturity criteria. For each
subset, we matched the corresponding U.S. government bond rates and Implied
Volatility Indices. This segmentation allows for a more insightful exploration
of the impacts of risk-free rates and underlying volatility on option pricing.
Next, we built four different machine learning models, including multilayer
perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and
self-attention GRU in comparison to the traditional binomial model. The
empirical result shows that self-attention GRU with historical data outperforms
other models due to its ability to capture complex temporal dependencies and
leverage the contextual information embedded in the historical data. Finally,
in order to unveil the "black box" of artificial intelligence, we employed the
SHapley Additive exPlanations (SHAP) method to interpret and analyze the
prediction results of the self-attention GRU model with historical data. This
provides insights into the significance and contributions of different input
features on the pricing of American-style options. | [
"Yanhui Shen"
] | 2023-10-19 06:05:46 | http://arxiv.org/abs/2310.12500v1 | http://arxiv.org/pdf/2310.12500v1 | 2310.12500v1 |
Quasi Manhattan Wasserstein Distance | The Quasi Manhattan Wasserstein Distance (QMWD) is a metric designed to
quantify the dissimilarity between two matrices by combining elements of the
Wasserstein Distance with specific transformations. It offers improved time and
space complexity compared to the Manhattan Wasserstein Distance (MWD) while
maintaining accuracy. QMWD is particularly advantageous for large datasets or
situations with limited computational resources. This article provides a
detailed explanation of QMWD, its computation, complexity analysis, and
comparisons with WD and MWD. | [
"Evan Unit Lim"
] | 2023-10-19 06:04:48 | http://arxiv.org/abs/2310.12498v1 | http://arxiv.org/pdf/2310.12498v1 | 2310.12498v1 |
SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models | Understanding the long-term impact of algorithmic interventions on society is
vital to achieving responsible AI. Traditional evaluation strategies often fall
short due to the complex, adaptive and dynamic nature of society. While
reinforcement learning (RL) can be a powerful approach for optimizing decisions
in dynamic settings, the difficulty of realistic environment design remains a
barrier to building robust agents that perform well in practical settings. To
address this issue we tap into the field of system dynamics (SD) as a
complementary method that incorporates collaborative simulation model
specification practices. We introduce SDGym, a low-code library built on the
OpenAI Gym framework which enables the generation of custom RL environments
based on SD simulation models. Through a feasibility study we validate that
well specified, rich RL environments can be generated from preexisting SD
models and a few lines of configuration code. We demonstrate the capabilities
of the SDGym environment using an SD model of the electric vehicle adoption
problem. We compare two SD simulators, PySD and BPTK-Py for parity, and train a
D4PG agent using the Acme framework to showcase learning and environment
interaction. Our preliminary findings underscore the dual potential of SD to
improve RL environment design and for RL to improve dynamic policy discovery
within SD models. By open-sourcing SDGym, the intent is to galvanize further
research and promote adoption across the SD and RL communities, thereby
catalyzing collaboration in this emerging interdisciplinary space. | [
"Emmanuel Klu",
"Sameer Sethi",
"DJ Passey",
"Donald Martin Jr"
] | 2023-10-19 05:56:25 | http://arxiv.org/abs/2310.12494v1 | http://arxiv.org/pdf/2310.12494v1 | 2310.12494v1 |
Improved Operator Learning by Orthogonal Attention | Neural operators, as an efficient surrogate model for learning the solutions
of PDEs, have received extensive attention in the field of scientific machine
learning. Among them, attention-based neural operators have become one of the
mainstreams in related research. However, existing approaches overfit the
limited training data due to the considerable number of parameters in the
attention mechanism. To address this, we develop an orthogonal attention based
on the eigendecomposition of the kernel integral operator and the neural
approximation of eigenfunctions. The orthogonalization naturally poses a proper
regularization effect on the resulting neural operator, which aids in resisting
overfitting and boosting generalization. Experiments on six standard neural
operator benchmark datasets comprising both regular and irregular geometries
show that our method can outperform competing baselines with decent margins. | [
"Zipeng Xiao",
"Zhongkai Hao",
"Bokai Lin",
"Zhijie Deng",
"Hang Su"
] | 2023-10-19 05:47:28 | http://arxiv.org/abs/2310.12487v2 | http://arxiv.org/pdf/2310.12487v2 | 2310.12487v2 |
Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights | In the realm of deep learning, transformers have emerged as a dominant
architecture, particularly in natural language processing tasks. However, with
their widespread adoption, concerns regarding the security and privacy of the
data processed by these models have arisen. In this paper, we address a pivotal
question: Can the data fed into transformers be recovered using their attention
weights and outputs? We introduce a theoretical framework to tackle this
problem. Specifically, we present an algorithm that aims to recover the input
data $X \in \mathbb{R}^{d \times n}$ from given attention weights $W = QK^\top
\in \mathbb{R}^{d \times d}$ and output $B \in \mathbb{R}^{n \times n}$ by
minimizing the loss function $L(X)$. This loss function captures the
discrepancy between the expected output and the actual output of the
transformer. Our findings have significant implications for the Localized
Layer-wise Mechanism (LLM), suggesting potential vulnerabilities in the model's
design from a security and privacy perspective. This work underscores the
importance of understanding and safeguarding the internal workings of
transformers to ensure the confidentiality of processed data. | [
"Yichuan Deng",
"Zhao Song",
"Shenghao Xie",
"Chiwun Yang"
] | 2023-10-19 04:41:01 | http://arxiv.org/abs/2310.12462v1 | http://arxiv.org/pdf/2310.12462v1 | 2310.12462v1 |
Balanced Group Convolution: An Improved Group Convolution Based on Approximability Estimates | The performance of neural networks has been significantly improved by
increasing the number of channels in convolutional layers. However, this
increase in performance comes with a higher computational cost, resulting in
numerous studies focused on reducing it. One promising approach to address this
issue is group convolution, which effectively reduces the computational cost by
grouping channels. However, to the best of our knowledge, there has been no
theoretical analysis on how well the group convolution approximates the
standard convolution. In this paper, we mathematically analyze the
approximation of the group convolution to the standard convolution with respect
to the number of groups. Furthermore, we propose a novel variant of the group
convolution called balanced group convolution, which shows a higher
approximation with a small additional computational cost. We provide
experimental results that validate our theoretical findings and demonstrate the
superior performance of the balanced group convolution over other variants of
group convolution. | [
"Youngkyu Lee",
"Jongho Park",
"Chang-Ock Lee"
] | 2023-10-19 04:39:38 | http://arxiv.org/abs/2310.12461v1 | http://arxiv.org/pdf/2310.12461v1 | 2310.12461v1 |
MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale | Among the many variants of graph neural network (GNN) architectures capable
of modeling data with cross-instance relations, an important subclass involves
layers designed such that the forward pass iteratively reduces a
graph-regularized energy function of interest. In this way, node embeddings
produced at the output layer dually serve as both predictive features for
solving downstream tasks (e.g., node classification) and energy function
minimizers that inherit desirable inductive biases and interpretability.
However, scaling GNN architectures constructed in this way remains challenging,
in part because the convergence of the forward pass may involve models with
considerable depth. To tackle this limitation, we propose a sampling-based
energy function and scalable GNN layers that iteratively reduce it, guided by
convergence guarantees in certain settings. We also instantiate a full GNN
architecture based on these designs, and the model achieves competitive
accuracy and scalability when applied to the largest publicly-available node
classification benchmark exceeding 1TB in size. | [
"Haitian Jiang",
"Renjie Liu",
"Xiao Yan",
"Zhenkun Cai",
"Minjie Wang",
"David Wipf"
] | 2023-10-19 04:30:14 | http://arxiv.org/abs/2310.12457v1 | http://arxiv.org/pdf/2310.12457v1 | 2310.12457v1 |
MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant Features | Medical time series data are indispensable in healthcare, providing critical
insights for disease diagnosis, treatment planning, and patient management. The
exponential growth in data complexity, driven by advanced sensor technologies,
has presented challenges related to data labeling. Self-supervised learning
(SSL) has emerged as a transformative approach to address these challenges,
eliminating the need for extensive human annotation. In this study, we
introduce a novel framework for Medical Time Series Representation Learning,
known as MTS-LOF. MTS-LOF leverages the strengths of contrastive learning and
Masked Autoencoder (MAE) methods, offering a unique approach to representation
learning for medical time series data. By combining these techniques, MTS-LOF
enhances the potential of healthcare applications by providing more
sophisticated, context-rich representations. Additionally, MTS-LOF employs a
multi-masking strategy to facilitate occlusion-invariant feature learning. This
approach allows the model to create multiple views of the data by masking
portions of it. By minimizing the discrepancy between the representations of
these masked patches and the fully visible patches, MTS-LOF learns to capture
rich contextual information within medical time series datasets. The results of
experiments conducted on diverse medical time series datasets demonstrate the
superiority of MTS-LOF over other methods. These findings hold promise for
significantly enhancing healthcare applications by improving representation
learning. Furthermore, our work delves into the integration of joint-embedding
SSL and MAE techniques, shedding light on the intricate interplay between
temporal and structural dependencies in healthcare data. This understanding is
crucial, as it allows us to grasp the complexities of healthcare data analysis. | [
"Huayu Li",
"Ana S. Carreon-Rascon",
"Xiwen Chen",
"Geng Yuan",
"Ao Li"
] | 2023-10-19 04:08:19 | http://arxiv.org/abs/2310.12451v1 | http://arxiv.org/pdf/2310.12451v1 | 2310.12451v1 |
Constrained Reweighting of Distributions: an Optimal Transport Approach | We commonly encounter the problem of identifying an optimally weight adjusted
version of the empirical distribution of observed data, adhering to predefined
constraints on the weights. Such constraints often manifest as restrictions on
the moments, tail behaviour, shapes, number of modes, etc., of the resulting
weight adjusted empirical distribution. In this article, we substantially
enhance the flexibility of such methodology by introducing a nonparametrically
imbued distributional constraints on the weights, and developing a general
framework leveraging the maximum entropy principle and tools from optimal
transport. The key idea is to ensure that the maximum entropy weight adjusted
empirical distribution of the observed data is close to a pre-specified
probability distribution in terms of the optimal transport metric while
allowing for subtle departures. The versatility of the framework is
demonstrated in the context of three disparate applications where data
re-weighting is warranted to satisfy side constraints on the optimization
problem at the heart of the statistical task: namely, portfolio allocation,
semi-parametric inference for complex surveys, and ensuring algorithmic
fairness in machine learning algorithms. | [
"Abhisek Chakraborty",
"Anirban Bhattacharya",
"Debdeep Pati"
] | 2023-10-19 03:54:31 | http://arxiv.org/abs/2310.12447v1 | http://arxiv.org/pdf/2310.12447v1 | 2310.12447v1 |
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer | Pretrained transformer models have demonstrated remarkable performance across
various natural language processing tasks. These models leverage the attention
mechanism to capture long- and short-range dependencies in the sequence.
