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Local Search GFlowNets | Generative Flow Networks (GFlowNets) are amortized sampling methods that
learn a distribution over discrete objects proportional to their rewards.
GFlowNets exhibit a remarkable ability to generate diverse samples, yet
occasionally struggle to consistently produce samples with high rewards due to
over-exploration on wide sample space. This paper proposes to train GFlowNets
with local search which focuses on exploiting high rewarded sample space to
resolve this issue. Our main idea is to explore the local neighborhood via
destruction and reconstruction guided by backward and forward policies,
respectively. This allows biasing the samples toward high-reward solutions,
which is not possible for a typical GFlowNet solution generation scheme which
uses the forward policy to generate the solution from scratch. Extensive
experiments demonstrate a remarkable performance improvement in several
biochemical tasks. Source code is available:
\url{https://github.com/dbsxodud-11/ls_gfn}. | [
"Minsu Kim",
"Taeyoung Yun",
"Emmanuel Bengio",
"Dinghuai Zhang",
"Yoshua Bengio",
"Sungsoo Ahn",
"Jinkyoo Park"
] | 2023-10-04 10:27:17 | http://arxiv.org/abs/2310.02710v1 | http://arxiv.org/pdf/2310.02710v1 | 2310.02710v1 |
Tackling Hybrid Heterogeneity on Federated Optimization via Gradient Diversity Maximization | Federated learning refers to a distributed machine learning paradigm in which
data samples are decentralized and distributed among multiple clients. These
samples may exhibit statistical heterogeneity, which refers to data
distributions are not independent and identical across clients. Additionally,
system heterogeneity, or variations in the computational power of the clients,
introduces biases into federated learning. The combined effects of statistical
and system heterogeneity can significantly reduce the efficiency of federated
optimization. However, the impact of hybrid heterogeneity is not rigorously
discussed. This paper explores how hybrid heterogeneity affects federated
optimization by investigating server-side optimization. The theoretical results
indicate that adaptively maximizing gradient diversity in server update
direction can help mitigate the potential negative consequences of hybrid
heterogeneity. To this end, we introduce a novel server-side gradient-based
optimizer \textsc{FedAWARE} with theoretical guarantees provided. Intensive
experiments in heterogeneous federated settings demonstrate that our proposed
optimizer can significantly enhance the performance of federated learning
across varying degrees of hybrid heterogeneity. | [
"Dun Zeng",
"Zenglin Xu",
"Yu Pan",
"Qifan Wang",
"Xiaoying Tang"
] | 2023-10-04 10:15:57 | http://arxiv.org/abs/2310.02702v1 | http://arxiv.org/pdf/2310.02702v1 | 2310.02702v1 |
Exploring Federated Optimization by Reducing Variance of Adaptive Unbiased Client Sampling | Federated Learning (FL) systems usually sample a fraction of clients to
conduct a training process. Notably, the variance of global estimates for
updating the global model built on information from sampled clients is highly
related to federated optimization quality. This paper explores a line of "free"
adaptive client sampling techniques in federated optimization, where the server
builds promising sampling probability and reliable global estimates without
requiring additional local communication and computation. We capture a minor
variant in the sampling procedure and improve the global estimation
accordingly. Based on that, we propose a novel sampler called K-Vib, which
solves an online convex optimization respecting client sampling in federated
optimization. It achieves improved a linear speed up on regret bound
$\tilde{\mathcal{O}}\big(N^{\frac{1}{3}}T^{\frac{2}{3}}/K^{\frac{4}{3}}\big)$
with communication budget $K$. As a result, it significantly improves the
performance of federated optimization. Theoretical improvements and intensive
experiments on classic federated tasks demonstrate our findings. | [
"Dun Zeng",
"Zenglin Xu",
"Yu Pan",
"Qifan Wang",
"Xiaoying Tang"
] | 2023-10-04 10:08:01 | http://arxiv.org/abs/2310.02698v1 | http://arxiv.org/pdf/2310.02698v1 | 2310.02698v1 |
Probabilistic Block Term Decomposition for the Modelling of Higher-Order Arrays | Tensors are ubiquitous in science and engineering and tensor factorization
approaches have become important tools for the characterization of higher order
structure. Factorizations includes the outer-product rank Canonical Polyadic
Decomposition (CPD) as well as the multi-linear rank Tucker decomposition in
which the Block-Term Decomposition (BTD) is a structured intermediate
interpolating between these two representations. Whereas CPD, Tucker, and BTD
have traditionally relied on maximum-likelihood estimation, Bayesian inference
has been use to form probabilistic CPD and Tucker. We propose, an efficient
variational Bayesian probabilistic BTD, which uses the von-Mises Fisher matrix
distribution to impose orthogonality in the multi-linear Tucker parts forming
the BTD. On synthetic and two real datasets, we highlight the Bayesian
inference procedure and demonstrate using the proposed pBTD on noisy data and
for model order quantification. We find that the probabilistic BTD can quantify
suitable multi-linear structures providing a means for robust inference of
patterns in multi-linear data. | [
"Jesper Løve Hinrich",
"Morten Mørup"
] | 2023-10-04 10:03:15 | http://arxiv.org/abs/2310.02694v1 | http://arxiv.org/pdf/2310.02694v1 | 2310.02694v1 |
Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators | In climate simulations, small-scale processes shape ocean dynamics but remain
computationally expensive to resolve directly. For this reason, their
contributions are commonly approximated using empirical parameterizations,
which lead to significant errors in long-term projections. In this work, we
develop parameterizations based on Fourier Neural Operators, showcasing their
accuracy and generalizability in comparison to other approaches. Finally, we
discuss the potential and limitations of neural networks operating in the
frequency domain, paving the way for future investigation. | [
"Victor Mangeleer",
"Gilles Louppe"
] | 2023-10-04 10:02:49 | http://arxiv.org/abs/2310.02691v1 | http://arxiv.org/pdf/2310.02691v1 | 2310.02691v1 |
Memoria: Hebbian Memory Architecture for Human-Like Sequential Processing | Transformers have demonstrated their success in various domains and tasks.
However, Transformers struggle with long input sequences due to their limited
capacity. While one solution is to increase input length, endlessly stretching
the length is unrealistic. Furthermore, humans selectively remember and use
only relevant information from inputs, unlike Transformers which process all
raw data from start to end. We introduce Memoria, a general memory network that
applies Hebbian theory which is a major theory explaining human memory
formulation to enhance long-term dependencies in neural networks. Memoria
stores and retrieves information called engram at multiple memory levels of
working memory, short-term memory, and long-term memory, using connection
weights that change according to Hebb's rule. Through experiments with popular
Transformer-based models like BERT and GPT, we present that Memoria
significantly improves the ability to consider long-term dependencies in
various tasks. Results show that Memoria outperformed existing methodologies in
sorting and language modeling, and long text classification. | [
"Sangjun Park",
"JinYeong Bak"
] | 2023-10-04 09:40:46 | http://arxiv.org/abs/2310.03052v1 | http://arxiv.org/pdf/2310.03052v1 | 2310.03052v1 |
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization | We tackle the problem of sampling from intractable high-dimensional density
functions, a fundamental task that often appears in machine learning and
statistics. We extend recent sampling-based approaches that leverage controlled
stochastic processes to model approximate samples from these target densities.
The main drawback of these approaches is that the training objective requires
full trajectories to compute, resulting in sluggish credit assignment issues
due to use of entire trajectories and a learning signal present only at the
terminal time. In this work, we present Diffusion Generative Flow Samplers
(DGFS), a sampling-based framework where the learning process can be tractably
broken down into short partial trajectory segments, via parameterizing an
additional "flow function". Our method takes inspiration from the theory
developed for generative flow networks (GFlowNets), allowing us to make use of
intermediate learning signals and benefit from off-policy exploration
capabilities. Through a variety of challenging experiments, we demonstrate that
DGFS results in more accurate estimates of the normalization constant than
closely-related prior methods. | [
"Dinghuai Zhang",
"Ricky Tian Qi Chen",
"Cheng-Hao Liu",
"Aaron Courville",
"Yoshua Bengio"
] | 2023-10-04 09:39:05 | http://arxiv.org/abs/2310.02679v1 | http://arxiv.org/pdf/2310.02679v1 | 2310.02679v1 |
PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting | Accurate precipitation forecasting is a vital challenge of both scientific
and societal importance. Data-driven approaches have emerged as a widely used
solution for addressing this challenge. However, solely relying on data-driven
approaches has limitations in modeling the underlying physics, making accurate
predictions difficult. Coupling AI-based post-processing techniques with
traditional Numerical Weather Prediction (NWP) methods offers a more effective
solution for improving forecasting accuracy. Despite previous post-processing
efforts, accurately predicting heavy rainfall remains challenging due to the
imbalanced precipitation data across locations and complex relationships
between multiple meteorological variables. To address these limitations, we
introduce the PostRainBench, a comprehensive multi-variable NWP post-processing
benchmark consisting of three datasets for NWP post-processing-based
precipitation forecasting. We propose CAMT, a simple yet effective Channel
Attention Enhanced Multi-task Learning framework with a specially designed
weighted loss function. Its flexible design allows for easy plug-and-play
integration with various backbones. Extensive experimental results on the
proposed benchmark show that our method outperforms state-of-the-art methods by
6.3%, 4.7%, and 26.8% in rain CSI on the three datasets respectively. Most
notably, our model is the first deep learning-based method to outperform
traditional Numerical Weather Prediction (NWP) approaches in extreme
precipitation conditions. It shows improvements of 15.6%, 17.4%, and 31.8% over
NWP predictions in heavy rain CSI on respective datasets. These results
highlight the potential impact of our model in reducing the severe consequences
of extreme weather events. | [
"Yujin Tang",
"Jiaming Zhou",
"Xiang Pan",
"Zeying Gong",
"Junwei Liang"
] | 2023-10-04 09:27:39 | http://arxiv.org/abs/2310.02676v2 | http://arxiv.org/pdf/2310.02676v2 | 2310.02676v2 |
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods | Markov Decision Processes (MDPs) are a formal framework for modeling and
solving sequential decision-making problems. In finite-time horizons such
problems are relevant for instance for optimal stopping or specific supply
chain problems, but also in the training of large language models. In contrast
to infinite horizon MDPs optimal policies are not stationary, policies must be
learned for every single epoch. In practice all parameters are often trained
simultaneously, ignoring the inherent structure suggested by dynamic
programming. This paper introduces a combination of dynamic programming and
policy gradient called dynamic policy gradient, where the parameters are
trained backwards in time. For the tabular softmax parametrisation we carry out
the convergence analysis for simultaneous and dynamic policy gradient towards
global optima, both in the exact and sampled gradient settings without
regularisation. It turns out that the use of dynamic policy gradient training
much better exploits the structure of finite-time problems which is reflected
in improved convergence bounds. | [
"Sara Klein",
"Simon Weissmann",
"Leif Döring"
] | 2023-10-04 09:21:01 | http://arxiv.org/abs/2310.02671v1 | http://arxiv.org/pdf/2310.02671v1 | 2310.02671v1 |
On Memorization in Diffusion Models | Due to their capacity to generate novel and high-quality samples, diffusion
models have attracted significant research interest in recent years. Notably,
the typical training objective of diffusion models, i.e., denoising score
matching, has a closed-form optimal solution that can only generate training
data replicating samples. This indicates that a memorization behavior is
theoretically expected, which contradicts the common generalization ability of
state-of-the-art diffusion models, and thus calls for a deeper understanding.
Looking into this, we first observe that memorization behaviors tend to occur
on smaller-sized datasets, which motivates our definition of effective model
memorization (EMM), a metric measuring the maximum size of training data at
which a learned diffusion model approximates its theoretical optimum. Then, we
quantify the impact of the influential factors on these memorization behaviors
in terms of EMM, focusing primarily on data distribution, model configuration,
and training procedure. Besides comprehensive empirical results identifying the
influential factors, we surprisingly find that conditioning training data on
uninformative random labels can significantly trigger the memorization in
diffusion models. Our study holds practical significance for diffusion model
users and offers clues to theoretical research in deep generative models. Code
is available at https://github.com/sail-sg/DiffMemorize. | [
"Xiangming Gu",
"Chao Du",
"Tianyu Pang",
"Chongxuan Li",
"Min Lin",
"Ye Wang"
] | 2023-10-04 09:04:20 | http://arxiv.org/abs/2310.02664v1 | http://arxiv.org/pdf/2310.02664v1 | 2310.02664v1 |
COVID-19 South African Vaccine Hesitancy Models Show Boost in Performance Upon Fine-Tuning on M-pox Tweets | Very large numbers of M-pox cases have, since the start of May 2022, been
reported in non-endemic countries leading many to fear that the M-pox Outbreak
would rapidly transition into another pandemic, while the COVID-19 pandemic
ravages on. Given the similarities of M-pox with COVID-19, we chose to test the
performance of COVID-19 models trained on South African twitter data on a
hand-labelled M-pox dataset before and after fine-tuning. More than 20k
M-pox-related tweets from South Africa were hand-labelled as being either
positive, negative or neutral. After fine-tuning these COVID-19 models on the
M-pox dataset, the F1-scores increased by more than 8% falling just short of
70%, but still outperforming state-of-the-art models and well-known
classification algorithms. An LDA-based topic modelling procedure was used to
compare the miss-classified M-pox tweets of the original COVID-19 RoBERTa model
with its fine-tuned version, and from this analysis, we were able to draw
conclusions on how to build more sophisticated models. | [
"Nicholas Perikli",
"Srimoy Bhattacharya",
"Blessing Ogbuokiri",
"Zahra Movahedi Nia",
"Benjamin Lieberman",
"Nidhi Tripathi",
"Salah-Eddine Dahbi",
"Finn Stevenson",
"Nicola Bragazzi",
"Jude Kong",
"Bruce Mellado"
] | 2023-10-04 08:30:22 | http://arxiv.org/abs/2310.04453v1 | http://arxiv.org/pdf/2310.04453v1 | 2310.04453v1 |
A Study of Quantisation-aware Training on Time Series Transformer Models for Resource-constrained FPGAs | This study explores the quantisation-aware training (QAT) on time series
Transformer models. We propose a novel adaptive quantisation scheme that
dynamically selects between symmetric and asymmetric schemes during the QAT
phase. Our approach demonstrates that matching the quantisation scheme to the
real data distribution can reduce computational overhead while maintaining
acceptable precision. Moreover, our approach is robust when applied to
real-world data and mixed-precision quantisation, where most objects are
quantised to 4 bits. Our findings inform model quantisation and deployment
decisions while providing a foundation for advancing quantisation techniques. | [
"Tianheng Ling",
"Chao Qian",
"Lukas Einhaus",
"Gregor Schiele"
] | 2023-10-04 08:25:03 | http://arxiv.org/abs/2310.02654v1 | http://arxiv.org/pdf/2310.02654v1 | 2310.02654v1 |
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning | The success of federated Learning (FL) depends on the quantity and quality of
the data owners (DOs) as well as their motivation to join FL model training.
Reputation-based FL participant selection methods have been proposed. However,
they still face the challenges of the cold start problem and potential
selection bias towards highly reputable DOs. Such a bias can result in lower
reputation DOs being prematurely excluded from future FL training rounds,
thereby reducing the diversity of training data and the generalizability of the
resulting models. To address these challenges, we propose the Gradual
Participant Selection scheme for Auction-based Federated Learning (GPS-AFL).
Unlike existing AFL incentive mechanisms which generally assume that all DOs
required for an FL task must be selected in one go, GPS-AFL gradually selects
the required DOs over multiple rounds of training as more information is
revealed through repeated interactions. It is designed to strike a balance
between cost saving and performance enhancement, while mitigating the drawbacks
of selection bias in reputation-based FL. Extensive experiments based on
real-world datasets demonstrate the significant advantages of GPS-AFL, which
reduces costs by 33.65% and improved total utility by 2.91%, on average
compared to the best-performing state-of-the-art approach. | [
"Xavier Tan",
"Han Yu"
] | 2023-10-04 08:19:04 | http://arxiv.org/abs/2310.02651v1 | http://arxiv.org/pdf/2310.02651v1 | 2310.02651v1 |
Foundation Reinforcement Learning: towards Embodied Generalist Agents with Foundation Prior Assistance | Recently, people have shown that large-scale pre-training from internet-scale
data is the key to building generalist models, as witnessed in NLP. To build
embodied generalist agents, we and many other researchers hypothesize that such
foundation prior is also an indispensable component. However, it is unclear
what is the proper concrete form to represent those embodied foundation priors
and how they should be used in the downstream task. In this paper, we propose
an intuitive and effective set of embodied priors that consist of foundation
policy, value, and success reward. The proposed priors are based on the
goal-conditioned MDP. To verify their effectiveness, we instantiate an
actor-critic method assisted by the priors, called Foundation Actor-Critic
(FAC). We name our framework as Foundation Reinforcement Learning (FRL), since
it completely relies on embodied foundation priors to explore, learn and
reinforce. The benefits of FRL are threefold. (1) Sample efficient. With
foundation priors, FAC learns significantly faster than traditional RL. Our
evaluation on the Meta-World has proved that FAC can achieve 100% success rates
for 7/8 tasks under less than 200k frames, which outperforms the baseline
method with careful manual-designed rewards under 1M frames. (2) Robust to
noisy priors. Our method tolerates the unavoidable noise in embodied foundation
models. We show that FAC works well even under heavy noise or quantization
errors. (3) Minimal human intervention: FAC completely learns from the
foundation priors, without the need of human-specified dense reward, or
providing teleoperated demos. Thus, FAC can be easily scaled up. We believe our
FRL framework could enable the future robot to autonomously explore and learn
without human intervention in the physical world. In summary, our proposed FRL
is a novel and powerful learning paradigm, towards achieving embodied
generalist agents. | [
"Weirui Ye",
"Yunsheng Zhang",
"Mengchen Wang",
"Shengjie Wang",
"Xianfan Gu",
"Pieter Abbeel",
"Yang Gao"
] | 2023-10-04 07:56:42 | http://arxiv.org/abs/2310.02635v2 | http://arxiv.org/pdf/2310.02635v2 | 2310.02635v2 |
Multi-rules mining algorithm for combinatorially exploded decision trees with modified Aitchison-Aitken function-based Bayesian optimization | Decision trees offer the benefit of easy interpretation because they allow
the classification of input data based on if--then rules. However, as decision
trees are constructed by an algorithm that achieves clear classification with
minimum necessary rules, the trees possess the drawback of extracting only
minimum rules, even when various latent rules exist in data. Approaches that
construct multiple trees using randomly selected feature subsets do exist.
However, the number of trees that can be constructed remains at the same scale
because the number of feature subsets is a combinatorial explosion.
Additionally, when multiple trees are constructed, numerous rules are
generated, of which several are untrustworthy and/or highly similar. Therefore,
we propose "MAABO-MT" and "GS-MRM" algorithms that strategically construct
trees with high estimation performance among all possible trees with small
computational complexity and extract only reliable and non-similar rules,
respectively. Experiments are conducted using several open datasets to analyze
the effectiveness of the proposed method. The results confirm that MAABO-MT can
discover reliable rules at a lower computational cost than other methods that
rely on randomness. Furthermore, the proposed method is confirmed to provide
deeper insights than single decision trees commonly used in previous studies.
Therefore, MAABO-MT and GS-MRM can efficiently extract rules from
combinatorially exploded decision trees. | [
"Yuto Omae",
"Masaya Mori",
"Yohei Kakimoto"
] | 2023-10-04 07:55:51 | http://arxiv.org/abs/2310.02633v1 | http://arxiv.org/pdf/2310.02633v1 | 2310.02633v1 |
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs | Generating realistic time series data is important for many engineering and
scientific applications. Existing work tackles this problem using generative
adversarial networks (GANs). However, GANs are often unstable during training,
and they can suffer from mode collapse. While variational autoencoders (VAEs)
are known to be more robust to these issues, they are (surprisingly) less often
considered for time series generation. In this work, we introduce Koopman VAE
(KVAE), a new generative framework that is based on a novel design for the
model prior, and that can be optimized for either regular and irregular
training data. Inspired by Koopman theory, we represent the latent conditional
prior dynamics using a linear map. Our approach enhances generative modeling
with two desired features: (i) incorporating domain knowledge can be achieved
by leverageing spectral tools that prescribe constraints on the eigenvalues of
the linear map; and (ii) studying the qualitative behavior and stablity of the
system can be performed using tools from dynamical systems theory. Our results
show that KVAE outperforms state-of-the-art GAN and VAE methods across several
challenging synthetic and real-world time series generation benchmarks. Whether
trained on regular or irregular data, KVAE generates time series that improve
both discriminative and predictive metrics. We also present visual evidence
suggesting that KVAE learns probability density functions that better
approximate empirical ground truth distributions. | [
"Ilan Naiman",
"N. Benjamin Erichson",
"Pu Ren",
"Michael W. Mahoney",
"Omri Azencot"
] | 2023-10-04 07:14:43 | http://arxiv.org/abs/2310.02619v1 | http://arxiv.org/pdf/2310.02619v1 | 2310.02619v1 |
Analyzing and Improving OT-based Adversarial Networks | Optimal Transport (OT) problem aims to find a transport plan that bridges two
distributions while minimizing a given cost function. OT theory has been widely
utilized in generative modeling. In the beginning, OT distance has been used as
a measure for assessing the distance between data and generated distributions.
Recently, OT transport map between data and prior distributions has been
utilized as a generative model. These OT-based generative models share a
similar adversarial training objective. In this paper, we begin by unifying
these OT-based adversarial methods within a single framework. Then, we
elucidate the role of each component in training dynamics through a
comprehensive analysis of this unified framework. Moreover, we suggest a simple
but novel method that improves the previously best-performing OT-based model.
Intuitively, our approach conducts a gradual refinement of the generated
distribution, progressively aligning it with the data distribution. Our
approach achieves a FID score of 2.51 on CIFAR-10, outperforming unified
OT-based adversarial approaches. | [
"Jaemoo Choi",
"Jaewoong Choi",
"Myungjoo Kang"
] | 2023-10-04 06:52:03 | http://arxiv.org/abs/2310.02611v1 | http://arxiv.org/pdf/2310.02611v1 | 2310.02611v1 |
Learning adjacency matrix for dynamic graph neural network | In recent work, [1] introduced the concept of using a Block Adjacency Matrix
(BA) for the representation of spatio-temporal data. While their method
successfully concatenated adjacency matrices to encapsulate spatio-temporal
relationships in a single graph, it formed a disconnected graph. This
limitation hampered the ability of Graph Convolutional Networks (GCNs) to
perform message passing across nodes belonging to different time steps, as no
temporal links were present. To overcome this challenge, we introduce an
encoder block specifically designed to learn these missing temporal links. The
encoder block processes the BA and predicts connections between previously
unconnected subgraphs, resulting in a Spatio-Temporal Block Adjacency Matrix
(STBAM). This enriched matrix is then fed into a Graph Neural Network (GNN) to
capture the complex spatio-temporal topology of the network. Our evaluations on
benchmark datasets, surgVisDom and C2D2, demonstrate that our method, with
slightly higher complexity, achieves superior results compared to
state-of-the-art results. Our approach's computational overhead remains
significantly lower than conventional non-graph-based methodologies for
spatio-temporal data. | [
"Osama Ahmad",
"Omer Abdul Jalil",
"Usman Nazir",
"Murtaza Taj"
] | 2023-10-04 06:42:33 | http://arxiv.org/abs/2310.02606v1 | http://arxiv.org/pdf/2310.02606v1 | 2310.02606v1 |
Analyzing Key Users' behavior trends in Volunteer-Based Networks | Online social networks usage has increased significantly in the last decade
and continues to grow in popularity. Multiple social platforms use volunteers
as a central component. The behavior of volunteers in volunteer-based networks
has been studied extensively in recent years. Here, we explore the development
of volunteer-based social networks, primarily focusing on their key users'
behaviors and activities. We developed two novel algorithms: the first reveals
key user behavior patterns over time; the second utilizes machine learning
methods to generate a forecasting model that can predict the future behavior of
key users, including whether they will remain active donors or change their
behavior to become mainly recipients, and vice-versa. These algorithms allowed
us to analyze the factors that significantly influence behavior predictions.
To evaluate our algorithms, we utilized data from over 2.4 million users on a
peer-to-peer food-sharing online platform. Using our algorithm, we identified
four main types of key user behavior patterns that occur over time. Moreover,
we succeeded in forecasting future active donor key users and predicting the
key users that would change their behavior to donors, with an accuracy of up to
89.6%. These findings provide valuable insights into the behavior of key users
in volunteer-based social networks and pave the way for more effective
communities-building in the future, while using the potential of machine
learning for this goal. | [
"Nofar Piterman",
"Tamar Makov",
"Michael Fire"
] | 2023-10-04 06:42:21 | http://arxiv.org/abs/2310.05978v1 | http://arxiv.org/pdf/2310.05978v1 | 2310.05978v1 |
Multi-Agent Reinforcement Learning for Power Grid Topology Optimization | Recent challenges in operating power networks arise from increasing energy
demands and unpredictable renewable sources like wind and solar. While
reinforcement learning (RL) shows promise in managing these networks, through
topological actions like bus and line switching, efficiently handling large
action spaces as networks grow is crucial. This paper presents a hierarchical
multi-agent reinforcement learning (MARL) framework tailored for these
expansive action spaces, leveraging the power grid's inherent hierarchical
nature. Experimental results indicate the MARL framework's competitive
performance with single-agent RL methods. We also compare different RL
algorithms for lower-level agents alongside different policies for higher-order
agents. | [
"Erica van der Sar",
"Alessandro Zocca",
"Sandjai Bhulai"
] | 2023-10-04 06:37:43 | http://arxiv.org/abs/2310.02605v1 | http://arxiv.org/pdf/2310.02605v1 | 2310.02605v1 |
ViT-ReciproCAM: Gradient and Attention-Free Visual Explanations for Vision Transformer | This paper presents a novel approach to address the challenges of
understanding the prediction process and debugging prediction errors in Vision
Transformers (ViT), which have demonstrated superior performance in various
computer vision tasks such as image classification and object detection. While
several visual explainability techniques, such as CAM, Grad-CAM, Score-CAM, and
Recipro-CAM, have been extensively researched for Convolutional Neural Networks
(CNNs), limited research has been conducted on ViT. Current state-of-the-art
solutions for ViT rely on class agnostic Attention-Rollout and Relevance
techniques. In this work, we propose a new gradient-free visual explanation
method for ViT, called ViT-ReciproCAM, which does not require attention matrix
and gradient information. ViT-ReciproCAM utilizes token masking and generated
new layer outputs from the target layer's input to exploit the correlation
between activated tokens and network predictions for target classes. Our
proposed method outperforms the state-of-the-art Relevance method in the
Average Drop-Coherence-Complexity (ADCC) metric by $4.58\%$ to $5.80\%$ and
generates more localized saliency maps. Our experiments demonstrate the
effectiveness of ViT-ReciproCAM and showcase its potential for understanding
and debugging ViT models. Our proposed method provides an efficient and
easy-to-implement alternative for generating visual explanations, without
requiring attention and gradient information, which can be beneficial for
various applications in the field of computer vision. | [
"Seok-Yong Byun",
"Wonju Lee"
] | 2023-10-04 05:09:50 | http://arxiv.org/abs/2310.02588v1 | http://arxiv.org/pdf/2310.02588v1 | 2310.02588v1 |
Machine Learning-Enabled Precision Position Control and Thermal Regulation in Advanced Thermal Actuators | With their unique combination of characteristics - an energy density almost
100 times that of human muscle, and a power density of 5.3 kW/kg, similar to a
jet engine's output - Nylon artificial muscles stand out as particularly apt
for robotics applications. However, the necessity of integrating sensors and
controllers poses a limitation to their practical usage. Here we report a
constant power open-loop controller based on machine learning. We show that we
can control the position of a nylon artificial muscle without external sensors.
To this end, we construct a mapping from a desired displacement trajectory to a
required power using an ensemble encoder-style feed-forward neural network. The
neural controller is carefully trained on a physics-based denoised dataset and
can be fine-tuned to accommodate various types of thermal artificial muscles,
irrespective of the presence or absence of hysteresis. | [
"Seyed Mo Mirvakili",
"Ehsan Haghighat",
"Douglas Sim"
] | 2023-10-04 05:01:47 | http://arxiv.org/abs/2310.02583v1 | http://arxiv.org/pdf/2310.02583v1 | 2310.02583v1 |
Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning | Recently, reinforcement learning has gained prominence in modern statistics,
with policy evaluation being a key component. Unlike traditional machine
learning literature on this topic, our work places emphasis on statistical
inference for the parameter estimates computed using reinforcement learning
algorithms. While most existing analyses assume random rewards to follow
standard distributions, limiting their applicability, we embrace the concept of
robust statistics in reinforcement learning by simultaneously addressing issues
of outlier contamination and heavy-tailed rewards within a unified framework.