However, the (full) attention mechanism incurs high computational cost -
quadratic in the sequence length, which is not affordable in tasks with long
sequences, e.g., inputs with 8k tokens. Although sparse attention can be used
to improve computational efficiency, as suggested in existing work, it has
limited modeling capacity and often fails to capture complicated dependencies
in long sequences. To tackle this challenge, we propose MASFormer, an
easy-to-implement transformer variant with Mixed Attention Spans. Specifically,
MASFormer is equipped with full attention to capture long-range dependencies,
but only at a small number of layers. For the remaining layers, MASformer only
employs sparse attention to capture short-range dependencies. Our experiments
on natural language modeling and generation tasks show that a decoder-only
MASFormer model of 1.3B parameters can achieve competitive performance to
vanilla transformers with full attention while significantly reducing
computational cost (up to 75%). Additionally, we investigate the effectiveness
of continual training with long sequence data and how sequence length impacts
downstream generation performance, which may be of independent interest. | [
"Qingru Zhang",
"Dhananjay Ram",
"Cole Hawkins",
"Sheng Zha",
"Tuo Zhao"
] | 2023-10-19 03:32:05 | http://arxiv.org/abs/2310.12442v1 | http://arxiv.org/pdf/2310.12442v1 | 2310.12442v1 |
CAT: Closed-loop Adversarial Training for Safe End-to-End Driving | Driving safety is a top priority for autonomous vehicles. Orthogonal to prior
work handling accident-prone traffic events by algorithm designs at the policy
level, we investigate a Closed-loop Adversarial Training (CAT) framework for
safe end-to-end driving in this paper through the lens of environment
augmentation. CAT aims to continuously improve the safety of driving agents by
training the agent on safety-critical scenarios that are dynamically generated
over time. A novel resampling technique is developed to turn log-replay
real-world driving scenarios into safety-critical ones via probabilistic
factorization, where the adversarial traffic generation is modeled as the
multiplication of standard motion prediction sub-problems. Consequently, CAT
can launch more efficient physical attacks compared to existing safety-critical
scenario generation methods and yields a significantly less computational cost
in the iterative learning pipeline. We incorporate CAT into the MetaDrive
simulator and validate our approach on hundreds of driving scenarios imported
from real-world driving datasets. Experimental results demonstrate that CAT can
effectively generate adversarial scenarios countering the agent being trained.
After training, the agent can achieve superior driving safety in both
log-replay and safety-critical traffic scenarios on the held-out test set. Code
and data are available at https://metadriverse.github.io/cat. | [
"Linrui Zhang",
"Zhenghao Peng",
"Quanyi Li",
"Bolei Zhou"
] | 2023-10-19 02:49:31 | http://arxiv.org/abs/2310.12432v1 | http://arxiv.org/pdf/2310.12432v1 | 2310.12432v1 |
Towards Enhanced Local Explainability of Random Forests: a Proximity-Based Approach | We initiate a novel approach to explain the out of sample performance of
random forest (RF) models by exploiting the fact that any RF can be formulated
as an adaptive weighted K nearest-neighbors model. Specifically, we use the
proximity between points in the feature space learned by the RF to re-write
random forest predictions exactly as a weighted average of the target labels of
training data points. This linearity facilitates a local notion of
explainability of RF predictions that generates attributions for any model
prediction across observations in the training set, and thereby complements
established methods like SHAP, which instead generates attributions for a model
prediction across dimensions of the feature space. We demonstrate this approach
in the context of a bond pricing model trained on US corporate bond trades, and
compare our approach to various existing approaches to model explainability. | [
"Joshua Rosaler",
"Dhruv Desai",
"Bhaskarjit Sarmah",
"Dimitrios Vamvourellis",
"Deran Onay",
"Dhagash Mehta",
"Stefano Pasquali"
] | 2023-10-19 02:42:20 | http://arxiv.org/abs/2310.12428v1 | http://arxiv.org/pdf/2310.12428v1 | 2310.12428v1 |
Automated Repair of Declarative Software Specifications in the Era of Large Language Models | The growing adoption of declarative software specification languages, coupled
with their inherent difficulty in debugging, has underscored the need for
effective and automated repair techniques applicable to such languages.
Researchers have recently explored various methods to automatically repair
declarative software specifications, such as template-based repair,
feedback-driven iterative repair, and bounded exhaustive approaches. The latest
developments in large language models provide new opportunities for the
automatic repair of declarative specifications. In this study, we assess the
effectiveness of utilizing OpenAI's ChatGPT to repair software specifications
written in the Alloy declarative language. Unlike imperative languages,
specifications in Alloy are not executed but rather translated into logical
formulas and evaluated using backend constraint solvers to identify
specification instances and counterexamples to assertions. Our evaluation
focuses on ChatGPT's ability to improve the correctness and completeness of
Alloy declarative specifications through automatic repairs. We analyze the
results produced by ChatGPT and compare them with those of leading automatic
Alloy repair methods. Our study revealed that while ChatGPT falls short in
comparison to existing techniques, it was able to successfully repair bugs that
no other technique could address. Our analysis also identified errors in
ChatGPT's generated repairs, including improper operator usage, type errors,
higher-order logic misuse, and relational arity mismatches. Additionally, we
observed instances of hallucinations in ChatGPT-generated repairs and
inconsistency in its results. Our study provides valuable insights for software
practitioners, researchers, and tool builders considering ChatGPT for
declarative specification repairs. | [
"Md Rashedul Hasan",
"Jiawei Li",
"Iftekhar Ahmed",
"Hamid Bagheri"
] | 2023-10-19 02:30:42 | http://arxiv.org/abs/2310.12425v1 | http://arxiv.org/pdf/2310.12425v1 | 2310.12425v1 |
Detecting and Mitigating Algorithmic Bias in Binary Classification using Causal Modeling | This paper proposes the use of causal modeling to detect and mitigate
algorithmic bias. We provide a brief description of causal modeling and a
general overview of our approach. We then use the Adult dataset, which is
available for download from the UC Irvine Machine Learning Repository, to
develop (1) a prediction model, which is treated as a black box, and (2) a
causal model for bias mitigation. In this paper, we focus on gender bias and
the problem of binary classification. We show that gender bias in the
prediction model is statistically significant at the 0.05 level. We demonstrate
the effectiveness of the causal model in mitigating gender bias by
cross-validation. Furthermore, we show that the overall classification accuracy
is improved slightly. Our novel approach is intuitive, easy-to-use, and can be
implemented using existing statistical software tools such as "lavaan" in R.
Hence, it enhances explainability and promotes trust. | [
"Wendy Hui",
"Wai Kwong Lau"
] | 2023-10-19 02:21:04 | http://arxiv.org/abs/2310.12421v1 | http://arxiv.org/pdf/2310.12421v1 | 2310.12421v1 |
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding | The recent success of large pre-trained language models (PLMs) heavily hinges
on massive labeled data, which typically produces inferior performance in
low-resource scenarios. To remedy this dilemma, we study self-training as one
of the predominant semi-supervised learning (SSL) approaches, which utilizes
large-scale unlabeled data to generate synthetic examples. However, too many
noisy labels will hurt the model performance, and the self-training procedure
requires multiple training iterations making it more expensive if all the model
parameters of the PLM are updated. This paper presents UPET, a novel
Uncertainty-aware Parameter-Efficient self-Training framework to effectively
and efficiently address the labeled data scarcity issue. Specifically, we
incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to
perform uncertainty estimation for the teacher model and then judiciously
select reliable pseudo-labeled examples based on confidence and certainty.
During the student training, we introduce multiple parameter-efficient learning
(PEL) paradigms that allow the optimization of only a small percentage of
parameters. We also propose a novel Easy-Hard Contrastive Tuning to enhance the
robustness and generalization. Extensive experiments over multiple downstream
tasks demonstrate that UPET achieves a substantial improvement in terms of
performance and efficiency. Our codes and data are released at https:
//github.com/wjn1996/UPET. | [
"Jianing Wang",
"Qiushi Sun",
"Nuo Chen",
"Chengyu Wang",
"Jun Huang",
"Ming Gao",
"Xiang Li"
] | 2023-10-19 02:18:29 | http://arxiv.org/abs/2310.13022v1 | http://arxiv.org/pdf/2310.13022v1 | 2310.13022v1 |
Provable Guarantees for Neural Networks via Gradient Feature Learning | Neural networks have achieved remarkable empirical performance, while the
current theoretical analysis is not adequate for understanding their success,
e.g., the Neural Tangent Kernel approach fails to capture their key feature
learning ability, while recent analyses on feature learning are typically
problem-specific. This work proposes a unified analysis framework for two-layer
networks trained by gradient descent. The framework is centered around the
principle of feature learning from gradients, and its effectiveness is
demonstrated by applications in several prototypical problems, such as mixtures
of Gaussians and parity functions. The framework also sheds light on
interesting network learning phenomena such as feature learning beyond kernels
and the lottery ticket hypothesis. | [
"Zhenmei Shi",
"Junyi Wei",
"Yingyu Liang"
] | 2023-10-19 01:45:37 | http://arxiv.org/abs/2310.12408v1 | http://arxiv.org/pdf/2310.12408v1 | 2310.12408v1 |
Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing | We address the challenge of tracking an unknown number of targets in strong
clutter environments using measurements from a radar sensor. Leveraging the
range-Doppler spectra information, we identify the measurement classes, which
serve as additional information to enhance clutter rejection and data
association, thus bolstering the robustness of target tracking. We first
introduce a novel neural enhanced message passing approach, where the beliefs
obtained by the unified message passing are fed into the neural network as
additional information. The output beliefs are then utilized to refine the
original beliefs. Then, we propose a classification-aided robust multiple
target tracking algorithm, employing the neural enhanced message passing
technique. This algorithm is comprised of three modules: a message-passing
module, a neural network module, and a Dempster-Shafer module. The
message-passing module is used to represent the statistical model by the factor
graph and infers target kinematic states, visibility states, and data
associations based on the spatial measurement information. The neural network
module is employed to extract features from range-Doppler spectra and derive
beliefs on whether a measurement is target-generated or clutter-generated. The
Dempster-Shafer module is used to fuse the beliefs obtained from both the
factor graph and the neural network. As a result, our proposed algorithm adopts
a model-and-data-driven framework, effectively enhancing clutter suppression
and data association, leading to significant improvements in multiple target
tracking performance. We validate the effectiveness of our approach using both
simulated and real data scenarios, demonstrating its capability to handle
challenging tracking scenarios in practical radar applications. | [
"Xianglong Bai",
"Zengfu Wang",
"Quan Pan",
"Tao Yun",
"Hua Lan"
] | 2023-10-19 01:41:11 | http://arxiv.org/abs/2310.12407v1 | http://arxiv.org/pdf/2310.12407v1 | 2310.12407v1 |
Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing | Creating music is iterative, requiring varied methods at each stage. However,
existing AI music systems fall short in orchestrating multiple subsystems for
diverse needs. To address this gap, we introduce Loop Copilot, a novel system
that enables users to generate and iteratively refine music through an
interactive, multi-round dialogue interface. The system uses a large language
model to interpret user intentions and select appropriate AI models for task
execution. Each backend model is specialized for a specific task, and their
outputs are aggregated to meet the user's requirements. To ensure musical
coherence, essential attributes are maintained in a centralized table. We
evaluate the effectiveness of the proposed system through semi-structured
interviews and questionnaires, highlighting its utility not only in
facilitating music creation but also its potential for broader applications. | [
"Yixiao Zhang",
"Akira Maezawa",
"Gus Xia",
"Kazuhiko Yamamoto",
"Simon Dixon"
] | 2023-10-19 01:20:12 | http://arxiv.org/abs/2310.12404v1 | http://arxiv.org/pdf/2310.12404v1 | 2310.12404v1 |
Cooperative Minibatching in Graph Neural Networks | Significant computational resources are required to train Graph Neural
Networks (GNNs) at a large scale, and the process is highly data-intensive. One
of the most effective ways to reduce resource requirements is minibatch
training coupled with graph sampling. GNNs have the unique property that items
in a minibatch have overlapping data. However, the commonly implemented
Independent Minibatching approach assigns each Processing Element (PE) its own
minibatch to process, leading to duplicated computations and input data access
across PEs. This amplifies the Neighborhood Explosion Phenomenon (NEP), which
is the main bottleneck limiting scaling. To reduce the effects of NEP in the
multi-PE setting, we propose a new approach called Cooperative Minibatching.