In this paper, we develop an online robust policy evaluation procedure, and
establish the limiting distribution of our estimator, based on its Bahadur
representation. Furthermore, we develop a fully-online procedure to efficiently
conduct statistical inference based on the asymptotic distribution. This paper
bridges the gap between robust statistics and statistical inference in
reinforcement learning, offering a more versatile and reliable approach to
policy evaluation. Finally, we validate the efficacy of our algorithm through
numerical experiments conducted in real-world reinforcement learning
experiments. | [
"Weidong Liu",
"Jiyuan Tu",
"Yichen Zhang",
"Xi Chen"
] | 2023-10-04 04:57:35 | http://arxiv.org/abs/2310.02581v1 | http://arxiv.org/pdf/2310.02581v1 | 2310.02581v1 |
On the Stability of Expressive Positional Encodings for Graph Neural Networks | Designing effective positional encodings for graphs is key to building
powerful graph transformers and enhancing message-passing graph neural
networks. Although widespread, using Laplacian eigenvectors as positional
encodings faces two fundamental challenges: (1) \emph{Non-uniqueness}: there
are many different eigendecompositions of the same Laplacian, and (2)
\emph{Instability}: small perturbations to the Laplacian could result in
completely different eigenspaces, leading to unpredictable changes in
positional encoding.
Despite many attempts to address non-uniqueness, most methods overlook
stability, leading to poor generalization on unseen graph structures. We
identify the cause of instability to be a "hard partition" of eigenspaces.
Hence, we introduce Stable and Expressive Positional Encodings (SPE), an
architecture for processing eigenvectors that uses eigenvalues to "softly
partition" eigenspaces. SPE is the first architecture that is (1) provably
stable, and (2) universally expressive for basis invariant functions whilst
respecting all symmetries of eigenvectors. Besides guaranteed stability, we
prove that SPE is at least as expressive as existing methods, and highly
capable of counting graph structures. Finally, we evaluate the effectiveness of
our method on molecular property prediction, and out-of-distribution
generalization tasks, finding improved generalization compared to existing
positional encoding methods. | [
"Yinan Huang",
"William Lu",
"Joshua Robinson",
"Yu Yang",
"Muhan Zhang",
"Stefanie Jegelka",
"Pan Li"
] | 2023-10-04 04:48:55 | http://arxiv.org/abs/2310.02579v1 | http://arxiv.org/pdf/2310.02579v1 | 2310.02579v1 |
AdaMerging: Adaptive Model Merging for Multi-Task Learning | Multi-task learning (MTL) aims to empower a model to tackle multiple tasks
simultaneously. A recent development known as task arithmetic has revealed that
several models, each fine-tuned for distinct tasks, can be directly merged into
a single model to execute MTL without necessitating a retraining process using
the initial training data. Nevertheless, this direct addition of models often
leads to a significant deterioration in the overall performance of the merged
model. This decline occurs due to potential conflicts and intricate
correlations among the multiple tasks. Consequently, the challenge emerges of
how to merge pre-trained models more effectively without using their original
training data. This paper introduces an innovative technique called Adaptive
Model Merging (AdaMerging). This approach aims to autonomously learn the
coefficients for model merging, either in a task-wise or layer-wise manner,
without relying on the original training data. Specifically, our AdaMerging
method operates as an automatic, unsupervised task arithmetic scheme. It
leverages entropy minimization on unlabeled test samples from the multi-task
setup as a surrogate objective function to iteratively refine the merging
coefficients of the multiple models. Our experimental findings across eight
tasks demonstrate the efficacy of the AdaMerging scheme we put forth. Compared
to the current state-of-the-art task arithmetic merging scheme, AdaMerging
showcases a remarkable 11\% improvement in performance. Notably, AdaMerging
also exhibits superior generalization capabilities when applied to unseen
downstream tasks. Furthermore, it displays a significantly enhanced robustness
to data distribution shifts that may occur during the testing phase. | [
"Enneng Yang",
"Zhenyi Wang",
"Li Shen",
"Shiwei Liu",
"Guibing Guo",
"Xingwei Wang",
"Dacheng Tao"
] | 2023-10-04 04:26:33 | http://arxiv.org/abs/2310.02575v1 | http://arxiv.org/pdf/2310.02575v1 | 2310.02575v1 |
Improving Knowledge Distillation with Teacher's Explanation | Knowledge distillation (KD) improves the performance of a low-complexity
student model with the help of a more powerful teacher. The teacher in KD is a
black-box model, imparting knowledge to the student only through its
predictions. This limits the amount of transferred knowledge. In this work, we
introduce a novel Knowledge Explaining Distillation (KED) framework, which
allows the student to learn not only from the teacher's predictions but also
from the teacher's explanations. We propose a class of superfeature-explaining
teachers that provide explanation over groups of features, along with the
corresponding student model. We also present a method for constructing the
superfeatures. We then extend KED to reduce complexity in convolutional neural
networks, to allow augmentation with hidden-representation distillation
methods, and to work with a limited amount of training data using chimeric
sets. Our experiments over a variety of datasets show that KED students can
substantially outperform KD students of similar complexity. | [
"Sayantan Chowdhury",
"Ben Liang",
"Ali Tizghadam",
"Ilijc Albanese"
] | 2023-10-04 04:18:01 | http://arxiv.org/abs/2310.02572v1 | http://arxiv.org/pdf/2310.02572v1 | 2310.02572v1 |
Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks | Although pervasive spread of misinformation on social media platforms has
become a pressing challenge, existing platform interventions have shown limited
success in curbing its dissemination. In this study, we propose a stance-aware
graph neural network (stance-aware GNN) that leverages users' stances to
proactively predict misinformation spread. As different user stances can form
unique echo chambers, we customize four information passing paths in
stance-aware GNN, while the trainable attention weights provide explainability
by highlighting each structure's importance. Evaluated on a real-world dataset,
stance-aware GNN outperforms benchmarks by 32.65% and exceeds advanced GNNs
without user stance by over 4.69%. Furthermore, the attention weights indicate
that users' opposition stances have a higher impact on their neighbors'
behaviors than supportive ones, which function as social correction to halt
misinformation propagation. Overall, our study provides an effective predictive
model for platforms to combat misinformation, and highlights the impact of user
stances in the misinformation propagation. | [
"Zihan Chen",
"Jingyi Sun",
"Rong Liu",
"Feng Mai"
] | 2023-10-04 04:02:32 | http://arxiv.org/abs/2310.02568v1 | http://arxiv.org/pdf/2310.02568v1 | 2310.02568v1 |
Improving Automatic VQA Evaluation Using Large Language Models | 8 years after the visual question answering (VQA) task was proposed, accuracy
remains the primary metric for automatic evaluation. VQA Accuracy has been
effective so far in the IID evaluation setting. However, our community is
undergoing a shift towards open-ended generative models and OOD evaluation. In
this new paradigm, the existing VQA Accuracy metric is overly stringent and
underestimates the performance of VQA systems. Thus, there is a need to develop
more robust automatic VQA metrics that serve as a proxy for human judgment. In
this work, we propose to leverage the in-context learning capabilities of
instruction-tuned large language models (LLMs) to build a better VQA metric. We
formulate VQA evaluation as an answer-rating task where the LLM is instructed
to score the accuracy of a candidate answer given a set of reference answers.
We demonstrate the proposed metric better correlates with human judgment
compared to existing metrics across several VQA models and benchmarks. We hope
wide adoption of our metric will contribute to better estimating the research
progress on the VQA task. | [
"Oscar Mañas",
"Benno Krojer",
"Aishwarya Agrawal"
] | 2023-10-04 03:59:57 | http://arxiv.org/abs/2310.02567v1 | http://arxiv.org/pdf/2310.02567v1 | 2310.02567v1 |
Practical, Private Assurance of the Value of Collaboration | Two parties wish to collaborate on their datasets. However, before they
reveal their datasets to each other, the parties want to have the guarantee
that the collaboration would be fruitful. We look at this problem from the
point of view of machine learning, where one party is promised an improvement
on its prediction model by incorporating data from the other party. The parties
would only wish to collaborate further if the updated model shows an
improvement in accuracy. Before this is ascertained, the two parties would not
want to disclose their models and datasets. In this work, we construct an
interactive protocol for this problem based on the fully homomorphic encryption
scheme over the Torus (TFHE) and label differential privacy, where the
underlying machine learning model is a neural network. Label differential
privacy is used to ensure that computations are not done entirely in the
encrypted domain, which is a significant bottleneck for neural network training
according to the current state-of-the-art FHE implementations. We prove the
security of our scheme in the universal composability framework assuming
honest-but-curious parties, but where one party may not have any expertise in
labelling its initial dataset. Experiments show that we can obtain the output,
i.e., the accuracy of the updated model, with time many orders of magnitude
faster than a protocol using entirely FHE operations. | [
"Hassan Jameel Asghar",
"Zhigang Lu",
"Zhongrui Zhao",
"Dali Kaafar"
] | 2023-10-04 03:47:21 | http://arxiv.org/abs/2310.02563v1 | http://arxiv.org/pdf/2310.02563v1 | 2310.02563v1 |
Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework | Under the organization of the base station (BS), wireless federated learning
(FL) enables collaborative model training among multiple devices. However, the
BS is merely responsible for aggregating local updates during the training
process, which incurs a waste of the computational resource at the BS. To
tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to
leverage the computing capabilities of both the BS and devices for a hybrid
implementation of centralized learning (CL) and FL. Specifically, each device
sends both local gradients and data samples to the BS for training a shared
global model. To improve communication efficiency over the same time-frequency
resources, we integrate over-the-air computation for aggregation and
non-orthogonal multiple access for transmission by designing a novel
transceiver structure. To gain deep insights, we conduct convergence analysis
by deriving a closed-form optimality gap for SemiFL and extend the result to
two extra cases. In the first case, the BS uses all accumulated data samples to
calculate the CL gradient, while a decreasing learning rate is adopted in the
second case. Our analytical results capture the destructive effect of wireless
communication and show that both FL and CL are special cases of SemiFL. Then,
we formulate a non-convex problem to reduce the optimality gap by jointly
optimizing the transmit power and receive beamformers. Accordingly, we propose
a two-stage algorithm to solve this intractable problem, in which we provide
the closed-form solutions to the beamformers. Extensive simulation results on
two real-world datasets corroborate our theoretical analysis, and show that the
proposed SemiFL outperforms conventional FL and achieves 3.2% accuracy gain on
the MNIST dataset compared to state-of-the-art benchmarks. | [
"Jingheng Zheng",
"Wanli Ni",
"Hui Tian",
"Deniz Gunduz",
"Tony Q. S. Quek",
"Zhu Han"
] | 2023-10-04 03:32:39 | http://arxiv.org/abs/2310.02559v1 | http://arxiv.org/pdf/2310.02559v1 | 2310.02559v1 |
Generalization in diffusion models arises from geometry-adaptive harmonic representation | High-quality samples generated with score-based reverse diffusion algorithms
provide evidence that deep neural networks (DNN) trained for denoising can
learn high-dimensional densities, despite the curse of dimensionality. However,
recent reports of memorization of the training set raise the question of
whether these networks are learning the "true" continuous density of the data.
Here, we show that two denoising DNNs trained on non-overlapping subsets of a
dataset learn nearly the same score function, and thus the same density, with a
surprisingly small number of training images. This strong generalization
demonstrates an alignment of powerful inductive biases in the DNN architecture
and/or training algorithm with properties of the data distribution. We analyze
these, demonstrating that the denoiser performs a shrinkage operation in a
basis adapted to the underlying image. Examination of these bases reveals
oscillating harmonic structures along contours and in homogeneous image
regions. We show that trained denoisers are inductively biased towards these
geometry-adaptive harmonic representations by demonstrating that they arise
even when the network is trained on image classes such as low-dimensional
manifolds, for which the harmonic basis is suboptimal. Additionally, we show
that the denoising performance of the networks is near-optimal when trained on
regular image classes for which the optimal basis is known to be
geometry-adaptive and harmonic. | [
"Zahra Kadkhodaie",
"Florentin Guth",
"Eero P. Simoncelli",
"Stéphane Mallat"
] | 2023-10-04 03:30:32 | http://arxiv.org/abs/2310.02557v1 | http://arxiv.org/pdf/2310.02557v1 | 2310.02557v1 |
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning | Federated Learning (FL) is a machine learning paradigm, which enables
multiple and decentralized clients to collaboratively train a model under the
orchestration of a central aggregator. Traditional FL solutions rely on the
trust assumption of the centralized aggregator, which forms cohorts of clients
in a fair and honest manner. However, a malicious aggregator, in reality, could
abandon and replace the client's training models, or launch Sybil attacks to
insert fake clients. Such malicious behaviors give the aggregator more power to
control clients in the FL setting and determine the final training results. In
this work, we introduce zkFL, which leverages zero-knowledge proofs (ZKPs) to
tackle the issue of a malicious aggregator during the training model
aggregation process. To guarantee the correct aggregation results, the
aggregator needs to provide a proof per round. The proof can demonstrate to the
clients that the aggregator executes the intended behavior faithfully. To
further reduce the verification cost of clients, we employ a blockchain to
handle the proof in a zero-knowledge way, where miners (i.e., the nodes
validating and maintaining the blockchain data) can verify the proof without
knowing the clients' local and aggregated models. The theoretical analysis and
empirical results show that zkFL can achieve better security and privacy than
traditional FL, without modifying the underlying FL network structure or
heavily compromising the training speed. | [
"Zhipeng Wang",
"Nanqing Dong",
"Jiahao Sun",
"William Knottenbelt"
] | 2023-10-04 03:24:33 | http://arxiv.org/abs/2310.02554v3 | http://arxiv.org/pdf/2310.02554v3 | 2310.02554v3 |
Heterogeneous Federated Learning Using Knowledge Codistillation | Federated Averaging, and many federated learning algorithm variants which
build upon it, have a limitation: all clients must share the same model
architecture. This results in unused modeling capacity on many clients, which
limits model performance. To address this issue, we propose a method that
involves training a small model on the entire pool and a larger model on a
subset of clients with higher capacity. The models exchange information
bidirectionally via knowledge distillation, utilizing an unlabeled dataset on a
server without sharing parameters. We present two variants of our method, which
improve upon federated averaging on image classification and language modeling
tasks. We show this technique can be useful even if only out-of-domain or
limited in-domain distillation data is available. Additionally, the
bi-directional knowledge distillation allows for domain transfer between the
models when different pool populations introduce domain shift. | [
"Jared Lichtarge",
"Ehsan Amid",
"Shankar Kumar",
"Tien-Ju Yang",
"Rohan Anil",
"Rajiv Mathews"
] | 2023-10-04 03:17:26 | http://arxiv.org/abs/2310.02549v1 | http://arxiv.org/pdf/2310.02549v1 | 2310.02549v1 |
Exact and soft boundary conditions in Physics-Informed Neural Networks for the Variable Coefficient Poisson equation | Boundary conditions (BCs) are a key component in every Physics-Informed
Neural Network (PINN). By defining the solution to partial differential
equations (PDEs) along domain boundaries, BCs constrain the underlying boundary
value problem (BVP) that a PINN tries to approximate. Without them, unique PDE
solutions may not exist and finding approximations with PINNs would be a
challenging, if not impossible task. This study examines how soft loss-based
and exact distance function-based BC imposition approaches differ when applied
in PINNs. The well known variable coefficient Poisson equation serves as the
target PDE for all PINN models trained in this work. Besides comparing BC
imposition approaches, the goal of this work is to also provide resources on
how to implement these PINNs in practice. To this end, Keras models with
Tensorflow backend as well as a Python notebook with code examples and
step-by-step explanations on how to build soft/exact BC PINNs are published
alongside this review. | [
"Sebastian Barschkis"
] | 2023-10-04 03:16:03 | http://arxiv.org/abs/2310.02548v1 | http://arxiv.org/pdf/2310.02548v1 | 2310.02548v1 |
Joint Design of Protein Sequence and Structure based on Motifs | Designing novel proteins with desired functions is crucial in biology and
chemistry. However, most existing work focus on protein sequence design,
leaving protein sequence and structure co-design underexplored. In this paper,
we propose GeoPro, a method to design protein backbone structure and sequence
jointly. Our motivation is that protein sequence and its backbone structure
constrain each other, and thus joint design of both can not only avoid
nonfolding and misfolding but also produce more diverse candidates with desired
functions. To this end, GeoPro is powered by an equivariant encoder for
three-dimensional (3D) backbone structure and a protein sequence decoder guided
by 3D geometry. Experimental results on two biologically significant
metalloprotein datasets, including $\beta$-lactamases and myoglobins, show that
our proposed GeoPro outperforms several strong baselines on most metrics.
Remarkably, our method discovers novel $\beta$-lactamases and myoglobins which
are not present in protein data bank (PDB) and UniProt. These proteins exhibit
stable folding and active site environments reminiscent of those of natural
proteins, demonstrating their excellent potential to be biologically
functional. | [
"Zhenqiao Song",
"Yunlong Zhao",
"Yufei Song",
"Wenxian Shi",
"Yang Yang",
"Lei Li"
] | 2023-10-04 03:07:03 | http://arxiv.org/abs/2310.02546v1 | http://arxiv.org/pdf/2310.02546v1 | 2310.02546v1 |
Provable Tensor Completion with Graph Information | Graphs, depicting the interrelations between variables, has been widely used
as effective side information for accurate data recovery in various
matrix/tensor recovery related applications. In this paper, we study the tensor
completion problem with graph information. Current research on
graph-regularized tensor completion tends to be task-specific, lacking
generality and systematic approaches. Furthermore, a recovery theory to ensure
performance remains absent. Moreover, these approaches overlook the dynamic
aspects of graphs, treating them as static akin to matrices, even though graphs
could exhibit dynamism in tensor-related scenarios. To confront these
challenges, we introduce a pioneering framework in this paper that
systematically formulates a novel model, theory, and algorithm for solving the
dynamic graph regularized tensor completion problem. For the model, we
establish a rigorous mathematical representation of the dynamic graph, based on
which we derive a new tensor-oriented graph smoothness regularization. By
integrating this regularization into a tensor decomposition model based on
transformed t-SVD, we develop a comprehensive model simultaneously capturing
the low-rank and similarity structure of the tensor. In terms of theory, we
showcase the alignment between the proposed graph smoothness regularization and
a weighted tensor nuclear norm. Subsequently, we establish assurances of
statistical consistency for our model, effectively bridging a gap in the
theoretical examination of the problem involving tensor recovery with graph
information. In terms of the algorithm, we develop a solution of high
effectiveness, accompanied by a guaranteed convergence, to address the
resulting model. To showcase the prowess of our proposed model in contrast to
established ones, we provide in-depth numerical experiments encompassing
synthetic data as well as real-world datasets. | [
"Kaidong Wang",
"Yao Wang",
"Xiuwu Liao",
"Shaojie Tang",
"Can Yang",
"Deyu Meng"
] | 2023-10-04 02:55:10 | http://arxiv.org/abs/2310.02543v1 | http://arxiv.org/pdf/2310.02543v1 | 2310.02543v1 |
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data | Neural networks trained by gradient descent (GD) have exhibited a number of
surprising generalization behaviors. First, they can achieve a perfect fit to
noisy training data and still generalize near-optimally, showing that
overfitting can sometimes be benign. Second, they can undergo a period of
classical, harmful overfitting -- achieving a perfect fit to training data with
near-random performance on test data -- before transitioning ("grokking") to
near-optimal generalization later in training. In this work, we show that both
of these phenomena provably occur in two-layer ReLU networks trained by GD on
XOR cluster data where a constant fraction of the training labels are flipped.
In this setting, we show that after the first step of GD, the network achieves
100% training accuracy, perfectly fitting the noisy labels in the training
data, but achieves near-random test accuracy. At a later training step, the
network achieves near-optimal test accuracy while still fitting the random
labels in the training data, exhibiting a "grokking" phenomenon. This provides
the first theoretical result of benign overfitting in neural network
classification when the data distribution is not linearly separable. Our proofs
rely on analyzing the feature learning process under GD, which reveals that the
network implements a non-generalizable linear classifier after one step and
gradually learns generalizable features in later steps. | [
"Zhiwei Xu",
"Yutong Wang",
"Spencer Frei",
"Gal Vardi",
"Wei Hu"
] | 2023-10-04 02:50:34 | http://arxiv.org/abs/2310.02541v1 | http://arxiv.org/pdf/2310.02541v1 | 2310.02541v1 |
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data | Classical machine learning models, such as linear models and tree-based
models, are widely used in industry. These models are sensitive to data
distribution, thus feature preprocessing, which transforms features from one
distribution to another, is a crucial step to ensure good model quality.
Manually constructing a feature preprocessing pipeline is challenging because
data scientists need to make difficult decisions about which preprocessors to
select and in which order to compose them. In this paper, we study how to
automate feature preprocessing (Auto-FP) for tabular data. Due to the large
search space, a brute-force solution is prohibitively expensive. To address
this challenge, we interestingly observe that Auto-FP can be modelled as either
a hyperparameter optimization (HPO) or a neural architecture search (NAS)
problem. This observation enables us to extend a variety of HPO and NAS
algorithms to solve the Auto-FP problem. We conduct a comprehensive evaluation
and analysis of 15 algorithms on 45 public ML datasets. Overall,
evolution-based algorithms show the leading average ranking. Surprisingly, the
random search turns out to be a strong baseline. Many surrogate-model-based and
bandit-based search algorithms, which achieve good performance for HPO and NAS,
do not outperform random search for Auto-FP. We analyze the reasons for our
findings and conduct a bottleneck analysis to identify the opportunities to
improve these algorithms. Furthermore, we explore how to extend Auto-FP to
support parameter search and compare two ways to achieve this goal. In the end,
we evaluate Auto-FP in an AutoML context and discuss the limitations of popular
AutoML tools. To the best of our knowledge, this is the first study on
automated feature preprocessing. We hope our work can inspire researchers to
develop new algorithms tailored for Auto-FP. | [
"Danrui Qi",
"Jinglin Peng",
"Yongjun He",
"Jiannan Wang"
] | 2023-10-04 02:46:44 | http://arxiv.org/abs/2310.02540v1 | http://arxiv.org/pdf/2310.02540v1 | 2310.02540v1 |
Quantifying and mitigating the impact of label errors on model disparity metrics | Errors in labels obtained via human annotation adversely affect a model's
performance. Existing approaches propose ways to mitigate the effect of label
error on a model's downstream accuracy, yet little is known about its impact on
a model's disparity metrics. Here we study the effect of label error on a
model's disparity metrics. We empirically characterize how varying levels of
label error, in both training and test data, affect these disparity metrics. We
find that group calibration and other metrics are sensitive to train-time and
test-time label error -- particularly for minority groups. This disparate
effect persists even for models trained with noise-aware algorithms. To
mitigate the impact of training-time label error, we present an approach to
estimate the influence of a training input's label on a model's group disparity
metric. We empirically assess the proposed approach on a variety of datasets
and find significant improvement, compared to alternative approaches, in
identifying training inputs that improve a model's disparity metric. We
complement the approach with an automatic relabel-and-finetune scheme that
produces updated models with, provably, improved group calibration error. | [
"Julius Adebayo",
"Melissa Hall",
"Bowen Yu",
"Bobbie Chern"
] | 2023-10-04 02:18:45 | http://arxiv.org/abs/2310.02533v1 | http://arxiv.org/pdf/2310.02533v1 | 2310.02533v1 |
QuATON: Quantization Aware Training of Optical Neurons | Optical neural architectures (ONAs) use coding elements with optimized
physical parameters to perform intelligent measurements. However, fabricating
ONAs while maintaining design performances is challenging. Limitations in
fabrication techniques often limit the realizable precision of the trained
parameters. Physical constraints may also limit the range of values the
physical parameters can hold. Thus, ONAs should be trained within the
implementable constraints. However, such physics-based constraints reduce the
training objective to a constrained optimization problem, making it harder to
optimize with existing gradient-based methods. To alleviate these critical
issues that degrade performance from simulation to realization we propose a
physics-informed quantization-aware training framework. Our approach accounts
for the physical constraints during the training process, leading to robust
designs. We evaluate our approach on an ONA proposed in the literature, named a
diffractive deep neural network (D2NN), for all-optical phase imaging and for
classification of phase objects. With extensive experiments on different
quantization levels and datasets, we show that our approach leads to ONA
designs that are robust to quantization noise. | [
"Hasindu Kariyawasam",
"Ramith Hettiarachchi",
"Dushan Wadduwage"
] | 2023-10-04 02:18:28 | http://arxiv.org/abs/2310.03049v1 | http://arxiv.org/pdf/2310.03049v1 | 2310.03049v1 |
Federated Conditional Stochastic Optimization | Conditional stochastic optimization has found applications in a wide range of
machine learning tasks, such as invariant learning, AUPRC maximization, and
meta-learning. As the demand for training models with large-scale distributed
data grows in these applications, there is an increasing need for
communication-efficient distributed optimization algorithms, such as federated
learning algorithms. This paper considers the nonconvex conditional stochastic
optimization in federated learning and proposes the first federated conditional
stochastic optimization algorithm (FCSG) with a conditional stochastic gradient
estimator and a momentum-based algorithm (FCSG-M). To match the lower bound
complexity in the single-machine setting, we design an accelerated algorithm
(Acc-FCSG-M) via the variance reduction to achieve the best sample and
communication complexity. Compared with the existing optimization analysis for
MAML in FL, federated conditional stochastic optimization considers the sample
of tasks. Extensive experimental results on various tasks validate the
efficiency of these algorithms. | [
"Xidong Wu",
"Jianhui Sun",
"Zhengmian Hu",
"Junyi Li",
"Aidong Zhang",
"Heng Huang"
] | 2023-10-04 01:47:37 | http://arxiv.org/abs/2310.02524v1 | http://arxiv.org/pdf/2310.02524v1 | 2310.02524v1 |
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation | Health risk prediction is one of the fundamental tasks under predictive
modeling in the medical domain, which aims to forecast the potential health
risks that patients may face in the future using their historical Electronic
Health Records (EHR). Researchers have developed several risk prediction models
to handle the unique challenges of EHR data, such as its sequential nature,
high dimensionality, and inherent noise. These models have yielded impressive
results. Nonetheless, a key issue undermining their effectiveness is data
insufficiency. A variety of data generation and augmentation methods have been
introduced to mitigate this issue by expanding the size of the training data
set through the learning of underlying data distributions. However, the
performance of these methods is often limited due to their task-unrelated
design. To address these shortcomings, this paper introduces a novel,
end-to-end diffusion-based risk prediction model, named MedDiffusion. It
enhances risk prediction performance by creating synthetic patient data during
training to enlarge sample space. Furthermore, MedDiffusion discerns hidden
relationships between patient visits using a step-wise attention mechanism,
enabling the model to automatically retain the most vital information for
generating high-quality data. Experimental evaluation on four real-world
medical datasets demonstrates that MedDiffusion outperforms 14 cutting-edge
baselines in terms of PR-AUC, F1, and Cohen's Kappa. We also conduct ablation
studies and benchmark our model against GAN-based alternatives to further
validate the rationality and adaptability of our model design. Additionally, we
analyze generated data to offer fresh insights into the model's
interpretability. | [
"Yuan Zhong",
"Suhan Cui",
"Jiaqi Wang",
"Xiaochen Wang",
"Ziyi Yin",
"Yaqing Wang",
"Houping Xiao",
"Mengdi Huai",
"Ting Wang",
"Fenglong Ma"
] | 2023-10-04 01:36:30 | http://arxiv.org/abs/2310.02520v2 | http://arxiv.org/pdf/2310.02520v2 | 2310.02520v2 |
Parameterized Convex Minorant for Objective Function Approximation in Amortized Optimization | Parameterized convex minorant (PCM) method is proposed for the approximation
of the objective function in amortized optimization. In the proposed method,
the objective function approximator is expressed by the sum of a PCM and a
nonnegative gap function, where the objective function approximator is bounded
from below by the PCM convex in the optimization variable. The proposed
objective function approximator is a universal approximator for continuous
functions, and the global minimizer of the PCM attains the global minimum of
the objective function approximator. Therefore, the global minimizer of the
objective function approximator can be obtained by a single convex
optimization. As a realization of the proposed method, extended parameterized
log-sum-exp network is proposed by utilizing a parameterized log-sum-exp
network as the PCM. Numerical simulation is performed for
non-parameterized-convex objective function approximation and for
learning-based nonlinear model predictive control to demonstrate the
performance and characteristics of the proposed method. The simulation results
support that the proposed method can be used to learn objective functions and
to find the global minimizer reliably and quickly by using convex optimization
algorithms. | [
"Jinrae Kim",
"Youdan Kim"
] | 2023-10-04 01:34:36 | http://arxiv.org/abs/2310.02519v2 | http://arxiv.org/pdf/2310.02519v2 | 2310.02519v2 |
A Recipe for Improved Certifiable Robustness: Capacity and Data | A key challenge, supported both theoretically and empirically, is that
robustness demands greater network capacity and more data than standard
training. However, effectively adding capacity under stringent Lipschitz
constraints has proven more difficult than it may seem, evident by the fact
that state-of-the-art approach tend more towards \emph{underfitting} than
overfitting. Moreover, we posit that a lack of careful exploration of the
design space for Lipshitz-based approaches has left potential performance gains
on the table. In this work, we provide a more comprehensive evaluation to
better uncover the potential of Lipschitz-based certification methods. Using a
combination of novel techniques, design optimizations, and synthesis of prior
work, we are able to significantly improve the state-of-the-art \emph{verified
robust accuracy} (VRA) for deterministic certification on a variety of
benchmark datasets, and over a range of perturbation sizes. Of particular note,
we discover that the addition of large "Cholesky-orthogonalized residual dense"
layers to the end of existing state-of-the-art Lipschitz-controlled ResNet
architectures is especially effective for increasing network capacity and
performance. Combined with filtered generative data augmentation, our final
results further the state of the art deterministic VRA by up to 8.5 percentage
points. Code is available at \url{https://github.com/hukkai/liresnet}. | [
"Kai Hu",
"Klas Leino",
"Zifan Wang",
"Matt Fredrikson"
] | 2023-10-04 01:18:59 | http://arxiv.org/abs/2310.02513v1 | http://arxiv.org/pdf/2310.02513v1 | 2310.02513v1 |
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders | Three-dimensional native states of natural proteins display recurring and
hierarchical patterns. Yet, traditional graph-based modeling of protein
structures is often limited to operate within a single fine-grained resolution,
and lacks hourglass neural architectures to learn those high-level building
blocks. We narrow this gap by introducing Ophiuchus, an SO(3)-equivariant
coarse-graining model that efficiently operates on all heavy atoms of standard
protein residues, while respecting their relevant symmetries. Our model departs
from current approaches that employ graph modeling, instead focusing on local
convolutional coarsening to model sequence-motif interactions in log-linear
length complexity. We train Ophiuchus on contiguous fragments of PDB monomers,
investigating its reconstruction capabilities across different compression
rates. We examine the learned latent space and demonstrate its prompt usage in
conformational interpolation, comparing interpolated trajectories to structure
snapshots from the PDBFlex dataset. Finally, we leverage denoising diffusion
probabilistic models (DDPM) to efficiently sample readily-decodable latent
embeddings of diverse miniproteins. Our experiments demonstrate Ophiuchus to be
a scalable basis for efficient protein modeling and generation. | [
"Allan dos Santos Costa",
"Ilan Mitnikov",
"Mario Geiger",
"Manvitha Ponnapati",
"Tess Smidt",
"Joseph Jacobson"
] | 2023-10-04 01:01:11 | http://arxiv.org/abs/2310.02508v1 | http://arxiv.org/pdf/2310.02508v1 | 2310.02508v1 |
Learning to Reach Goals via Diffusion | Diffusion models are a powerful class of generative models capable of mapping
random noise in high-dimensional spaces to a target manifold through iterative
denoising. In this work, we present a novel perspective on goal-conditioned
reinforcement learning by framing it within the context of diffusion modeling.