Our approach capitalizes on the fact that the size of the sampled subgraph is a
concave function of the batch size, leading to significant reductions in the
amount of work per seed vertex as batch sizes increase. Hence, it is favorable
for processors equipped with a fast interconnect to work on a large minibatch
together as a single larger processor, instead of working on separate smaller
minibatches, even though global batch size is identical. We also show how to
take advantage of the same phenomenon in serial execution by generating
dependent consecutive minibatches. Our experimental evaluations show up to 4x
bandwidth savings for fetching vertex embeddings, by simply increasing this
dependency without harming model convergence. Combining our proposed
approaches, we achieve up to 64% speedup over Independent Minibatching on
single-node multi-GPU systems. | [
"Muhammed Fatih Balin",
"Dominique LaSalle",
"Ümit V. Çatalyürek"
] | 2023-10-19 01:15:24 | http://arxiv.org/abs/2310.12403v2 | http://arxiv.org/pdf/2310.12403v2 | 2310.12403v2 |
Closed-Form Diffusion Models | Score-based generative models (SGMs) sample from a target distribution by
iteratively transforming noise using the score function of the perturbed
target. For any finite training set, this score function can be evaluated in
closed form, but the resulting SGM memorizes its training data and does not
generate novel samples. In practice, one approximates the score by training a
neural network via score-matching. The error in this approximation promotes
generalization, but neural SGMs are costly to train and sample, and the
effective regularization this error provides is not well-understood
theoretically. In this work, we instead explicitly smooth the closed-form score
to obtain an SGM that generates novel samples without training. We analyze our
model and propose an efficient nearest-neighbor-based estimator of its score
function. Using this estimator, our method achieves sampling times competitive
with neural SGMs while running on consumer-grade CPUs. | [
"Christopher Scarvelis",
"Haitz Sáez de Ocáriz Borde",
"Justin Solomon"
] | 2023-10-19 00:45:05 | http://arxiv.org/abs/2310.12395v1 | http://arxiv.org/pdf/2310.12395v1 | 2310.12395v1 |
Learning to Solve Climate Sensor Placement Problems with a Transformer | The optimal placement of sensors for environmental monitoring and disaster
management is a challenging problem due to its NP-hard nature. Traditional
methods for sensor placement involve exact, approximation, or heuristic
approaches, with the latter being the most widely used. However, heuristic
methods are limited by expert intuition and experience. Deep learning (DL) has
emerged as a promising approach for generating heuristic algorithms
automatically. In this paper, we introduce a novel sensor placement approach
focused on learning improvement heuristics using deep reinforcement learning
(RL) methods. Our approach leverages an RL formulation for learning improvement
heuristics, driven by an actor-critic algorithm for training the policy
network. We compare our method with several state-of-the-art approaches by
conducting comprehensive experiments, demonstrating the effectiveness and
superiority of our proposed approach in producing high-quality solutions. Our
work presents a promising direction for applying advanced DL and RL techniques
to challenging climate sensor placement problems. | [
"Chen Wang",
"Victoria Huang",
"Gang Chen",
"Hui Ma",
"Bryce Chen",
"Jochen Schmidt"
] | 2023-10-18 23:58:54 | http://arxiv.org/abs/2310.12387v1 | http://arxiv.org/pdf/2310.12387v1 | 2310.12387v1 |
No-Regret Learning in Bilateral Trade via Global Budget Balance | Bilateral trade revolves around the challenge of facilitating transactions
between two strategic agents -- a seller and a buyer -- both of whom have a
private valuations for the item. We study the online version of the problem, in
which at each time step a new seller and buyer arrive. The learner's task is to
set a price for each agent, without any knowledge about their valuations. The
sequence of sellers and buyers is chosen by an oblivious adversary. In this
setting, known negative results rule out the possibility of designing
algorithms with sublinear regret when the learner has to guarantee budget
balance for each iteration. In this paper, we introduce the notion of global
budget balance, which requires the agent to be budget balance only over the
entire time horizon. By requiring global budget balance, we provide the first
no-regret algorithms for bilateral trade with adversarial inputs under various
feedback models. First, we show that in the full-feedback model the learner can
guarantee $\tilde{O}(\sqrt{T})$ regret against the best fixed prices in
hindsight, which is order-wise optimal. Then, in the case of partial feedback
models, we provide an algorithm guaranteeing a $\tilde{O}(T^{3/4})$ regret
upper bound with one-bit feedback, which we complement with a nearly-matching
lower bound. Finally, we investigate how these results vary when measuring
regret using an alternative benchmark. | [
"Martino Bernasconi",
"Matteo Castiglioni",
"Andrea Celli",
"Federico Fusco"
] | 2023-10-18 22:34:32 | http://arxiv.org/abs/2310.12370v1 | http://arxiv.org/pdf/2310.12370v1 | 2310.12370v1 |
MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits | Variable speed limit (VSL) control is a promising traffic management strategy
for enhancing safety and mobility. This work introduces MARVEL, a multi-agent
reinforcement learning (MARL) framework for implementing large-scale VSL
control on freeway corridors using only commonly available data. The agents
learn through a reward structure that incorporates adaptability to traffic
conditions, safety, and mobility; enabling coordination among the agents. The
proposed framework scales to cover corridors with many gantries thanks to a
parameter sharing among all VSL agents. The agents are trained in a
microsimulation environment based on a short freeway stretch with 8 gantries
spanning 7 miles and tested with 34 gantries spanning 17 miles of I-24 near
Nashville, TN. MARVEL improves traffic safety by 63.4% compared to the no
control scenario and enhances traffic mobility by 14.6% compared to a
state-of-the-practice algorithm that has been deployed on I-24. An
explainability analysis is undertaken to explore the learned policy under
different traffic conditions and the results provide insights into the
decision-making process of agents. Finally, we test the policy learned from the
simulation-based experiments on real input data from I-24 to illustrate the
potential deployment capability of the learned policy. | [
"Yuhang Zhang",
"Marcos Quinones-Grueiro",
"Zhiyao Zhang",
"Yanbing Wang",
"William Barbour",
"Gautam Biswas",
"Daniel Work"
] | 2023-10-18 22:09:29 | http://arxiv.org/abs/2310.12359v1 | http://arxiv.org/pdf/2310.12359v1 | 2310.12359v1 |
Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach | Traffic state forecasting is crucial for traffic management and control
strategies, as well as user- and system-level decision making in the
transportation network. While traffic forecasting has been approached with a
variety of techniques over the last couple of decades, most approaches simply
rely on endogenous traffic variables for state prediction, despite the evidence
that exogenous factors can significantly impact traffic conditions. This paper
proposes a multi-dimensional spatio-temporal graph attention-based traffic
prediction approach (M-STGAT), which predicts traffic based on past
observations of speed, along with lane closure events, temperature, and
visibility across the transportation network. The approach is based on a graph
attention network architecture, which also learns based on the structure of the
transportation network on which these variables are observed. Numerical
experiments are performed using traffic speed and lane closure data from the
California Department of Transportation (Caltrans) Performance Measurement
System (PeMS). The corresponding weather data were downloaded from the National
Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing
Systems (ASOS). For comparison, the numerical experiments implement three
alternative models which do not allow for the multi-dimensional input. The
M-STGAT is shown to outperform the three alternative models, when performing
tests using our primary data set for prediction with a 30-, 45-, and 60-minute
prediction horizon, in terms of three error measures: Mean Absolute Error
(MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).
However, the model's transferability can vary for different transfer data sets
and this aspect may require further investigation. | [
"Syed Islam",
"Monika Filipovska"
] | 2023-10-18 21:57:20 | http://arxiv.org/abs/2310.12353v1 | http://arxiv.org/pdf/2310.12353v1 | 2310.12353v1 |
Equipping Federated Graph Neural Networks with Structure-aware Group Fairness | Graph Neural Networks (GNNs) have been widely used for various types of graph
data processing and analytical tasks in different domains. Training GNNs over
centralized graph data can be infeasible due to privacy concerns and regulatory
restrictions. Thus, federated learning (FL) becomes a trending solution to
address this challenge in a distributed learning paradigm. However, as GNNs may
inherit historical bias from training data and lead to discriminatory
predictions, the bias of local models can be easily propagated to the global
model in distributed settings. This poses a new challenge in mitigating bias in
federated GNNs. To address this challenge, we propose $\text{F}^2$GNN, a Fair
Federated Graph Neural Network, that enhances group fairness of federated GNNs.