Analogous to the diffusion process, where Gaussian noise is used to create
random trajectories that walk away from the data manifold, we construct
trajectories that move away from potential goal states. We then learn a
goal-conditioned policy analogous to the score function. This approach, which
we call Merlin, can reach predefined or novel goals from an arbitrary initial
state without learning a separate value function. We consider three choices for
the noise model to replace Gaussian noise in diffusion - reverse play from the
buffer, reverse dynamics model, and a novel non-parametric approach. We
theoretically justify our approach and validate it on offline goal-reaching
tasks. Empirical results are competitive with state-of-the-art methods, which
suggests this perspective on diffusion for RL is a simple, scalable, and
effective direction for sequential decision-making. | [
"Vineet Jain",
"Siamak Ravanbakhsh"
] | 2023-10-04 00:47:02 | http://arxiv.org/abs/2310.02505v1 | http://arxiv.org/pdf/2310.02505v1 | 2310.02505v1 |
Towards an Interpretable Representation of Speaker Identity via Perceptual Voice Qualities | Unlike other data modalities such as text and vision, speech does not lend
itself to easy interpretation. While lay people can understand how to describe
an image or sentence via perception, non-expert descriptions of speech often
end at high-level demographic information, such as gender or age. In this
paper, we propose a possible interpretable representation of speaker identity
based on perceptual voice qualities (PQs). By adding gendered PQs to the
pathology-focused Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V)
protocol, our PQ-based approach provides a perceptual latent space of the
character of adult voices that is an intermediary of abstraction between
high-level demographics and low-level acoustic, physical, or learned
representations. Contrary to prior belief, we demonstrate that these PQs are
hearable by ensembles of non-experts, and further demonstrate that the
information encoded in a PQ-based representation is predictable by various
speech representations. | [
"Robin Netzorg",
"Bohan Yu",
"Andrea Guzman",
"Peter Wu",
"Luna McNulty",
"Gopala Anumanchipalli"
] | 2023-10-04 00:06:17 | http://arxiv.org/abs/2310.02497v1 | http://arxiv.org/pdf/2310.02497v1 | 2310.02497v1 |
DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks | Deep operator networks (DeepONets, DONs) offer a distinct advantage over
traditional neural networks in their ability to be trained on multi-resolution
data. This property becomes especially relevant in real-world scenarios where
high-resolution measurements are difficult to obtain, while low-resolution data
is more readily available. Nevertheless, DeepONets alone often struggle to
capture and maintain dependencies over long sequences compared to other
state-of-the-art algorithms. We propose a novel architecture, named DON-LSTM,
which extends the DeepONet with a long short-term memory network (LSTM).
Combining these two architectures, we equip the network with explicit
mechanisms to leverage multi-resolution data, as well as capture temporal
dependencies in long sequences. We test our method on long-time-evolution
modeling of multiple non-linear systems and show that the proposed
multi-resolution DON-LSTM achieves significantly lower generalization error and
requires fewer high-resolution samples compared to its vanilla counterparts. | [
"Katarzyna Michałowska",
"Somdatta Goswami",
"George Em Karniadakis",
"Signe Riemer-Sørensen"
] | 2023-10-03 23:43:16 | http://arxiv.org/abs/2310.02491v1 | http://arxiv.org/pdf/2310.02491v1 | 2310.02491v1 |
ResidualTransformer: Residual Low-rank Learning with Weight-sharing for Transformer Layers | Memory constraint of always-on devices is one of the major concerns when
deploying speech processing models on these devices. While larger models
trained with sufficiently large amount of data generally perform better, making
them fit in the device memory is a demanding challenge. In this paper, we aim
to reduce model size by reparameterizing model weights across Transformer
encoder layers and assuming a special weight composition and structure. More
specifically, inspired by ResNet and the more recent LoRA work, we propose an
approach named ResidualTransformer, where each weight matrix in a Transformer
layer comprises 1) a shared full-rank component with its adjacent layers, and
2) a unique low-rank component to itself. The low-rank matrices only account
for a small amount of model size increase. In addition, we add diagonal weight
matrices to improve modeling capacity of the low-rank matrices. Experiments of
our 10k-hour speech recognition and speech translation tasks show that the
Transformer encoder size can be reduced by ~3X with very slight performance
degradation. | [
"Yiming Wang",
"Jinyu Li"
] | 2023-10-03 23:31:48 | http://arxiv.org/abs/2310.02489v1 | http://arxiv.org/pdf/2310.02489v1 | 2310.02489v1 |
OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation | Accurate detection of oral cancer is crucial for improving patient outcomes.
However, the field faces two key challenges: the scarcity of deep
learning-based image segmentation research specifically targeting oral cancer
and the lack of annotated data. Our study proposes OCU-Net, a pioneering U-Net
image segmentation architecture exclusively designed to detect oral cancer in
hematoxylin and eosin (H&E) stained image datasets. OCU-Net incorporates
advanced deep learning modules, such as the Channel and Spatial Attention
Fusion (CSAF) module, a novel and innovative feature that emphasizes important
channel and spatial areas in H&E images while exploring contextual information.
In addition, OCU-Net integrates other innovative components such as
Squeeze-and-Excite (SE) attention module, Atrous Spatial Pyramid Pooling (ASPP)
module, residual blocks, and multi-scale fusion. The incorporation of these
modules showed superior performance for oral cancer segmentation for two
datasets used in this research. Furthermore, we effectively utilized the
efficient ImageNet pre-trained MobileNet-V2 model as a backbone of our OCU-Net
to create OCU-Netm, an enhanced version achieving state-of-the-art results.
Comprehensive evaluation demonstrates that OCU-Net and OCU-Netm outperformed
existing segmentation methods, highlighting their precision in identifying
cancer cells in H&E images from OCDC and ORCA datasets. | [
"Ahmed Albishri",
"Syed Jawad Hussain Shah",
"Yugyung Lee",
"Rong Wang"
] | 2023-10-03 23:25:19 | http://arxiv.org/abs/2310.02486v1 | http://arxiv.org/pdf/2310.02486v1 | 2310.02486v1 |
Splitting the Difference on Adversarial Training | The existence of adversarial examples points to a basic weakness of deep
neural networks. One of the most effective defenses against such examples,
adversarial training, entails training models with some degree of robustness,
usually at the expense of a degraded natural accuracy. Most adversarial
training methods aim to learn a model that finds, for each class, a common
decision boundary encompassing both the clean and perturbed examples. In this
work, we take a fundamentally different approach by treating the perturbed
examples of each class as a separate class to be learned, effectively splitting
each class into two classes: "clean" and "adversarial." This split doubles the
number of classes to be learned, but at the same time considerably simplifies
the decision boundaries. We provide a theoretical plausibility argument that
sheds some light on the conditions under which our approach can be expected to
be beneficial. Likewise, we empirically demonstrate that our method learns
robust models while attaining optimal or near-optimal natural accuracy, e.g.,
on CIFAR-10 we obtain near-optimal natural accuracy of $95.01\%$ alongside
significant robustness across multiple tasks. The ability to achieve such
near-optimal natural accuracy, while maintaining a significant level of
robustness, makes our method applicable to real-world applications where
natural accuracy is at a premium. As a whole, our main contribution is a
general method that confers a significant level of robustness upon classifiers
with only minor or negligible degradation of their natural accuracy. | [
"Matan Levi",
"Aryeh Kontorovich"
] | 2023-10-03 23:09:47 | http://arxiv.org/abs/2310.02480v1 | http://arxiv.org/pdf/2310.02480v1 | 2310.02480v1 |
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning | Understanding the key factors shaping environmental hazard exposures and
their associated environmental injustice issues is vital for formulating
equitable policy measures. Traditional perspectives on environmental injustice
have primarily focused on the socioeconomic dimensions, often overlooking the
influence of heterogeneous urban characteristics. This limited view may
obstruct a comprehensive understanding of the complex nature of environmental
justice and its relationship with urban design features. To address this gap,
this study creates an interpretable machine learning model to examine the
effects of various urban features and their non-linear interactions to the
exposure disparities of three primary hazards: air pollution, urban heat, and
flooding. The analysis trains and tests models with data from six metropolitan
counties in the United States using Random Forest and XGBoost. The performance
is used to measure the extent to which variations of urban features shape
disparities in environmental hazard levels. In addition, the analysis of
feature importance reveals features related to social-demographic
characteristics as the most prominent urban features that shape hazard extent.
Features related to infrastructure distribution and land cover are relatively
important for urban heat and air pollution exposure respectively. Moreover, we
evaluate the models' transferability across different regions and hazards. The
results highlight limited transferability, underscoring the intricate
differences among hazards and regions and the way in which urban features shape
hazard exposures. The insights gleaned from this study offer fresh perspectives
on the relationship among urban features and their interplay with environmental
hazard exposure disparities, informing the development of more integrated urban
design policies to enhance social equity and environmental injustice issues. | [
"Yu-Hsuan Ho",
"Zhewei Liu",
"Cheng-Chun Lee",
"Ali Mostafavi"
] | 2023-10-03 22:48:58 | http://arxiv.org/abs/2310.02476v1 | http://arxiv.org/pdf/2310.02476v1 | 2310.02476v1 |
Prompting-based Efficient Temporal Domain Generalization | Machine learning traditionally assumes that training and testing data are
distributed independently and identically. However, in many real-world
settings, the data distribution can shift over time, leading to poor
generalization of trained models in future time periods. Our paper presents a
novel prompting-based approach to temporal domain generalization that is
parameter-efficient, time-efficient, and does not require access to the target
domain data (i.e., unseen future time periods) during training. Our method
adapts a target pre-trained model to temporal drift by learning global prompts,
domain-specific prompts, and drift-aware prompts that capture underlying
temporal dynamics. It is compatible across diverse tasks, such as
classification, regression, and time series forecasting, and sets a new
state-of-the-art benchmark in temporal domain generalization. The code
repository will be publicly shared. | [
"Sepidehsadat Hosseini",
"Mengyao Zhai",
"Hossein Hajimirsadegh",
"Frederick Tung"
] | 2023-10-03 22:40:56 | http://arxiv.org/abs/2310.02473v1 | http://arxiv.org/pdf/2310.02473v1 | 2310.02473v1 |
Large Language Models Can Be Good Privacy Protection Learners | The proliferation of Large Language Models (LLMs) has driven considerable
interest in fine-tuning them with domain-specific data to create specialized
language models. Nevertheless, such domain-specific fine-tuning data often
contains sensitive personally identifiable information (PII). Direct
fine-tuning LLMs on this data without privacy protection poses a risk of
leakage. To address this challenge, we introduce Privacy Protection Language
Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects
domain-specific knowledge while safeguarding data privacy. Our work offers a
theoretical analysis for model design and delves into various techniques such
as corpus curation, penalty-based unlikelihood in training loss, and
instruction-based tuning, etc. Extensive experiments across diverse datasets
and scenarios demonstrate the effectiveness of our approaches. In particular,
instruction tuning with both positive and negative examples, stands out as a
promising method, effectively protecting private data while enhancing the
model's knowledge. Our work underscores the potential for Large Language Models
as robust privacy protection learners. | [
"Yijia Xiao",
"Yiqiao Jin",
"Yushi Bai",
"Yue Wu",
"Xianjun Yang",
"Xiao Luo",
"Wenchao Yu",
"Xujiang Zhao",
"Yanchi Liu",
"Haifeng Chen",
"Wei Wang",
"Wei Cheng"
] | 2023-10-03 22:37:01 | http://arxiv.org/abs/2310.02469v1 | http://arxiv.org/pdf/2310.02469v1 | 2310.02469v1 |
Differentiable Chemical Physics by Geometric Deep Learning for Gradient-based Property Optimization of Mixtures | Chemical mixtures, satisfying multi-objective performance metrics and
constraints, enable their use in chemical processes and electrochemical
devices. In this work, we develop a differentiable chemical-physics framework
for modeling chemical mixtures, DiffMix, where geometric deep learning (GDL) is
leveraged to map from molecular species, compositions and environment
conditions, to physical coefficients in the mixture physics laws. In
particular, we extend mixture thermodynamic and transport laws by creating
learnable physical coefficients, where we use graph neural networks as the
molecule encoder and enforce component-wise permutation-invariance. We start
our model evaluations with thermodynamics of binary mixtures, and further
benchmarked multicomponent electrolyte mixtures on their transport properties,
in order to test the model generalizability. We show improved prediction
accuracy and model robustness of DiffMix than its purely data-driven variants.