As bias can be sourced from both data and learning algorithms, $\text{F}^2$GNN
aims to mitigate both types of bias under federated settings. First, we provide
theoretical insights on the connection between data bias in a training graph
and statistical fairness metrics of the trained GNN models. Based on the
theoretical analysis, we design $\text{F}^2$GNN which contains two key
components: a fairness-aware local model update scheme that enhances group
fairness of the local models on the client side, and a fairness-weighted global
model update scheme that takes both data bias and fairness metrics of local
models into consideration in the aggregation process. We evaluate
$\text{F}^2$GNN empirically versus a number of baseline methods, and
demonstrate that $\text{F}^2$GNN outperforms these baselines in terms of both
fairness and model accuracy. | [
"Nan Cui",
"Xiuling Wang",
"Wendy Hui Wang",
"Violet Chen",
"Yue Ning"
] | 2023-10-18 21:51:42 | http://arxiv.org/abs/2310.12350v1 | http://arxiv.org/pdf/2310.12350v1 | 2310.12350v1 |
Tracking electricity losses and their perceived causes using nighttime light and social media | Urban environments are intricate systems where the breakdown of critical
infrastructure can impact both the economic and social well-being of
communities. Electricity systems hold particular significance, as they are
essential for other infrastructure, and disruptions can trigger widespread
consequences. Typically, assessing electricity availability requires
ground-level data, a challenge in conflict zones and regions with limited
access. This study shows how satellite imagery, social media, and information
extraction can monitor blackouts and their perceived causes. Night-time light
data (in March 2019 for Caracas, Venezuela) is used to indicate blackout
regions. Twitter data is used to determine sentiment and topic trends, while
statistical analysis and topic modeling delved into public perceptions
regarding blackout causes. The findings show an inverse relationship between
nighttime light intensity. Tweets mentioning the Venezuelan President displayed
heightened negativity and a greater prevalence of blame-related terms,
suggesting a perception of government accountability for the outages. | [
"Samuel W Kerber",
"Nicholas A Duncan",
"Guillaume F LHer",
"Morgan Bazilian",
"Chris Elvidge",
"Mark R Deinert"
] | 2023-10-18 21:44:39 | http://arxiv.org/abs/2310.12346v1 | http://arxiv.org/pdf/2310.12346v1 | 2310.12346v1 |
ClusT3: Information Invariant Test-Time Training | Deep Learning models have shown remarkable performance in a broad range of
vision tasks. However, they are often vulnerable against domain shifts at
test-time. Test-time training (TTT) methods have been developed in an attempt
to mitigate these vulnerabilities, where a secondary task is solved at training
time simultaneously with the main task, to be later used as an self-supervised
proxy task at test-time. In this work, we propose a novel unsupervised TTT
technique based on the maximization of Mutual Information between multi-scale
feature maps and a discrete latent representation, which can be integrated to
the standard training as an auxiliary clustering task. Experimental results
demonstrate competitive classification performance on different popular
test-time adaptation benchmarks. | [
"Gustavo A. Vargas Hakim",
"David Osowiechi",
"Mehrdad Noori",
"Milad Cheraghalikhani",
"Ismail Ben Ayed",
"Christian Desrosiers"
] | 2023-10-18 21:43:37 | http://arxiv.org/abs/2310.12345v1 | http://arxiv.org/pdf/2310.12345v1 | 2310.12345v1 |
Opportunities for Adaptive Experiments to Enable Continuous Improvement that Trades-off Instructor and Researcher Incentives | Randomized experimental comparisons of alternative pedagogical strategies
could provide useful empirical evidence in instructors' decision-making.
However, traditional experiments do not have a clear and simple pathway to
using data rapidly to try to increase the chances that students in an
experiment get the best conditions. Drawing inspiration from the use of machine
learning and experimentation in product development at leading technology
companies, we explore how adaptive experimentation might help in continuous
course improvement. In adaptive experiments, as different arms/conditions are
deployed to students, data is analyzed and used to change the experience for
future students. This can be done using machine learning algorithms to identify
which actions are more promising for improving student experience or outcomes.
This algorithm can then dynamically deploy the most effective conditions to
future students, resulting in better support for students' needs. We illustrate
the approach with a case study providing a side-by-side comparison of
traditional and adaptive experimentation of self-explanation prompts in online
homework problems in a CS1 course. This provides a first step in exploring the
future of how this methodology can be useful in bridging research and practice
in doing continuous improvement. | [
"Ilya Musabirov",
"Angela Zavaleta-Bernuy",
"Pan Chen",
"Michael Liut",
"Joseph Jay Williams"
] | 2023-10-18 20:54:59 | http://arxiv.org/abs/2310.12324v1 | http://arxiv.org/pdf/2310.12324v1 | 2310.12324v1 |
A Unifying Framework for Learning Argumentation Semantics | Argumentation is a very active research field of Artificial Intelligence
concerned with the representation and evaluation of arguments used in dialogues
between humans and/or artificial agents. Acceptability semantics of formal
argumentation systems define the criteria for the acceptance or rejection of
arguments. Several software systems, known as argumentation solvers, have been
developed to compute the accepted/rejected arguments using such criteria. These
include systems that learn to identify the accepted arguments using
non-interpretable methods. In this paper we present a novel framework, which
uses an Inductive Logic Programming approach to learn the acceptability
semantics for several abstract and structured argumentation frameworks in an
interpretable way. Through an empirical evaluation we show that our framework
outperforms existing argumentation solvers, thus opening up new future research
directions in the area of formal argumentation and human-machine dialogues. | [
"Zlatina Mileva",
"Antonis Bikakis",
"Fabio Aurelio D'Asaro",
"Mark Law",
"Alessandra Russo"
] | 2023-10-18 20:18:05 | http://arxiv.org/abs/2310.12309v1 | http://arxiv.org/pdf/2310.12309v1 | 2310.12309v1 |
Preference Optimization for Molecular Language Models | Molecular language modeling is an effective approach to generating novel
chemical structures. However, these models do not \emph{a priori} encode
certain preferences a chemist may desire. We investigate the use of fine-tuning
using Direct Preference Optimization to better align generated molecules with
chemist preferences. Our findings suggest that this approach is simple,
efficient, and highly effective. | [
"Ryan Park",
"Ryan Theisen",
"Navriti Sahni",
"Marcel Patek",
"Anna Cichońska",
"Rayees Rahman"
] | 2023-10-18 20:11:46 | http://arxiv.org/abs/2310.12304v1 | http://arxiv.org/pdf/2310.12304v1 | 2310.12304v1 |
Document-Level Language Models for Machine Translation | Despite the known limitations, most machine translation systems today still
operate on the sentence-level. One reason for this is, that most parallel
training data is only sentence-level aligned, without document-level meta
information available. In this work, we set out to build context-aware
translation systems utilizing document-level monolingual data instead. This can
be achieved by combining any existing sentence-level translation model with a
document-level language model. We improve existing approaches by leveraging
recent advancements in model combination. Additionally, we propose novel
weighting techniques that make the system combination more flexible and
significantly reduce computational overhead. In a comprehensive evaluation on
four diverse translation tasks, we show that our extensions improve
document-targeted scores substantially and are also computationally more
efficient. However, we also find that in most scenarios, back-translation gives
even better results, at the cost of having to re-train the translation system.
Finally, we explore language model fusion in the light of recent advancements
in large language models. Our findings suggest that there might be strong
potential in utilizing large language models via model combination. | [
"Frithjof Petrick",
"Christian Herold",
"Pavel Petrushkov",
"Shahram Khadivi",
"Hermann Ney"
] | 2023-10-18 20:10:07 | http://arxiv.org/abs/2310.12303v1 | http://arxiv.org/pdf/2310.12303v1 | 2310.12303v1 |
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization | Despite their better convergence properties compared to first-order
optimizers, second-order optimizers for deep learning have been less popular
due to their significant computational costs. The primary efficiency bottleneck
in such optimizers is matrix inverse calculations in the preconditioning step,
which are expensive to compute on GPUs. In this paper, we introduce Jorge, a
second-order optimizer that promises the best of both worlds -- rapid
convergence benefits of second-order methods, and high computational efficiency
typical of first-order methods. We address the primary computational bottleneck
of computing matrix inverses by completely eliminating them using an
approximation of the preconditioner computation. This makes Jorge extremely
efficient on GPUs in terms of wall-clock time. Further, we describe an approach
to determine Jorge's hyperparameters directly from a well-tuned SGD baseline,
thereby significantly minimizing tuning efforts. Our empirical evaluations
demonstrate the distinct advantages of using Jorge, outperforming
state-of-the-art optimizers such as SGD, AdamW, and Shampoo across multiple
deep learning models, both in terms of sample efficiency and wall-clock time. | [
"Siddharth Singh",
"Zachary Sating",
"Abhinav Bhatele"
] | 2023-10-18 19:58:54 | http://arxiv.org/abs/2310.12298v1 | http://arxiv.org/pdf/2310.12298v1 | 2310.12298v1 |
Open-Set Multivariate Time-Series Anomaly Detection | Numerous methods for time series anomaly detection (TSAD) methods have
emerged in recent years. Most existing methods are unsupervised and assume the
availability of normal training samples only, while few supervised methods have
shown superior performance by incorporating labeled anomalous samples in the
training phase. However, certain anomaly types are inherently challenging for
unsupervised methods to differentiate from normal data, while supervised
methods are constrained to detecting anomalies resembling those present during
training, failing to generalize to unseen anomaly classes. This paper is the
first attempt in providing a novel approach for the open-set TSAD problem, in
which a small number of labeled anomalies from a limited class of anomalies are
visible in the training phase, with the objective of detecting both seen and
unseen anomaly classes in the test phase. The proposed method, called
Multivariate Open-Set timeseries Anomaly Detection (MOSAD) consists of three
primary modules: a Feature Extractor to extract meaningful time-series
features; a Multi-head Network consisting of Generative-, Deviation-, and
Contrastive heads for capturing both seen and unseen anomaly classes; and an
Anomaly Scoring module leveraging the insights of the three heads to detect
anomalies. Extensive experiments on three real-world datasets consistently show
that our approach surpasses existing methods under various experimental
settings, thus establishing a new state-of-the-art performance in the TSAD
field. | [
"Thomas Lai",
"Thi Kieu Khanh Ho",
"Narges Armanfard"
] | 2023-10-18 19:55:11 | http://arxiv.org/abs/2310.12294v1 | http://arxiv.org/pdf/2310.12294v1 | 2310.12294v1 |
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning | In recent years, Massive Open Online Courses (MOOCs) have gained significant
traction as a rapidly growing phenomenon in online learning. Unlike traditional
classrooms, MOOCs offer a unique opportunity to cater to a diverse audience
from different backgrounds and geographical locations. Renowned universities
and MOOC-specific providers, such as Coursera, offer MOOC courses on various
subjects. Automated assessment tasks like grade and early dropout predictions
are necessary due to the high enrollment and limited direct interaction between
teachers and learners. However, current automated assessment approaches
overlook the structural links between different entities involved in the
downstream tasks, such as the students and courses. Our hypothesis suggests
that these structural relationships, manifested through an interaction graph,
contain valuable information that can enhance the performance of the task at
hand. To validate this, we construct a unique knowledge graph for a large MOOC
dataset, which will be publicly available to the research community.