Furthermore, we demonstrate the efficient optimization of electrolyte transport
properties, built on the gradient obtained using DiffMix auto-differentiation.
Our simulation runs are then backed up by the data generated by a robotic
experimentation setup, Clio. By combining mixture physics and GDL, DiffMix
expands the predictive modeling methods for chemical mixtures and provides
low-cost optimization approaches in large chemical spaces. | [
"Shang Zhu",
"Bharath Ramsundar",
"Emil Annevelink",
"Hongyi Lin",
"Adarsh Dave",
"Pin-Wen Guan",
"Kevin Gering",
"Venkatasubramanian Viswanathan"
] | 2023-10-03 22:26:38 | http://arxiv.org/abs/2310.03047v1 | http://arxiv.org/pdf/2310.03047v1 | 2310.03047v1 |
Short text classification with machine learning in the social sciences: The case of climate change on Twitter | To analyse large numbers of texts, social science researchers are
increasingly confronting the challenge of text classification. When manual
labeling is not possible and researchers have to find automatized ways to
classify texts, computer science provides a useful toolbox of machine-learning
methods whose performance remains understudied in the social sciences. In this
article, we compare the performance of the most widely used text classifiers by
applying them to a typical research scenario in social science research: a
relatively small labeled dataset with infrequent occurrence of categories of
interest, which is a part of a large unlabeled dataset. As an example case, we
look at Twitter communication regarding climate change, a topic of increasing
scholarly interest in interdisciplinary social science research. Using a novel
dataset including 5,750 tweets from various international organizations
regarding the highly ambiguous concept of climate change, we evaluate the
performance of methods in automatically classifying tweets based on whether
they are about climate change or not. In this context, we highlight two main
findings. First, supervised machine-learning methods perform better than
state-of-the-art lexicons, in particular as class balance increases. Second,
traditional machine-learning methods, such as logistic regression and random
forest, perform similarly to sophisticated deep-learning methods, whilst
requiring much less training time and computational resources. The results have
important implications for the analysis of short texts in social science
research. | [
"Karina Shyrokykh",
"Maksym Girnyk",
"Lisa Dellmuth"
] | 2023-10-03 22:09:43 | http://arxiv.org/abs/2310.04452v1 | http://arxiv.org/pdf/2310.04452v1 | 2310.04452v1 |
Distributionally Safe Reinforcement Learning under Model Uncertainty: A Single-Level Approach by Differentiable Convex Programming | Safety assurance is uncompromisable for safety-critical environments with the
presence of drastic model uncertainties (e.g., distributional shift),
especially with humans in the loop. However, incorporating uncertainty in safe
learning will naturally lead to a bi-level problem, where at the lower level
the (worst-case) safety constraint is evaluated within the uncertainty
ambiguity set. In this paper, we present a tractable distributionally safe
reinforcement learning framework to enforce safety under a distributional shift
measured by a Wasserstein metric. To improve the tractability, we first use
duality theory to transform the lower-level optimization from
infinite-dimensional probability space where distributional shift is measured,
to a finite-dimensional parametric space. Moreover, by differentiable convex
programming, the bi-level safe learning problem is further reduced to a
single-level one with two sequential computationally efficient modules: a
convex quadratic program to guarantee safety followed by a projected gradient
ascent to simultaneously find the worst-case uncertainty. This end-to-end
differentiable framework with safety constraints, to the best of our knowledge,
is the first tractable single-level solution to address distributional safety.
We test our approach on first and second-order systems with varying
complexities and compare our results with the uncertainty-agnostic policies,
where our approach demonstrates a significant improvement on safety guarantees. | [
"Alaa Eddine Chriat",
"Chuangchuang Sun"
] | 2023-10-03 22:05:05 | http://arxiv.org/abs/2310.02459v1 | http://arxiv.org/pdf/2310.02459v1 | 2310.02459v1 |
Learning Optimal Advantage from Preferences and Mistaking it for Reward | We consider algorithms for learning reward functions from human preferences
over pairs of trajectory segments, as used in reinforcement learning from human
feedback (RLHF). Most recent work assumes that human preferences are generated
based only upon the reward accrued within those segments, or their partial
return. Recent work casts doubt on the validity of this assumption, proposing
an alternative preference model based upon regret. We investigate the
consequences of assuming preferences are based upon partial return when they
actually arise from regret. We argue that the learned function is an
approximation of the optimal advantage function, $\hat{A^*_r}$, not a reward
function. We find that if a specific pitfall is addressed, this incorrect
assumption is not particularly harmful, resulting in a highly shaped reward
function. Nonetheless, this incorrect usage of $\hat{A^*_r}$ is less desirable
than the appropriate and simpler approach of greedy maximization of
$\hat{A^*_r}$. From the perspective of the regret preference model, we also
provide a clearer interpretation of fine tuning contemporary large language
models with RLHF. This paper overall provides insight regarding why learning
under the partial return preference model tends to work so well in practice,
despite it conforming poorly to how humans give preferences. | [
"W. Bradley Knox",
"Stephane Hatgis-Kessell",
"Sigurdur Orn Adalgeirsson",
"Serena Booth",
"Anca Dragan",
"Peter Stone",
"Scott Niekum"
] | 2023-10-03 21:58:24 | http://arxiv.org/abs/2310.02456v1 | http://arxiv.org/pdf/2310.02456v1 | 2310.02456v1 |
Dual-stage Flows-based Generative Modeling for Traceable Urban Planning | Urban planning, which aims to design feasible land-use configurations for
target areas, has become increasingly essential due to the high-speed
urbanization process in the modern era. However, the traditional urban planning
conducted by human designers can be a complex and onerous task. Thanks to the
advancement of deep learning algorithms, researchers have started to develop
automated planning techniques. While these models have exhibited promising
results, they still grapple with a couple of unresolved limitations: 1)
Ignoring the relationship between urban functional zones and configurations and
failing to capture the relationship among different functional zones. 2) Less
interpretable and stable generation process. To overcome these limitations, we
propose a novel generative framework based on normalizing flows, namely
Dual-stage Urban Flows (DSUF) framework. Specifically, the first stage is to
utilize zone-level urban planning flows to generate urban functional zones
based on given surrounding contexts and human guidance. Then we employ an
Information Fusion Module to capture the relationship among functional zones
and fuse the information of different aspects. The second stage is to use
configuration-level urban planning flows to obtain land-use configurations
derived from fused information. We design several experiments to indicate that
our framework can outperform compared to other generative models for the urban
planning task. | [
"Xuanming Hu",
"Wei Fan",
"Dongjie Wang",
"Pengyang Wang",
"Yong Li",
"Yanjie Fu"
] | 2023-10-03 21:49:49 | http://arxiv.org/abs/2310.02453v1 | http://arxiv.org/pdf/2310.02453v1 | 2310.02453v1 |
Feather: An Elegant Solution to Effective DNN Sparsification | Neural Network pruning is an increasingly popular way for producing compact
and efficient models, suitable for resource-limited environments, while
preserving high performance. While the pruning can be performed using a
multi-cycle training and fine-tuning process, the recent trend is to encompass
the sparsification process during the standard course of training. To this end,
we introduce Feather, an efficient sparse training module utilizing the
powerful Straight-Through Estimator as its core, coupled with a new
thresholding operator and a gradient scaling technique, enabling robust,
out-of-the-box sparsification performance. Feather's effectiveness and
adaptability is demonstrated using various architectures on the CIFAR dataset,
while on ImageNet it achieves state-of-the-art Top-1 validation accuracy using
the ResNet-50 architecture, surpassing existing methods, including more complex
and computationally heavy ones, by a considerable margin. Code is publicly
available at https://github.com/athglentis/feather . | [
"Athanasios Glentis Georgoulakis",
"George Retsinas",
"Petros Maragos"
] | 2023-10-03 21:37:13 | http://arxiv.org/abs/2310.02448v1 | http://arxiv.org/pdf/2310.02448v1 | 2310.02448v1 |
Machine learning assist nyc subway navigation safer and faster | Mainstream navigation software, like Google and Apple Maps, often lacks the
ability to provide routes prioritizing safety. However, safety remains a
paramount concern for many. Our aim is to strike a balance between safety and
efficiency. To achieve this, we're devising an Integer Programming model that
takes into account both the shortest path and the safest route. We will harness
machine learning to derive safety coefficients, employing methodologies such as
generalized linear models, linear regression, and recurrent neural networks.
Our evaluation will be based on the Root Mean Square Error (RMSE) across
various subway stations, helping us identify the most accurate model for safety
coefficient estimation. Furthermore, we'll conduct a comprehensive review of
different shortest-path algorithms, assessing them based on time complexity and
real-world data to determine their appropriateness in merging both safety and
time efficiency. | [
"Wencheng Bao",
"Shi Feng"
] | 2023-10-03 21:31:21 | http://arxiv.org/abs/2310.02447v1 | http://arxiv.org/pdf/2310.02447v1 | 2310.02447v1 |
Low-Resource Languages Jailbreak GPT-4 | AI safety training and red-teaming of large language models (LLMs) are
measures to mitigate the generation of unsafe content. Our work exposes the
inherent cross-lingual vulnerability of these safety mechanisms, resulting from
the linguistic inequality of safety training data, by successfully
circumventing GPT-4's safeguard through translating unsafe English inputs into
low-resource languages. On the AdvBenchmark, GPT-4 engages with the unsafe
translated inputs and provides actionable items that can get the users towards
their harmful goals 79% of the time, which is on par with or even surpassing
state-of-the-art jailbreaking attacks. Other high-/mid-resource languages have
significantly lower attack success rate, which suggests that the cross-lingual
vulnerability mainly applies to low-resource languages. Previously, limited
training on low-resource languages primarily affects speakers of those
languages, causing technological disparities. However, our work highlights a
crucial shift: this deficiency now poses a risk to all LLMs users. Publicly
available translation APIs enable anyone to exploit LLMs' safety
vulnerabilities. Therefore, our work calls for a more holistic red-teaming
efforts to develop robust multilingual safeguards with wide language coverage. | [
"Zheng-Xin Yong",
"Cristina Menghini",
"Stephen H. Bach"
] | 2023-10-03 21:30:56 | http://arxiv.org/abs/2310.02446v1 | http://arxiv.org/pdf/2310.02446v1 | 2310.02446v1 |
GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature | Generating diverse objects (e.g., images) using generative models (such as
GAN or VAE) has achieved impressive results in the recent years, to help solve
many design problems that are traditionally done by humans. Going beyond image
generation, we aim to find solutions to more general design problems, in which
both the diversity of the design and conformity of constraints are important.
Such a setting has applications in computer graphics, animation, industrial
design, material science, etc, in which we may want the output of the generator
to follow discrete/combinatorial constraints and penalize any deviation, which
is non-trivial with existing generative models and optimization solvers. To
address this, we propose GenCO, a novel framework that conducts end-to-end
training of deep generative models integrated with embedded combinatorial
solvers, aiming to uncover high-quality solutions aligned with nonlinear
objectives. While structurally akin to conventional generative models, GenCO
diverges in its role - it focuses on generating instances of combinatorial
optimization problems rather than final objects (e.g., images). This shift
allows finer control over the generated outputs, enabling assessments of their
feasibility and introducing an additional combinatorial loss component. We
demonstrate the effectiveness of our approach on a variety of generative tasks
characterized by combinatorial intricacies, including game level generation and
map creation for path planning, consistently demonstrating its capability to
yield diverse, high-quality solutions that reliably adhere to user-specified
combinatorial properties. | [
"Aaron Ferber",
"Arman Zharmagambetov",
"Taoan Huang",
"Bistra Dilkina",
"Yuandong Tian"
] | 2023-10-03 21:23:39 | http://arxiv.org/abs/2310.02442v1 | http://arxiv.org/pdf/2310.02442v1 | 2310.02442v1 |
Episodic Memory Theory for the Mechanistic Interpretation of Recurrent Neural Networks | Understanding the intricate operations of Recurrent Neural Networks (RNNs)
mechanistically is pivotal for advancing their capabilities and applications.
In this pursuit, we propose the Episodic Memory Theory (EMT), illustrating that
RNNs can be conceptualized as discrete-time analogs of the recently proposed
General Sequential Episodic Memory Model. To substantiate EMT, we introduce a
novel set of algorithmic tasks tailored to probe the variable binding behavior
in RNNs. Utilizing the EMT, we formulate a mathematically rigorous circuit that
facilitates variable binding in these tasks. Our empirical investigations
reveal that trained RNNs consistently converge to the variable binding circuit,
thus indicating universality in the dynamics of RNNs. Building on these
findings, we devise an algorithm to define a privileged basis, which reveals
hidden neurons instrumental in the temporal storage and composition of
variables, a mechanism vital for the successful generalization in these tasks.
We show that the privileged basis enhances the interpretability of the learned
parameters and hidden states of RNNs. Our work represents a step toward
demystifying the internal mechanisms of RNNs and, for computational
neuroscience, serves to bridge the gap between artificial neural networks and
neural memory models. | [
"Arjun Karuvally",
"Peter Delmastro",
"Hava T. Siegelmann"
] | 2023-10-03 20:52:37 | http://arxiv.org/abs/2310.02430v1 | http://arxiv.org/pdf/2310.02430v1 | 2310.02430v1 |
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations | Equivariant graph neural networks force fields (EGraFFs) have shown great
promise in modelling complex interactions in atomic systems by exploiting the
graphs' inherent symmetries. Recent works have led to a surge in the
development of novel architectures that incorporate equivariance-based
inductive biases alongside architectural innovations like graph transformers
and message passing to model atomic interactions. However, thorough evaluations
of these deploying EGraFFs for the downstream task of real-world atomistic
simulations, is lacking. To this end, here we perform a systematic benchmarking
of 6 EGraFF algorithms (NequIP, Allegro, BOTNet, MACE, Equiformer, TorchMDNet),
with the aim of understanding their capabilities and limitations for realistic
atomistic simulations. In addition to our thorough evaluation and analysis on
eight existing datasets based on the benchmarking literature, we release two
new benchmark datasets, propose four new metrics, and three new challenging
tasks. The new datasets and tasks evaluate the performance of EGraFF to
out-of-distribution data, in terms of different crystal structures,
temperatures, and new molecules. Interestingly, evaluation of the EGraFF models
based on dynamic simulations reveals that having a lower error on energy or
force does not guarantee stable or reliable simulation or faithful replication
of the atomic structures. Moreover, we find that no model clearly outperforms
other models on all datasets and tasks. Importantly, we show that the
performance of all the models on out-of-distribution datasets is unreliable,
pointing to the need for the development of a foundation model for force fields
that can be used in real-world simulations. In summary, this work establishes a
rigorous framework for evaluating machine learning force fields in the context
of atomic simulations and points to open research challenges within this
domain. | [
"Vaibhav Bihani",
"Utkarsh Pratiush",
"Sajid Mannan",
"Tao Du",
"Zhimin Chen",
"Santiago Miret",
"Matthieu Micoulaut",
"Morten M Smedskjaer",
"Sayan Ranu",
"N M Anoop Krishnan"
] | 2023-10-03 20:49:00 | http://arxiv.org/abs/2310.02428v1 | http://arxiv.org/pdf/2310.02428v1 | 2310.02428v1 |
Delta-AI: Local objectives for amortized inference in sparse graphical models | We present a new algorithm for amortized inference in sparse probabilistic
graphical models (PGMs), which we call $\Delta$-amortized inference
($\Delta$-AI). Our approach is based on the observation that when the sampling
of variables in a PGM is seen as a sequence of actions taken by an agent,
sparsity of the PGM enables local credit assignment in the agent's policy
learning objective. This yields a local constraint that can be turned into a
local loss in the style of generative flow networks (GFlowNets) that enables
off-policy training but avoids the need to instantiate all the random variables
for each parameter update, thus speeding up training considerably. The
$\Delta$-AI objective matches the conditional distribution of a variable given
its Markov blanket in a tractable learned sampler, which has the structure of a
Bayesian network, with the same conditional distribution under the target PGM.
As such, the trained sampler recovers marginals and conditional distributions
of interest and enables inference of partial subsets of variables. We
illustrate $\Delta$-AI's effectiveness for sampling from synthetic PGMs and
training latent variable models with sparse factor structure. | [
"Jean-Pierre Falet",
"Hae Beom Lee",
"Nikolay Malkin",
"Chen Sun",
"Dragos Secrieru",
"Dinghuai Zhang",
"Guillaume Lajoie",
"Yoshua Bengio"
] | 2023-10-03 20:37:03 | http://arxiv.org/abs/2310.02423v1 | http://arxiv.org/pdf/2310.02423v1 | 2310.02423v1 |
OneAdapt: Fast Adaptation for Deep Learning Applications via Backpropagation | Deep learning inference on streaming media data, such as object detection in
video or LiDAR feeds and text extraction from audio waves, is now ubiquitous.
To achieve high inference accuracy, these applications typically require
significant network bandwidth to gather high-fidelity data and extensive GPU
resources to run deep neural networks (DNNs). While the high demand for network
bandwidth and GPU resources could be substantially reduced by optimally
adapting the configuration knobs, such as video resolution and frame rate,
current adaptation techniques fail to meet three requirements simultaneously:
adapt configurations (i) with minimum extra GPU or bandwidth overhead; (ii) to
reach near-optimal decisions based on how the data affects the final DNN's
accuracy, and (iii) do so for a range of configuration knobs. This paper
presents OneAdapt, which meets these requirements by leveraging a
gradient-ascent strategy to adapt configuration knobs. The key idea is to
embrace DNNs' differentiability to quickly estimate the accuracy's gradient to
each configuration knob, called AccGrad. Specifically, OneAdapt estimates
AccGrad by multiplying two gradients: InputGrad (i.e. how each configuration
knob affects the input to the DNN) and DNNGrad (i.e. how the DNN input affects
the DNN inference output). We evaluate OneAdapt across five types of
configurations, four analytic tasks, and five types of input data. Compared to
state-of-the-art adaptation schemes, OneAdapt cuts bandwidth usage and GPU
usage by 15-59% while maintaining comparable accuracy or improves accuracy by
1-5% while using equal or fewer resources. | [
"Kuntai Du",
"Yuhan Liu",
"Yitian Hao",
"Qizheng Zhang",
"Haodong Wang",
"Yuyang Huang",
"Ganesh Ananthanarayanan",
"Junchen Jiang"
] | 2023-10-03 20:36:03 | http://arxiv.org/abs/2310.02422v1 | http://arxiv.org/pdf/2310.02422v1 | 2310.02422v1 |
Can a student Large Language Model perform as well as it's teacher? | The burgeoning complexity of contemporary deep learning models, while
achieving unparalleled accuracy, has inadvertently introduced deployment
challenges in resource-constrained environments. Knowledge distillation, a
technique aiming to transfer knowledge from a high-capacity "teacher" model to
a streamlined "student" model, emerges as a promising solution to this dilemma.
This paper provides a comprehensive overview of the knowledge distillation
paradigm, emphasizing its foundational principles such as the utility of soft
labels and the significance of temperature scaling. Through meticulous
examination, we elucidate the critical determinants of successful distillation,
including the architecture of the student model, the caliber of the teacher,
and the delicate balance of hyperparameters. While acknowledging its profound
advantages, we also delve into the complexities and challenges inherent in the
process. Our exploration underscores knowledge distillation's potential as a
pivotal technique in optimizing the trade-off between model performance and
deployment efficiency. | [
"Sia Gholami",
"Marwan Omar"
] | 2023-10-03 20:34:59 | http://arxiv.org/abs/2310.02421v1 | http://arxiv.org/pdf/2310.02421v1 | 2310.02421v1 |
FedL2P: Federated Learning to Personalize | Federated learning (FL) research has made progress in developing algorithms
for distributed learning of global models, as well as algorithms for local
personalization of those common models to the specifics of each client's local
data distribution. However, different FL problems may require different
personalization strategies, and it may not even be possible to define an
effective one-size-fits-all personalization strategy for all clients: depending
on how similar each client's optimal predictor is to that of the global model,
different personalization strategies may be preferred. In this paper, we
consider the federated meta-learning problem of learning personalization
strategies. Specifically, we consider meta-nets that induce the batch-norm and
learning rate parameters for each client given local data statistics. By
learning these meta-nets through FL, we allow the whole FL network to
collaborate in learning a customized personalization strategy for each client.
Empirical results show that this framework improves on a range of standard
hand-crafted personalization baselines in both label and feature shift
situations. | [
"Royson Lee",
"Minyoung Kim",
"Da Li",
"Xinchi Qiu",
"Timothy Hospedales",
"Ferenc Huszár",
"Nicholas D. Lane"
] | 2023-10-03 20:34:01 | http://arxiv.org/abs/2310.02420v1 | http://arxiv.org/pdf/2310.02420v1 | 2310.02420v1 |
Bag of Tricks for Fully Test-Time Adaptation | Fully Test-Time Adaptation (TTA), which aims at adapting models to data
drifts, has recently attracted wide interest. Numerous tricks and techniques
have been proposed to ensure robust learning on arbitrary streams of unlabeled
data. However, assessing the true impact of each individual technique and
obtaining a fair comparison still constitutes a significant challenge. To help
consolidate the community's knowledge, we present a categorization of selected
orthogonal TTA techniques, including small batch normalization, stream
rebalancing, reliable sample selection, and network confidence calibration. We
meticulously dissect the effect of each approach on different scenarios of
interest. Through our analysis, we shed light on trade-offs induced by those
techniques between accuracy, the computational power required, and model
complexity. We also uncover the synergy that arises when combining techniques
and are able to establish new state-of-the-art results. | [
"Saypraseuth Mounsaveng",
"Florent Chiaroni",
"Malik Boudiaf",
"Marco Pedersoli",
"Ismail Ben Ayed"
] | 2023-10-03 20:28:09 | http://arxiv.org/abs/2310.02416v1 | http://arxiv.org/pdf/2310.02416v1 | 2310.02416v1 |
Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness | Large Mixture of Experts (MoE) models could achieve state-of-the-art quality
on various language tasks, including machine translation task, thanks to the
efficient model scaling capability with expert parallelism. However, it has
brought a fundamental issue of larger memory consumption and increased memory
bandwidth bottleneck at deployment time. In this paper, we propose Mixture of
Quantized Experts (MoQE) which is a simple weight-only quantization method
applying ultra low-bit down to 2-bit quantizations only to expert weights for
mitigating the increased memory and latency issues of MoE models. We show that
low-bit quantization together with the MoE architecture delivers a reliable
model performance while reducing the memory size significantly even without any
additional training in most cases. In particular, expert layers in MoE models
are much more robust to the quantization than conventional feedforward networks
(FFN) layers. In our comprehensive analysis, we show that MoE models with 2-bit
expert weights can deliver better model performance than the dense model
trained on the same dataset. As a result of low-bit quantization, we show the
model size can be reduced by 79.6% of the original half precision floating
point (fp16) MoE model. Combined with an optimized GPU runtime implementation,
it also achieves 1.24X speed-up on A100 GPUs. | [
"Young Jin Kim",
"Raffy Fahim",
"Hany Hassan Awadalla"
] | 2023-10-03 20:11:23 | http://arxiv.org/abs/2310.02410v1 | http://arxiv.org/pdf/2310.02410v1 | 2310.02410v1 |
Nugget 2D: Dynamic Contextual Compression for Scaling Decoder-only Language Models | Standard Transformer-based language models (LMs) scale poorly to long
contexts. We propose a solution based on dynamic contextual compression, which
extends the Nugget approach of Qin & Van Durme (2023) from BERT-like frameworks
to decoder-only LMs. Our method models history as compressed "nuggets" which
are trained to allow for reconstruction, and it can be initialized with
off-the-shelf models such as LLaMA. We demonstrate through experiments in
language modeling, question answering, and summarization that Nugget2D retains
capabilities in these tasks, while drastically reducing the overhead during
decoding in terms of time and space. For example, in the experiments of
autoencoding, Nugget2D can shrink context at a 20x compression ratio with a
BLEU score of 98% for reconstruction, achieving nearly lossless encoding. | [
"Guanghui Qin",
"Corby Rosset",
"Ethan C. Chau",
"Nikhil Rao",
"Benjamin Van Durme"
] | 2023-10-03 20:07:06 | http://arxiv.org/abs/2310.02409v1 | http://arxiv.org/pdf/2310.02409v1 | 2310.02409v1 |
Automated Bug Generation in the era of Large Language Models | Bugs are essential in software engineering; many research studies in the past
decades have been proposed to detect, localize, and repair bugs in software
systems. Effectiveness evaluation of such techniques requires complex bugs,
i.e., those that are hard to detect through testing and hard to repair through
debugging. From the classic software engineering point of view, a
hard-to-repair bug differs from the correct code in multiple locations, making
it hard to localize and repair. Hard-to-detect bugs, on the other hand,
manifest themselves under specific test inputs and reachability conditions.