Furthermore, we utilize graph embedding techniques to extract latent structural
information encoded in the interactions between entities in the dataset. These
techniques do not require ground truth labels and can be utilized for various
tasks. Finally, by combining entity-specific features, behavioral features, and
extracted structural features, we enhance the performance of predictive machine
learning models in student assignment grade prediction. Our experiments
demonstrate that structural features can significantly improve the predictive
performance of downstream assessment tasks. The code and data are available in
\url{https://github.com/DSAatUSU/MOOPer_grade_prediction} | [
"Soheila Farokhi",
"Aswani Yaramala",
"Jiangtao Huang",
"Muhammad F. A. Khan",
"Xiaojun Qi",
"Hamid Karimi"
] | 2023-10-18 19:27:39 | http://arxiv.org/abs/2310.12281v1 | http://arxiv.org/pdf/2310.12281v1 | 2310.12281v1 |
An Image is Worth Multiple Words: Learning Object Level Concepts using Multi-Concept Prompt Learning | Textural Inversion, a prompt learning method, learns a singular embedding for
a new "word" to represent image style and appearance, allowing it to be
integrated into natural language sentences to generate novel synthesised
images. However, identifying and integrating multiple object-level concepts
within one scene poses significant challenges even when embeddings for
individual concepts are attainable. This is further confirmed by our empirical
tests. To address this challenge, we introduce a framework for Multi-Concept
Prompt Learning (MCPL), where multiple new "words" are simultaneously learned
from a single sentence-image pair. To enhance the accuracy of word-concept
correlation, we propose three regularisation techniques: Attention Masking
(AttnMask) to concentrate learning on relevant areas; Prompts Contrastive Loss
(PromptCL) to separate the embeddings of different concepts; and Bind adjective
(Bind adj.) to associate new "words" with known words. We evaluate via image
generation, editing, and attention visualisation with diverse images. Extensive
quantitative comparisons demonstrate that our method can learn more
semantically disentangled concepts with enhanced word-concept correlation.
Additionally, we introduce a novel dataset and evaluation protocol tailored for
this new task of learning object-level concepts. | [
"Chen Jin",
"Ryutaro Tanno",
"Amrutha Saseendran",
"Tom Diethe",
"Philip Teare"
] | 2023-10-18 19:18:19 | http://arxiv.org/abs/2310.12274v1 | http://arxiv.org/pdf/2310.12274v1 | 2310.12274v1 |
Improving SCGAN's Similarity Constraint and Learning a Better Disentangled Representation | SCGAN adds a similarity constraint between generated images and conditions as
a regularization term on generative adversarial networks. Similarity constraint
works as a tutor to instruct the generator network to comprehend the difference
of representations based on conditions. We understand how SCGAN works on a
deeper level. This understanding makes us realize that the similarity
constraint functions like the contrastive loss function. We believe that a
model with high understanding and intelligence measures the similarity between
images based on their structure and high level features, just like humans do.
Two major changes we applied to SCGAN in order to make a modified model are
using SSIM to measure similarity between images and applying contrastive loss
principles to the similarity constraint. The modified model performs better
using FID and FactorVAE metrics. The modified model also has better
generalisability compared to other models. Keywords Generative Adversarial
Nets, Unsupervised Learning, Disentangled Representation Learning, Contrastive
Disentanglement, SSIM | [
"Iman Yazdanpanah"
] | 2023-10-18 18:57:13 | http://arxiv.org/abs/2310.12262v1 | http://arxiv.org/pdf/2310.12262v1 | 2310.12262v1 |
Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm | Deep neural networks (DNNs) have significantly advanced various domains, but
their vulnerability to adversarial attacks poses serious concerns.
Understanding these vulnerabilities and developing effective defense mechanisms
is crucial. DeepFool, an algorithm proposed by Moosavi-Dezfooli et al. (2016),
finds minimal perturbations to misclassify input images. However, DeepFool
lacks a targeted approach, making it less effective in specific attack
scenarios. Also, in previous related works, researchers primarily focus on
success, not considering how much an image is getting distorted; the integrity
of the image quality, and the confidence level to misclassifying. So, in this
paper, we propose Targeted DeepFool, an augmented version of DeepFool that
allows targeting specific classes for misclassification. We also introduce a
minimum confidence score requirement hyperparameter to enhance flexibility. Our
experiments demonstrate the effectiveness and efficiency of the proposed method
across different deep neural network architectures while preserving image
integrity as much as possible. Results show that one of the deep convolutional
neural network architectures, AlexNet, and one of the state-of-the-art model
Vision Transformer exhibit high robustness to getting fooled. Our code will be
made public when publishing the paper. | [
"S. M. Fazle Rabby Labib",
"Joyanta Jyoti Mondal",
"Meem Arafat Manab"
] | 2023-10-18 18:50:39 | http://arxiv.org/abs/2310.13019v1 | http://arxiv.org/pdf/2310.13019v1 | 2310.13019v1 |
A PAC Learning Algorithm for LTL and Omega-regular Objectives in MDPs | Linear temporal logic (LTL) and omega-regular objectives -- a superset of LTL
-- have seen recent use as a way to express non-Markovian objectives in
reinforcement learning. We introduce a model-based probably approximately
correct (PAC) learning algorithm for omega-regular objectives in Markov
decision processes. Unlike prior approaches, our algorithm learns from sampled
trajectories of the system and does not require prior knowledge of the system's
topology. | [
"Mateo Perez",
"Fabio Somenzi",
"Ashutosh Trivedi"
] | 2023-10-18 18:33:41 | http://arxiv.org/abs/2310.12248v1 | http://arxiv.org/pdf/2310.12248v1 | 2310.12248v1 |
A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm | Domain incremental learning aims to adapt to a sequence of domains with
access to only a small subset of data (i.e., memory) from previous domains.
Various methods have been proposed for this problem, but it is still unclear
how they are related and when practitioners should choose one method over
another. In response, we propose a unified framework, dubbed Unified Domain
Incremental Learning (UDIL), for domain incremental learning with memory. Our
UDIL **unifies** various existing methods, and our theoretical analysis shows
that UDIL always achieves a tighter generalization error bound compared to
these methods. The key insight is that different existing methods correspond to
our bound with different **fixed** coefficients; based on insights from this
unification, our UDIL allows **adaptive** coefficients during training, thereby
always achieving the tightest bound. Empirical results show that our UDIL
outperforms the state-of-the-art domain incremental learning methods on both
synthetic and real-world datasets. Code will be available at
https://github.com/Wang-ML-Lab/unified-continual-learning. | [
"Haizhou Shi",
"Hao Wang"
] | 2023-10-18 18:30:07 | http://arxiv.org/abs/2310.12244v1 | http://arxiv.org/pdf/2310.12244v1 | 2310.12244v1 |
REVAMP: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes | Deep Learning models, such as those used in an autonomous vehicle are
vulnerable to adversarial attacks where an attacker could place an adversarial
object in the environment, leading to mis-classification. Generating these
adversarial objects in the digital space has been extensively studied, however
successfully transferring these attacks from the digital realm to the physical
realm has proven challenging when controlling for real-world environmental
factors. In response to these limitations, we introduce REVAMP, an easy-to-use
Python library that is the first-of-its-kind tool for creating attack scenarios
with arbitrary objects and simulating realistic environmental factors,
lighting, reflection, and refraction. REVAMP enables researchers and
practitioners to swiftly explore various scenarios within the digital realm by
offering a wide range of configurable options for designing experiments and
using differentiable rendering to reproduce physically plausible adversarial
objects. We will demonstrate and invite the audience to try REVAMP to produce
an adversarial texture on a chosen object while having control over various
scene parameters. The audience will choose a scene, an object to attack, the
desired attack class, and the number of camera positions to use. Then, in real
time, we show how this altered texture causes the chosen object to be
mis-classified, showcasing the potential of REVAMP in real-world scenarios.
REVAMP is open-source and available at https://github.com/poloclub/revamp. | [
"Matthew Hull",
"Zijie J. Wang",
"Duen Horng Chau"
] | 2023-10-18 18:28:44 | http://arxiv.org/abs/2310.12243v1 | http://arxiv.org/pdf/2310.12243v1 | 2310.12243v1 |
Few-Shot In-Context Imitation Learning via Implicit Graph Alignment | Consider the following problem: given a few demonstrations of a task across a
few different objects, how can a robot learn to perform that same task on new,
previously unseen objects? This is challenging because the large variety of
objects within a class makes it difficult to infer the task-relevant
relationship between the new objects and the objects in the demonstrations. We
address this by formulating imitation learning as a conditional alignment
problem between graph representations of objects. Consequently, we show that
this conditioning allows for in-context learning, where a robot can perform a
task on a set of new objects immediately after the demonstrations, without any
prior knowledge about the object class or any further training. In our
experiments, we explore and validate our design choices, and we show that our
method is highly effective for few-shot learning of several real-world,
everyday tasks, whilst outperforming baselines. Videos are available on our
project webpage at https://www.robot-learning.uk/implicit-graph-alignment. | [
"Vitalis Vosylius",
"Edward Johns"
] | 2023-10-18 18:26:01 | http://arxiv.org/abs/2310.12238v1 | http://arxiv.org/pdf/2310.12238v1 | 2310.12238v1 |
Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows | Pulsar timing arrays (PTAs) perform Bayesian posterior inference with
expensive MCMC methods. Given a dataset of ~10-100 pulsars and O(10^3) timing
residuals each, producing a posterior distribution for the stochastic
gravitational wave background (SGWB) can take days to a week. The computational
bottleneck arises because the likelihood evaluation required for MCMC is
extremely costly when considering the dimensionality of the search space.
Fortunately, generating simulated data is fast, so modern simulation-based
inference techniques can be brought to bear on the problem. In this paper, we
demonstrate how conditional normalizing flows trained on simulated data can be
used for extremely fast and accurate estimation of the SGWB posteriors,
reducing the sampling time from weeks to a matter of seconds. | [
"David Shih",
"Marat Freytsis",
"Stephen R. Taylor",
"Jeff A. Dror",
"Nolan Smyth"
] | 2023-10-18 18:00:04 | http://arxiv.org/abs/2310.12209v1 | http://arxiv.org/pdf/2310.12209v1 | 2310.12209v1 |
Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing | Adiabatic quantum computing (AQC) is a promising quantum computing approach
for discrete and often NP-hard optimization problems. Current AQCs allow to
implement problems of research interest, which has sparked the development of
quantum representations for many machine learning and computer vision tasks.
Despite requiring multiple measurements from the noisy AQC, current approaches
only utilize the best measurement, discarding information contained in the
remaining ones. In this work, we explore the potential of using this
information for probabilistic balanced k-means clustering. Instead of
discarding non-optimal solutions, we propose to use them to compute calibrated
posterior probabilities with little additional compute cost. This allows us to
identify ambiguous solutions and data points, which we demonstrate on a D-Wave
AQC on synthetic and real data. | [
"Jan-Nico Zaech",
"Martin Danelljan",
"Luc Van Gool"
] | 2023-10-18 17:59:45 | http://arxiv.org/abs/2310.12153v1 | http://arxiv.org/pdf/2310.12153v1 | 2310.12153v1 |
Fairer and More Accurate Tabular Models Through NAS | Making models algorithmically fairer in tabular data has been long studied,
with techniques typically oriented towards fixes which usually take a neural
model with an undesirable outcome and make changes to how the data are
ingested, what the model weights are, or how outputs are processed. We employ
an emergent and different strategy where we consider updating the model's
architecture and training hyperparameters to find an entirely new model with
better outcomes from the beginning of the debiasing procedure. In this work, we
propose using multi-objective Neural Architecture Search (NAS) and
Hyperparameter Optimization (HPO) in the first application to the very
challenging domain of tabular data. We conduct extensive exploration of
architectural and hyperparameter spaces (MLP, ResNet, and FT-Transformer)
across diverse datasets, demonstrating the dependence of accuracy and fairness
metrics of model predictions on hyperparameter combinations. We show that
models optimized solely for accuracy with NAS often fail to inherently address
fairness concerns. We propose a novel approach that jointly optimizes
architectural and training hyperparameters in a multi-objective constraint of
both accuracy and fairness. We produce architectures that consistently Pareto
dominate state-of-the-art bias mitigation methods either in fairness, accuracy
or both, all of this while being Pareto-optimal over hyperparameters achieved
through single-objective (accuracy) optimization runs. This research
underscores the promise of automating fairness and accuracy optimization in
deep learning models. | [
"Richeek Das",
"Samuel Dooley"
] | 2023-10-18 17:56:24 | http://arxiv.org/abs/2310.12145v1 | http://arxiv.org/pdf/2310.12145v1 | 2310.12145v1 |
Dynamic financial processes identification using sparse regressive reservoir computers | In this document, we present key findings in structured matrix approximation
theory, with applications to the regressive representation of dynamic financial
processes. Initially, we explore a comprehensive approach involving generic
nonlinear time delay embedding for time series data extracted from a financial
or economic system under examination. Subsequently, we employ sparse
least-squares and structured matrix approximation methods to discern
approximate representations of the output coupling matrices. These
representations play a pivotal role in establishing the regressive models
corresponding to the recursive structures inherent in a given financial system.
The document further introduces prototypical algorithms that leverage the
aforementioned techniques. These algorithms are demonstrated through
applications in approximate identification and predictive simulation of dynamic
financial and economic processes, encompassing scenarios that may or may not
exhibit chaotic behavior. | [
"Fredy Vides",
"Idelfonso B. R. Nogueira",
"Lendy Banegas",
"Evelyn Flores"
] | 2023-10-18 17:55:12 | http://arxiv.org/abs/2310.12144v1 | http://arxiv.org/pdf/2310.12144v1 | 2310.12144v1 |
Simple Mechanisms for Representing, Indexing and Manipulating Concepts | Deep networks typically learn concepts via classifiers, which involves
setting up a model and training it via gradient descent to fit the
concept-labeled data. We will argue instead that learning a concept could be
done by looking at its moment statistics matrix to generate a concrete
representation or signature of that concept. These signatures can be used to
discover structure across the set of concepts and could recursively produce
higher-level concepts by learning this structure from those signatures. When
the concepts are `intersected', signatures of the concepts can be used to find
a common theme across a number of related `intersected' concepts. This process
could be used to keep a dictionary of concepts so that inputs could correctly
identify and be routed to the set of concepts involved in the (latent)
generation of the input. | [
"Yuanzhi Li",
"Raghu Meka",
"Rina Panigrahy",
"Kulin Shah"
] | 2023-10-18 17:54:29 | http://arxiv.org/abs/2310.12143v1 | http://arxiv.org/pdf/2310.12143v1 | 2310.12143v1 |
Getting aligned on representational alignment | Biological and artificial information processing systems form representations
of the world that they can use to categorize, reason, plan, navigate, and make
decisions. To what extent do the representations formed by these diverse
systems agree? Can diverging representations still lead to the same behaviors?
And how can systems modify their representations to better match those of
another system? These questions pertaining to the study of
\textbf{\emph{representational alignment}} are at the heart of some of the most
active research areas in contemporary cognitive science, neuroscience, and
machine learning. Unfortunately, there is limited knowledge-transfer between
research communities interested in representational alignment, and much of the
progress in one field ends up being rediscovered independently in another, when
greater cross-field communication would be advantageous. To improve
communication between fields, we propose a unifying framework that can serve as
a common language between researchers studying representational alignment. We
survey the literature from the fields of cognitive science, neuroscience, and
machine learning, and demonstrate how prior work fits into this framework.
Finally, we lay out open problems in representational alignment where progress
can benefit all three fields. We hope that our work can catalyze
cross-disciplinary collaboration and accelerate progress for all communities
studying and developing information processing systems. We note that this is a
working paper and encourage readers to reach out with their suggestions for
future revisions. | [
"Ilia Sucholutsky",
"Lukas Muttenthaler",
"Adrian Weller",
"Andi Peng",
"Andreea Bobu",
"Been Kim",
"Bradley C. Love",
"Erin Grant",
"Jascha Achterberg",
"Joshua B. Tenenbaum",
"Katherine M. Collins",
"Katherine L. Hermann",
"Kerem Oktar",
"Klaus Greff",
"Martin N. Hebart",
"Nori Jacoby",
"Qiuyi",
"Zhang",
"Raja Marjieh",
"Robert Geirhos",
"Sherol Chen",
"Simon Kornblith",
"Sunayana Rane",
"Talia Konkle",
"Thomas P. O'Connell",
"Thomas Unterthiner",
"Andrew K. Lampinen",
"Klaus-Robert Müller",
"Mariya Toneva",
"Thomas L. Griffiths"
] | 2023-10-18 17:47:58 | http://arxiv.org/abs/2310.13018v1 | http://arxiv.org/pdf/2310.13018v1 | 2310.13018v1 |
DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning | Text-to-image (T2I) generation has seen significant growth over the past few
years. Despite this, there has been little work on generating diagrams with T2I
models. A diagram is a symbolic/schematic representation that explains
information using structurally rich and spatially complex visualizations (e.g.,
a dense combination of related objects, text labels, directional arrows,
connection lines, etc.). Existing state-of-the-art T2I models often fail at
diagram generation because they lack fine-grained object layout control when
many objects are densely connected via complex relations such as arrows/lines
and also often fail to render comprehensible text labels. To address this gap,
we present DiagrammerGPT, a novel two-stage text-to-diagram generation
framework that leverages the layout guidance capabilities of LLMs (e.g., GPT-4)
to generate more accurate open-domain, open-platform diagrams. In the first
stage, we use LLMs to generate and iteratively refine 'diagram plans' (in a
planner-auditor feedback loop) which describe all the entities (objects and
text labels), their relationships (arrows or lines), and their bounding box
layouts. In the second stage, we use a diagram generator, DiagramGLIGEN, and a
text label rendering module to generate diagrams following the diagram plans.
To benchmark the text-to-diagram generation task, we introduce AI2D-Caption, a
densely annotated diagram dataset built on top of the AI2D dataset. We show
quantitatively and qualitatively that our DiagrammerGPT framework produces more
accurate diagrams, outperforming existing T2I models. We also provide
comprehensive analysis including open-domain diagram generation, vector graphic
diagram generation in different platforms, human-in-the-loop diagram plan
editing, and multimodal planner/auditor LLMs (e.g., GPT-4Vision). We hope our
work can inspire further research on diagram generation via T2I models and
LLMs. | [
"Abhay Zala",
"Han Lin",
"Jaemin Cho",
"Mohit Bansal"
] | 2023-10-18 17:37:10 | http://arxiv.org/abs/2310.12128v1 | http://arxiv.org/pdf/2310.12128v1 | 2310.12128v1 |
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation | Recent instruction fine-tuned models can solve multiple NLP tasks when
prompted to do so, with machine translation (MT) being a prominent use case.
However, current research often focuses on standard performance benchmarks,
leaving compelling fairness and ethical considerations behind. In MT, this
might lead to misgendered translations, resulting, among other harms, in the
perpetuation of stereotypes and prejudices. In this work, we address this gap
by investigating whether and to what extent such models exhibit gender bias in
machine translation and how we can mitigate it. Concretely, we compute
established gender bias metrics on the WinoMT corpus from English to German and
Spanish. We discover that IFT models default to male-inflected translations,
even disregarding female occupational stereotypes. Next, using interpretability
methods, we unveil that models systematically overlook the pronoun indicating
the gender of a target occupation in misgendered translations. Finally, based
on this finding, we propose an easy-to-implement and effective bias mitigation
solution based on few-shot learning that leads to significantly fairer
translations. | [
"Giuseppe Attanasio",
"Flor Miriam Plaza-del-Arco",
"Debora Nozza",
"Anne Lauscher"
] | 2023-10-18 17:36:55 | http://arxiv.org/abs/2310.12127v1 | http://arxiv.org/pdf/2310.12127v1 | 2310.12127v1 |
SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks | We introduce SHARCS for adaptive inference that takes into account the
hardness of input samples. SHARCS can train a router on any transformer
network, enabling the model to direct different samples to sub-networks with
varying widths. Our experiments demonstrate that: (1) SHARCS outperforms or
complements existing per-sample adaptive inference methods across various
classification tasks in terms of accuracy vs. FLOPs; (2) SHARCS generalizes
across different architectures and can be even applied to compressed and
efficient transformer encoders to further improve their efficiency; (3) SHARCS
can provide a 2 times inference speed up at an insignificant drop in accuracy. | [
"Mohammadreza Salehi",
"Sachin Mehta",
"Aditya Kusupati",
"Ali Farhadi",
"Hannaneh Hajishirzi"
] | 2023-10-18 17:35:15 | http://arxiv.org/abs/2310.12126v1 | http://arxiv.org/pdf/2310.12126v1 | 2310.12126v1 |
Automatic prediction of mortality in patients with mental illness using electronic health records | Mental disorders impact the lives of millions of people globally, not only
impeding their day-to-day lives but also markedly reducing life expectancy.
This paper addresses the persistent challenge of predicting mortality in
patients with mental diagnoses using predictive machine-learning models with
electronic health records (EHR). Data from patients with mental disease
diagnoses were extracted from the well-known clinical MIMIC-III data set
utilizing demographic, prescription, and procedural information. Four machine
learning algorithms (Logistic Regression, Random Forest, Support Vector
Machine, and K-Nearest Neighbors) were used, with results indicating that
Random Forest and Support Vector Machine models outperformed others, with AUC
scores of 0.911. Feature importance analysis revealed that drug prescriptions,
particularly Morphine Sulfate, play a pivotal role in prediction. We applied a
variety of machine learning algorithms to predict 30-day mortality followed by
feature importance analysis. This study can be used to assist hospital workers
in identifying at-risk patients to reduce excess mortality. | [
"Sean Kim",
"Samuel Kim"
] | 2023-10-18 17:21:01 | http://arxiv.org/abs/2310.12121v1 | http://arxiv.org/pdf/2310.12121v1 | 2310.12121v1 |
MMD-based Variable Importance for Distributional Random Forest | Distributional Random Forest (DRF) is a flexible forest-based method to
estimate the full conditional distribution of a multivariate output of interest
given input variables. In this article, we introduce a variable importance
algorithm for DRFs, based on the well-established drop and relearn principle
and MMD distance. While traditional importance measures only detect variables
with an influence on the output mean, our algorithm detects variables impacting
the output distribution more generally. We show that the introduced importance
measure is consistent, exhibits high empirical performance on both real and
simulated data, and outperforms competitors. In particular, our algorithm is
highly efficient to select variables through recursive feature elimination, and
can therefore provide small sets of variables to build accurate estimates of
conditional output distributions. | [
"Clément Bénard",
"Jeffrey Näf",
"Julie Josse"
] | 2023-10-18 17:12:29 | http://arxiv.org/abs/2310.12115v1 | http://arxiv.org/pdf/2310.12115v1 | 2310.12115v1 |
A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses | Within the realm of privacy-preserving machine learning, empirical privacy
defenses have been proposed as a solution to achieve satisfactory levels of
training data privacy without a significant drop in model utility. Most
existing defenses against membership inference attacks assume access to
reference data, defined as an additional dataset coming from the same (or a
similar) underlying distribution as training data. Despite the common use of
reference data, previous works are notably reticent about defining and
evaluating reference data privacy. As gains in model utility and/or training
data privacy may come at the expense of reference data privacy, it is essential
that all three aspects are duly considered. In this paper, we first examine the
availability of reference data and its privacy treatment in previous works and
demonstrate its necessity for fairly comparing defenses. Second, we propose a
baseline defense that enables the utility-privacy tradeoff with respect to both
training and reference data to be easily understood. Our method is formulated
as an empirical risk minimization with a constraint on the generalization
error, which, in practice, can be evaluated as a weighted empirical risk
minimization (WERM) over the training and reference datasets. Although we
conceived of WERM as a simple baseline, our experiments show that,
surprisingly, it outperforms the most well-studied and current state-of-the-art
empirical privacy defenses using reference data for nearly all relative privacy
levels of reference and training data. Our investigation also reveals that
these existing methods are unable to effectively trade off reference data
privacy for model utility and/or training data privacy. Overall, our work
highlights the need for a proper evaluation of the triad model utility /
training data privacy / reference data privacy when comparing privacy defenses. | [
"Caelin G. Kaplan",
"Chuan Xu",
"Othmane Marfoq",
"Giovanni Neglia",
"Anderson Santana de Oliveira"
] | 2023-10-18 17:07:07 | http://arxiv.org/abs/2310.12112v1 | http://arxiv.org/pdf/2310.12112v1 | 2310.12112v1 |
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture | Machine learning models are increasingly being scaled in both sequence length
and model dimension to reach longer contexts and better performance. However,
existing architectures such as Transformers scale quadratically along both
these axes. We ask: are there performant architectures that can scale
sub-quadratically along sequence length and model dimension? We introduce
Monarch Mixer (M2), a new architecture that uses the same sub-quadratic
primitive along both sequence length and model dimension: Monarch matrices, a
simple class of expressive structured matrices that captures many linear
transforms, achieves high hardware efficiency on GPUs, and scales
sub-quadratically. As a proof of concept, we explore the performance of M2 in
three domains: non-causal BERT-style language modeling, ViT-style image
classification, and causal GPT-style language modeling. For non-causal
BERT-style modeling, M2 matches BERT-base and BERT-large in downstream GLUE
quality with up to 27% fewer parameters, and achieves up to 9.1$\times$ higher
throughput at sequence length 4K. On ImageNet, M2 outperforms ViT-b by 1% in
accuracy, with only half the parameters. Causal GPT-style models introduce a
technical challenge: enforcing causality via masking introduces a quadratic
bottleneck. To alleviate this bottleneck, we develop a novel theoretical view
of Monarch matrices based on multivariate polynomial evaluation and
interpolation, which lets us parameterize M2 to be causal while remaining
sub-quadratic. Using this parameterization, M2 matches GPT-style Transformers
at 360M parameters in pretraining perplexity on The PILE--showing for the first
time that it may be possible to match Transformer quality without attention or
MLPs. | [
"Daniel Y. Fu",
"Simran Arora",
"Jessica Grogan",
"Isys Johnson",
"Sabri Eyuboglu",
"Armin W. Thomas",
"Benjamin Spector",
"Michael Poli",
"Atri Rudra",
"Christopher Ré"
] | 2023-10-18 17:06:22 | http://arxiv.org/abs/2310.12109v1 | http://arxiv.org/pdf/2310.12109v1 | 2310.12109v1 |
An Online Learning Theory of Brokerage | We investigate brokerage between traders from an online learning perspective.
At any round $t$, two traders arrive with their private valuations, and the
broker proposes a trading price. Unlike other bilateral trade problems already
studied in the online learning literature, we focus on the case where there are
no designated buyer and seller roles: each trader will attempt to either buy or
sell depending on the current price of the good.
We assume the agents' valuations are drawn i.i.d. from a fixed but unknown
distribution. If the distribution admits a density bounded by some constant
$M$, then, for any time horizon $T$:
$\bullet$ If the agents' valuations are revealed after each interaction, we
provide an algorithm achieving regret $M \log T$ and show this rate is optimal,
up to constant factors.
$\bullet$ If only their willingness to sell or buy at the proposed price is
revealed after each interaction, we provide an algorithm achieving regret
$\sqrt{M T}$ and show this rate is optimal, up to constant factors.
Finally, if we drop the bounded density assumption, we show that the optimal
rate degrades to $\sqrt{T}$ in the first case, and the problem becomes
unlearnable in the second. | [
"Nataša Bolić",
"Tommaso Cesari",
"Roberto Colomboni"
] | 2023-10-18 17:01:32 | http://arxiv.org/abs/2310.12107v1 | http://arxiv.org/pdf/2310.12107v1 | 2310.12107v1 |
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling | Large language models (LLMs) and vision language models (VLMs) demonstrate
excellent performance on a wide range of tasks by scaling up parameter counts
from O(10^9) to O(10^{12}) levels and further beyond. These large scales make
it impossible to adapt and deploy fully specialized models given a task of
interest. Parameter-efficient fine-tuning (PEFT) emerges as a promising
direction to tackle the adaptation and serving challenges for such large
models. We categorize PEFT techniques into two types: intrusive and
non-intrusive. Intrusive PEFT techniques directly change a model's internal
architecture. Though more flexible, they introduce significant complexities for
training and serving. Non-intrusive PEFT techniques leave the internal
architecture unchanged and only adapt model-external parameters, such as
embeddings for input. In this work, we describe AdaLink as a non-intrusive PEFT
technique that achieves competitive performance compared to SoTA intrusive PEFT
(LoRA) and full model fine-tuning (FT) on various tasks. We evaluate using both
text-only and multimodal tasks, with experiments that account for both
parameter-count scaling and training regime (with and without instruction
tuning). | [
"Yaqing Wang",
"Jialin Wu",
"Tanmaya Dabral",
"Jiageng Zhang",
"Geoff Brown",
"Chun-Ta Lu",
"Frederick Liu",
"Yi Liang",
"Bo Pang",
"Michael Bendersky",
"Radu Soricut"
] | 2023-10-18 16:43:08 | http://arxiv.org/abs/2310.12100v1 | http://arxiv.org/pdf/2310.12100v1 | 2310.12100v1 |
Position Interpolation Improves ALiBi Extrapolation | Linear position interpolation helps pre-trained models using rotary position
embeddings (RoPE) to extrapolate to longer sequence lengths. We propose using
linear position interpolation to extend the extrapolation range of models using
Attention with Linear Biases (ALiBi). We find position interpolation
significantly improves extrapolation capability on upstream language modelling
and downstream summarization and retrieval tasks. | [
"Faisal Al-Khateeb",
"Nolan Dey",
"Daria Soboleva",
"Joel Hestness"
] | 2023-10-18 16:41:47 | http://arxiv.org/abs/2310.13017v1 | http://arxiv.org/pdf/2310.13017v1 | 2310.13017v1 |
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields | Deep Learning is having a remarkable impact on the design of Reduced Order
Models (ROMs) for Partial Differential Equations (PDEs), where it is exploited
as a powerful tool for tackling complex problems for which classical methods
might fail. In this respect, deep autoencoders play a fundamental role, as they
provide an extremely flexible tool for reducing the dimensionality of a given
problem by leveraging on the nonlinear capabilities of neural networks. Indeed,
starting from this paradigm, several successful approaches have already been
developed, which are here referred to as Deep Learning-based ROMs (DL-ROMs).
Nevertheless, when it comes to stochastic problems parameterized by random
fields, the current understanding of DL-ROMs is mostly based on empirical
evidence: in fact, their theoretical analysis is currently limited to the case
of PDEs depending on a finite number of (deterministic) parameters. The purpose
of this work is to extend the existing literature by providing some theoretical
insights about the use of DL-ROMs in the presence of stochasticity generated by
random fields. In particular, we derive explicit error bounds that can guide
domain practitioners when choosing the latent dimension of deep autoencoders.
We evaluate the practical usefulness of our theory by means of numerical
experiments, showing how our analysis can significantly impact the performance
of DL-ROMs. | [
"Nicola Rares Franco",
"Daniel Fraulin",
"Andrea Manzoni",
"Paolo Zunino"
] | 2023-10-18 16:38:23 | http://arxiv.org/abs/2310.12095v1 | http://arxiv.org/pdf/2310.12095v1 | 2310.12095v1 |
Unveiling the Siren's Song: Towards Reliable Fact-Conflicting Hallucination Detection | Large Language Models (LLMs), such as ChatGPT/GPT-4, have garnered widespread
attention owing to their myriad of practical applications, yet their adoption
has been constrained by issues of fact-conflicting hallucinations across web
platforms. The assessment of factuality in text, produced by LLMs, remains
inadequately explored, extending not only to the judgment of vanilla facts but
also encompassing the evaluation of factual errors emerging in complex
inferential tasks like multi-hop, and etc. In response, we introduce FactCHD, a
fact-conflicting hallucination detection benchmark meticulously designed for
LLMs. Functioning as a pivotal tool in evaluating factuality within
"Query-Respons" contexts, our benchmark assimilates a large-scale dataset,
encapsulating a broad spectrum of factuality patterns, such as vanilla,
multi-hops, comparison, and set-operation patterns. A distinctive feature of
our benchmark is its incorporation of fact-based chains of evidence, thereby
facilitating comprehensive and conducive factual reasoning throughout the
assessment process. We evaluate multiple LLMs, demonstrating the effectiveness
of the benchmark and current methods fall short of faithfully detecting factual
errors. Furthermore, we present TRUTH-TRIANGULATOR that synthesizes reflective
considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2, aiming
to yield more credible detection through the amalgamation of predictive results
and evidence. The benchmark dataset and source code will be made available in
https://github.com/zjunlp/FactCHD. | [
"Xiang Chen",
"Duanzheng Song",
"Honghao Gui",
"Chengxi Wang",
"Ningyu Zhang",
"Fei Huang",
"Chengfei Lv",
"Dan Zhang",
"Huajun Chen"
] | 2023-10-18 16:27:49 | http://arxiv.org/abs/2310.12086v1 | http://arxiv.org/pdf/2310.12086v1 | 2310.12086v1 |
Contributing Components of Metabolic Energy Models to Metabolic Cost Estimations in Gait | Objective: As metabolic cost is a primary factor influencing humans' gait, we
want to deepen our understanding of metabolic energy expenditure models.
Therefore, this paper identifies the parameters and input variables, such as
muscle or joint states, that contribute to accurate metabolic cost estimations.
Methods: We explored the parameters of four metabolic energy expenditure models
in a Monte Carlo sensitivity analysis. Then, we analysed the model parameters
by their calculated sensitivity indices, physiological context, and the
resulting metabolic rates during the gait cycle. The parameter combination with
the highest accuracy in the Monte Carlo simulations represented a
quasi-optimized model. In the second step, we investigated the importance of
input parameters and variables by analysing the accuracy of neural networks
trained with different input features. Results: Power-related parameters were
most influential in the sensitivity analysis and the neural network-based
feature selection. We observed that the quasi-optimized models produced
negative metabolic rates, contradicting muscle physiology. Neural network-based
models showed promising abilities but have been unable to match the accuracy of
traditional metabolic energy expenditure models. Conclusion: We showed that
power-related metabolic energy expenditure model parameters and inputs are most
influential during gait. Furthermore, our results suggest that neural
network-based metabolic energy expenditure models are viable. However, bigger
datasets are required to achieve better accuracy. Significance: As there is a
need for more accurate metabolic energy expenditure models, we explored which
musculoskeletal parameters are essential when developing a model to estimate
metabolic energy. | [
"Markus Gambietz",
"Marlies Nitschke",
"Jörg Miehling",
"Anne Koelewijn"
] | 2023-10-18 16:24:23 | http://arxiv.org/abs/2310.12083v1 | http://arxiv.org/pdf/2310.12083v1 | 2310.12083v1 |
Differential Equation Scaling Limits of Shaped and Unshaped Neural Networks | Recent analyses of neural networks with shaped activations (i.e. the
activation function is scaled as the network size grows) have led to scaling
limits described by differential equations. However, these results do not a
priori tell us anything about "ordinary" unshaped networks, where the
activation is unchanged as the network size grows. In this article, we find
similar differential equation based asymptotic characterization for two types
of unshaped networks.
Firstly, we show that the following two architectures converge to the same
infinite-depth-and-width limit at initialization: (i) a fully connected ResNet
with a $d^{-1/2}$ factor on the residual branch, where $d$ is the network
depth. (ii) a multilayer perceptron (MLP) with depth $d \ll$ width $n$ and
shaped ReLU activation at rate $d^{-1/2}$.
Secondly, for an unshaped MLP at initialization, we derive the first order
asymptotic correction to the layerwise correlation. In particular, if
$\rho_\ell$ is the correlation at layer $\ell$, then $q_t = \ell^2 (1 -
\rho_\ell)$ with $t = \frac{\ell}{n}$ converges to an SDE with a singularity at
$t=0$.
These results together provide a connection between shaped and unshaped
network architectures, and opens up the possibility of studying the effect of
normalization methods and how it connects with shaping activation functions. | [
"Mufan Bill Li",
"Mihai Nica"
] | 2023-10-18 16:15:10 | http://arxiv.org/abs/2310.12079v1 | http://arxiv.org/pdf/2310.12079v1 | 2310.12079v1 |
One-Shot Imitation Learning: A Pose Estimation Perspective | In this paper, we study imitation learning under the challenging setting of:
(1) only a single demonstration, (2) no further data collection, and (3) no
prior task or object knowledge. We show how, with these constraints, imitation
learning can be formulated as a combination of trajectory transfer and unseen
object pose estimation. To explore this idea, we provide an in-depth study on
how state-of-the-art unseen object pose estimators perform for one-shot
imitation learning on ten real-world tasks, and we take a deep dive into the
effects that camera calibration, pose estimation error, and spatial
generalisation have on task success rates. For videos, please visit
https://www.robot-learning.uk/pose-estimation-perspective. | [
"Pietro Vitiello",
"Kamil Dreczkowski",
"Edward Johns"
] | 2023-10-18 16:13:35 | http://arxiv.org/abs/2310.12077v1 | http://arxiv.org/pdf/2310.12077v1 | 2310.12077v1 |
Transformers for scientific data: a pedagogical review for astronomers | The deep learning architecture associated with ChatGPT and related generative
AI products is known as transformers. Initially applied to Natural Language
Processing, transformers and the self-attention mechanism they exploit have
gained widespread interest across the natural sciences. The goal of this
pedagogical and informal review is to introduce transformers to scientists. The
review includes the mathematics underlying the attention mechanism, a
description of the original transformer architecture, and a section on
applications to time series and imaging data in astronomy. We include a
Frequently Asked Questions section for readers who are curious about generative
AI or interested in getting started with transformers for their research
problem. | [
"Dimitrios Tanoglidis",
"Bhuvnesh Jain",
"Helen Qu"
] | 2023-10-18 16:02:32 | http://arxiv.org/abs/2310.12069v2 | http://arxiv.org/pdf/2310.12069v2 | 2310.12069v2 |
Black-Box Training Data Identification in GANs via Detector Networks | Since their inception Generative Adversarial Networks (GANs) have been
popular generative models across images, audio, video, and tabular data. In
this paper we study whether given access to a trained GAN, as well as fresh
samples from the underlying distribution, if it is possible for an attacker to
efficiently identify if a given point is a member of the GAN's training data.
This is of interest for both reasons related to copyright, where a user may
want to determine if their copyrighted data has been used to train a GAN, and
in the study of data privacy, where the ability to detect training set
membership is known as a membership inference attack. Unlike the majority of
prior work this paper investigates the privacy implications of using GANs in
black-box settings, where the attack only has access to samples from the
generator, rather than access to the discriminator as well. We introduce a
suite of membership inference attacks against GANs in the black-box setting and
evaluate our attacks on image GANs trained on the CIFAR10 dataset and tabular
GANs trained on genomic data. Our most successful attack, called The Detector,
involve training a second network to score samples based on their likelihood of
being generated by the GAN, as opposed to a fresh sample from the distribution.
We prove under a simple model of the generator that the detector is an
approximately optimal membership inference attack. Across a wide range of
tabular and image datasets, attacks, and GAN architectures, we find that
adversaries can orchestrate non-trivial privacy attacks when provided with
access to samples from the generator. At the same time, the attack success
achievable against GANs still appears to be lower compared to other generative
and discriminative models; this leaves the intriguing open question of whether
GANs are in fact more private, or if it is a matter of developing stronger
attacks. | [
"Lukman Olagoke",
"Salil Vadhan",
"Seth Neel"
] | 2023-10-18 15:53:20 | http://arxiv.org/abs/2310.12063v1 | http://arxiv.org/pdf/2310.12063v1 | 2310.12063v1 |
Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning | In inverse reinforcement learning (IRL), the central objective is to infer
underlying reward functions from observed expert behaviors in a way that not
only explains the given data but also generalizes to unseen scenarios. This
ensures robustness against reward ambiguity where multiple reward functions can
equally explain the same expert behaviors. While significant efforts have been
made in addressing this issue, current methods often face challenges with
high-dimensional problems and lack a geometric foundation. This paper harnesses
the optimal transport (OT) theory to provide a fresh perspective on these
challenges. By utilizing the Wasserstein distance from OT, we establish a
geometric framework that allows for quantifying reward ambiguity and
identifying a central representation or centroid of reward functions. These
insights pave the way for robust IRL methodologies anchored in geometric
interpretations, offering a structured approach to tackle reward ambiguity in
high-dimensional settings. | [
"Ali Baheri"
] | 2023-10-18 15:42:53 | http://arxiv.org/abs/2310.12055v1 | http://arxiv.org/pdf/2310.12055v1 | 2310.12055v1 |
Machine Learning-based Nutrient Application's Timeline Recommendation for Smart Agriculture: A Large-Scale Data Mining Approach | This study addresses the vital role of data analytics in monitoring
fertiliser applications in crop cultivation. Inaccurate fertiliser application
decisions can lead to costly consequences, hinder food production, and cause
environmental harm. We propose a solution to predict nutrient application by
determining required fertiliser quantities for an entire season. The proposed
solution recommends adjusting fertiliser amounts based on weather conditions
and soil characteristics to promote cost-effective and environmentally friendly
agriculture. The collected dataset is high-dimensional and heterogeneous. Our
research examines large-scale heterogeneous datasets in the context of the
decision-making process, encompassing data collection and analysis. We also
study the impact of fertiliser applications combined with weather data on crop
yield, using the winter wheat crop as a case study. By understanding local
contextual and geographic factors, we aspire to stabilise or even reduce the
demand for agricultural nutrients while enhancing crop development. The
proposed approach is proven to be efficient and scalable, as it is validated
using a real-world and large dataset. | [
"Usama Ikhlaq",
"Tahar Kechadi"
] | 2023-10-18 15:37:19 | http://arxiv.org/abs/2310.12052v1 | http://arxiv.org/pdf/2310.12052v1 | 2310.12052v1 |
Applications of ML-Based Surrogates in Bayesian Approaches to Inverse Problems | Neural networks have become a powerful tool as surrogate models to provide
numerical solutions for scientific problems with increased computational
efficiency. This efficiency can be advantageous for numerically challenging
problems where time to solution is important or when evaluation of many similar
analysis scenarios is required. One particular area of scientific interest is
the setting of inverse problems, where one knows the forward dynamics of a
system are described by a partial differential equation and the task is to
infer properties of the system given (potentially noisy) observations of these
dynamics. We consider the inverse problem of inferring the location of a wave
source on a square domain, given a noisy solution to the 2-D acoustic wave
equation. Under the assumption of Gaussian noise, a likelihood function for
source location can be formulated, which requires one forward simulation of the
system per evaluation. Using a standard neural network as a surrogate model
makes it computationally feasible to evaluate this likelihood several times,
and so Markov Chain Monte Carlo methods can be used to evaluate the posterior
distribution of the source location. We demonstrate that this method can
accurately infer source-locations from noisy data. | [
"Pelin Ersin",
"Emma Hayes",
"Peter Matthews",
"Paramjyoti Mohapatra",
"Elisa Negrini",
"Karl Schulz"
] | 2023-10-18 15:32:30 | http://arxiv.org/abs/2310.12046v1 | http://arxiv.org/pdf/2310.12046v1 | 2310.12046v1 |
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