These two objectives, i.e., generating hard-to-detect and hard-to-repair bugs,
are mostly aligned; a bug generation technique can change multiple statements
to be covered only under a specific set of inputs. However, these two
objectives are conflicting for learning-based techniques: A bug should have a
similar code representation to the correct code in the training data to
challenge a bug prediction model to distinguish them. The hard-to-repair bug
definition remains the same but with a caveat: the more a bug differs from the
original code (at multiple locations), the more distant their representations
are and easier to be detected. We propose BugFarm, to transform arbitrary code
into multiple complex bugs. BugFarm leverages LLMs to mutate code in multiple
locations (hard-to-repair). To ensure that multiple modifications do not
notably change the code representation, BugFarm analyzes the attention of the
underlying model and instructs LLMs to only change the least attended locations
(hard-to-detect). Our comprehensive evaluation of 320k+ bugs from over 2.5M
mutants generated by BugFarm and two alternative approaches demonstrates our
superiority in generating bugs that are hard to detect by learning-based bug
prediction approaches and hard to repair by SOTA learning-based program repair
technique. | [
"Ali Reza Ibrahimzada",
"Yang Chen",
"Ryan Rong",
"Reyhaneh Jabbarvand"
] | 2023-10-03 20:01:51 | http://arxiv.org/abs/2310.02407v1 | http://arxiv.org/pdf/2310.02407v1 | 2310.02407v1 |
PCGPT: Procedural Content Generation via Transformers | The paper presents the PCGPT framework, an innovative approach to procedural
content generation (PCG) using offline reinforcement learning and transformer
networks. PCGPT utilizes an autoregressive model based on transformers to
generate game levels iteratively, addressing the challenges of traditional PCG
methods such as repetitive, predictable, or inconsistent content. The framework
models trajectories of actions, states, and rewards, leveraging the
transformer's self-attention mechanism to capture temporal dependencies and
causal relationships. The approach is evaluated in the Sokoban puzzle game,
where the model predicts items that are needed with their corresponding
locations. Experimental results on the game Sokoban demonstrate that PCGPT
generates more complex and diverse game content. Interestingly, it achieves
these results in significantly fewer steps compared to existing methods,
showcasing its potential for enhancing game design and online content
generation. Our model represents a new PCG paradigm which outperforms previous
methods. | [
"Sajad Mohaghegh",
"Mohammad Amin Ramezan Dehnavi",
"Golnoosh Abdollahinejad",
"Matin Hashemi"
] | 2023-10-03 19:58:02 | http://arxiv.org/abs/2310.02405v1 | http://arxiv.org/pdf/2310.02405v1 | 2310.02405v1 |
On the Parallel Complexity of Multilevel Monte Carlo in Stochastic Gradient Descent | In the stochastic gradient descent (SGD) for sequential simulations such as
the neural stochastic differential equations, the Multilevel Monte Carlo (MLMC)
method is known to offer better theoretical computational complexity compared
to the naive Monte Carlo approach. However, in practice, MLMC scales poorly on
massively parallel computing platforms such as modern GPUs, because of its
large parallel complexity which is equivalent to that of the naive Monte Carlo
method. To cope with this issue, we propose the delayed MLMC gradient estimator
that drastically reduces the parallel complexity of MLMC by recycling
previously computed gradient components from earlier steps of SGD. The proposed
estimator provably reduces the average parallel complexity per iteration at the
cost of a slightly worse per-iteration convergence rate. In our numerical
experiments, we use an example of deep hedging to demonstrate the superior
parallel complexity of our method compared to the standard MLMC in SGD. | [
"Kei Ishikawa"
] | 2023-10-03 19:53:12 | http://arxiv.org/abs/2310.02402v2 | http://arxiv.org/pdf/2310.02402v2 | 2310.02402v2 |
Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment | While analytics of sleep electroencephalography (EEG) holds certain
advantages over other methods in clinical applications, high variability across
subjects poses a significant challenge when it comes to deploying machine
learning models for classification tasks in the real world. In such instances,
machine learning models that exhibit exceptional performance on a specific
dataset may not necessarily demonstrate similar proficiency when applied to a
distinct dataset for the same task. The scarcity of high-quality biomedical
data further compounds this challenge, making it difficult to evaluate the
model's generality comprehensively. In this paper, we introduce Transfer
Euclidean Alignment - a transfer learning technique to tackle the problem of
the dearth of human biomedical data for training deep learning models. We
tested the robustness of this transfer learning technique on various rule-based
classical machine learning models as well as the EEGNet-based deep learning
model by evaluating on different datasets, including human and mouse data in a
binary classification task of detecting individuals with versus without
traumatic brain injury (TBI). By demonstrating notable improvements with an
average increase of 14.42% for intraspecies datasets and 5.53% for interspecies
datasets, our findings underscore the importance of the use of transfer
learning to improve the performance of machine learning and deep learning
models when using diverse datasets for training. | [
"Manoj Vishwanath",
"Steven Cao",
"Nikil Dutt",
"Amir M. Rahmani",
"Miranda M. Lim",
"Hung Cao"
] | 2023-10-03 19:48:02 | http://arxiv.org/abs/2310.02398v1 | http://arxiv.org/pdf/2310.02398v1 | 2310.02398v1 |
Implicit regularization of multi-task learning and finetuning in overparameterized neural networks | It is common in deep learning to train networks on auxiliary tasks with the
expectation that the learning will transfer, at least partially, to another
task of interest. In this work, we investigate the inductive biases that result
from learning auxiliary tasks, either simultaneously (multi-task learning, MTL)
or sequentially (pretraining and subsequent finetuning, PT+FT). In the
simplified setting of two-layer diagonal linear networks trained with gradient
descent, we identify implicit regularization penalties associated with MTL and
PT+FT, both of which incentivize feature sharing between tasks and sparsity in
learned task-specific features. Notably, our results imply that during
finetuning, networks operate in a hybrid of the kernel (or "lazy") regime and
the feature learning ("rich") regime identified in prior work. Moreover, PT+FT
can exhibit a novel "nested feature learning" behavior not captured by either
regime, which biases it to extract a sparse subset of the features learned
during pretraining. In ReLU networks, we reproduce all of these qualitative
behaviors. We also observe that PT+FT (but not MTL) is biased to learn features
that are correlated with (but distinct from) those needed for the auxiliary
task, while MTL is biased toward using identical features for both tasks. As a
result, we find that in realistic settings, MTL generalizes better when
comparatively little data is available for the task of interest, while PT+FT
outperforms it with more data available. We show that our findings hold
qualitatively for a deep architecture trained on image classification tasks.
Our characterization of the nested feature learning regime also motivates a
modification to PT+FT that we find empirically improves performance. Overall,
our results shed light on the impact of auxiliary task learning and suggest
ways to leverage it more effectively. | [
"Jack W. Lindsey",
"Samuel Lippl"
] | 2023-10-03 19:39:30 | http://arxiv.org/abs/2310.02396v1 | http://arxiv.org/pdf/2310.02396v1 | 2310.02396v1 |
SE(3)-Stochastic Flow Matching for Protein Backbone Generation | The computational design of novel protein structures has the potential to
impact numerous scientific disciplines greatly. Toward this goal, we introduce
$\text{FoldFlow}$ a series of novel generative models of increasing modeling
power based on the flow-matching paradigm over $3\text{D}$ rigid motions --
i.e. the group $\text{SE(3)}$ -- enabling accurate modeling of protein
backbones. We first introduce $\text{FoldFlow-Base}$, a simulation-free
approach to learning deterministic continuous-time dynamics and matching
invariant target distributions on $\text{SE(3)}$. We next accelerate training
by incorporating Riemannian optimal transport to create $\text{FoldFlow-OT}$,
leading to the construction of both more simple and stable flows. Finally, we
design $\text{FoldFlow-SFM}$ coupling both Riemannian OT and simulation-free
training to learn stochastic continuous-time dynamics over $\text{SE(3)}$. Our
family of $\text{FoldFlow}$ generative models offer several key advantages over
previous approaches to the generative modeling of proteins: they are more
stable and faster to train than diffusion-based approaches, and our models
enjoy the ability to map any invariant source distribution to any invariant
target distribution over $\text{SE(3)}$. Empirically, we validate our FoldFlow
models on protein backbone generation of up to $300$ amino acids leading to
high-quality designable, diverse, and novel samples. | [
"Avishek Joey Bose",
"Tara Akhound-Sadegh",
"Kilian Fatras",
"Guillaume Huguet",
"Jarrid Rector-Brooks",
"Cheng-Hao Liu",
"Andrei Cristian Nica",
"Maksym Korablyov",
"Michael Bronstein",
"Alexander Tong"
] | 2023-10-03 19:24:24 | http://arxiv.org/abs/2310.02391v2 | http://arxiv.org/pdf/2310.02391v2 | 2310.02391v2 |
Secure and Effective Data Appraisal for Machine Learning | Essential for an unfettered data market is the ability to discreetly select
and evaluate training data before finalizing a transaction between the data
owner and model owner. To safeguard the privacy of both data and model, this
process involves scrutinizing the target model through Multi-Party Computation
(MPC). While prior research has posited that the MPC-based evaluation of
Transformer models is excessively resource-intensive, this paper introduces an
innovative approach that renders data selection practical. The contributions of
this study encompass three pivotal elements: (1) a groundbreaking pipeline for
confidential data selection using MPC, (2) replicating intricate
high-dimensional operations with simplified low-dimensional MLPs trained on a
limited subset of pertinent data, and (3) implementing MPC in a concurrent,
multi-phase manner. The proposed method is assessed across an array of
Transformer models and NLP/CV benchmarks. In comparison to the direct MPC-based
evaluation of the target model, our approach substantially reduces the time
required, from thousands of hours to mere tens of hours, with only a nominal
0.20% dip in accuracy when training with the selected data. | [
"Xu Ouyang",
"Changhong Yang",
"Felix Xiaozhu Lin",
"Yangfeng Ji"
] | 2023-10-03 18:52:57 | http://arxiv.org/abs/2310.02373v2 | http://arxiv.org/pdf/2310.02373v2 | 2310.02373v2 |
Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation | Software testing is a crucial aspect of software development, and the
creation of high-quality tests that adhere to best practices is essential for
effective maintenance. Recently, Large Language Models (LLMs) have gained
popularity for code generation, including the automated creation of test cases.
However, these LLMs are often trained on vast amounts of publicly available
code, which may include test cases that do not adhere to best practices and may
even contain test smells (anti-patterns). To address this issue, we propose a
novel technique called Reinforcement Learning from Static Quality Metrics
(RLSQM). To begin, we analyze the anti-patterns generated by the LLM and show
that LLMs can generate undesirable test smells. Thus, we train specific reward
models for each static quality metric, then utilize Proximal Policy
Optimization (PPO) to train models for optimizing a single quality metric at a
time. Furthermore, we amalgamate these rewards into a unified reward model
aimed at capturing different best practices and quality aspects of tests. By
comparing RL-trained models with those trained using supervised learning, we
provide insights into how reliably utilize RL to improve test generation
quality and into the effects of various training strategies. Our experimental
results demonstrate that the RL-optimized model consistently generated
high-quality test cases compared to the base LLM, improving the model by up to
21%, and successfully generates nearly 100% syntactically correct code. RLSQM
also outperformed GPT-4 on four out of seven metrics. This represents a
significant step towards enhancing the overall efficiency and reliability of
software testing through Reinforcement Learning and static quality metrics. Our
data are available at this link: https://figshare.com/s/ded476c8d4c221222849. | [
"Benjamin Steenhoek",
"Michele Tufano",
"Neel Sundaresan",
"Alexey Svyatkovskiy"
] | 2023-10-03 18:48:31 | http://arxiv.org/abs/2310.02368v1 | http://arxiv.org/pdf/2310.02368v1 | 2310.02368v1 |
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback | Interactive search can provide a better experience by incorporating
interaction feedback from the users. This can significantly improve search
accuracy as it helps avoid irrelevant information and captures the users'
search intents. Existing state-of-the-art (SOTA) systems use reinforcement
learning (RL) models to incorporate the interactions but focus on item-level
feedback, ignoring the fine-grained information found in sentence-level
feedback. Yet such feedback requires extensive RL action space exploration and
large amounts of annotated data. This work addresses these challenges by
proposing a new deep Q-learning (DQ) approach, DQrank. DQrank adapts BERT-based
models, the SOTA in natural language processing, to select crucial sentences
based on users' engagement and rank the items to obtain more satisfactory
responses. We also propose two mechanisms to better explore optimal actions.
DQrank further utilizes the experience replay mechanism in DQ to store the
feedback sentences to obtain a better initial ranking performance. We validate
the effectiveness of DQrank on three search datasets. The results show that
DQRank performs at least 12% better than the previous SOTA RL approaches. We
also conduct detailed ablation studies. The ablation results demonstrate that
each model component can efficiently extract and accumulate long-term
engagement effects from the users' sentence-level feedback. This structure
offers new technologies with promised performance to construct a search system
with sentence-level interaction. | [
"Jianghong Zhou",
"Joyce C. Ho",
"Chen Lin",
"Eugene Agichtein"
] | 2023-10-03 18:45:21 | http://arxiv.org/abs/2310.03043v1 | http://arxiv.org/pdf/2310.03043v1 | 2310.03043v1 |
Stochastic force inference via density estimation | Inferring dynamical models from low-resolution temporal data continues to be
a significant challenge in biophysics, especially within transcriptomics, where
separating molecular programs from noise remains an important open problem. We
explore a common scenario in which we have access to an adequate amount of
cross-sectional samples at a few time-points, and assume that our samples are
generated from a latent diffusion process. We propose an approach that relies
on the probability flow associated with an underlying diffusion process to
infer an autonomous, nonlinear force field interpolating between the
distributions. Given a prior on the noise model, we employ score-matching to
differentiate the force field from the intrinsic noise. Using relevant
biophysical examples, we demonstrate that our approach can extract
non-conservative forces from non-stationary data, that it learns equilibrium
dynamics when applied to steady-state data, and that it can do so with both
additive and multiplicative noise models. | [
"Victor Chardès",
"Suryanarayana Maddu",
"Michael J. Shelley"
] | 2023-10-03 18:42:59 | http://arxiv.org/abs/2310.02366v1 | http://arxiv.org/pdf/2310.02366v1 | 2310.02366v1 |
On the definition of toxicity in NLP | The fundamental problem in toxicity detection task lies in the fact that the
toxicity is ill-defined. This causes us to rely on subjective and vague data in
models' training, which results in non-robust and non-accurate results: garbage
in - garbage out.
This work suggests a new, stress-level-based definition of toxicity designed
to be objective and context-aware. On par with it, we also describe possible
ways of applying this new definition to dataset creation and model training. | [
"Sergey Berezin",
"Reza Farahbakhsh",
"Noel Crespi"
] | 2023-10-03 18:32:34 | http://arxiv.org/abs/2310.02357v3 | http://arxiv.org/pdf/2310.02357v3 | 2310.02357v3 |
Investigating Speed Deviation Patterns During Glucose Episodes: A Quantile Regression Approach | Given the growing prevalence of diabetes, there has been significant interest
in determining how diabetes affects instrumental daily functions, like driving.
Complication of glucose control in diabetes includes hypoglycemic and
hyperglycemic episodes, which may impair cognitive and psychomotor functions
needed for safe driving. The goal of this paper was to determine patterns of
diabetes speed behavior during acute glucose to drivers with diabetes who were
euglycemic or control drivers without diabetes in a naturalistic driving
environment. By employing distribution-based analytic methods which capture
distribution patterns, our study advances prior literature that has focused on
conventional approach of average speed to explore speed deviation patterns. | [
"Aparna Joshi",
"Jennifer Merickel",
"Cyrus V. Desouza",
"Matthew Rizzo",
"Pujitha Gunaratne",
"Anuj Sharma"
] | 2023-10-03 18:27:34 | http://arxiv.org/abs/2310.02351v1 | http://arxiv.org/pdf/2310.02351v1 | 2310.02351v1 |
Approximately Equivariant Quantum Neural Network for $p4m$ Group Symmetries in Images | Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms
which can be efficiently simulated with a low depth on near-term quantum
hardware in the presence of noises. However, their performance highly relies on
choosing the most suitable architecture of Variational Quantum Algorithms
(VQAs), and the problem-agnostic models often suffer issues regarding
trainability and generalization power. As a solution, the most recent works
explore Geometric Quantum Machine Learning (GQML) using QNNs equivariant with
respect to the underlying symmetry of the dataset. GQML adds an inductive bias
to the model by incorporating the prior knowledge on the given dataset and
leads to enhancing the optimization performance while constraining the search
space. This work proposes equivariant Quantum Convolutional Neural Networks
(EquivQCNNs) for image classification under planar $p4m$ symmetry, including
reflectional and $90^\circ$ rotational symmetry. We present the results tested
in different use cases, such as phase detection of the 2D Ising model and
classification of the extended MNIST dataset, and compare them with those
obtained with the non-equivariant model, proving that the equivariance fosters
better generalization of the model. | [
"Su Yeon Chang",
"Michele Grossi",
"Bertrand Le Saux",
"Sofia Vallecorsa"
] | 2023-10-03 18:01:02 | http://arxiv.org/abs/2310.02323v1 | http://arxiv.org/pdf/2310.02323v1 | 2310.02323v1 |
DREAM: Visual Decoding from Reversing Human Visual System | In this work we present DREAM, an fMRI-to-image method for reconstructing
viewed images from brain activities, grounded on fundamental knowledge of the
human visual system. We craft reverse pathways that emulate the hierarchical
and parallel nature of how humans perceive the visual world. These tailored
pathways are specialized to decipher semantics, color, and depth cues from fMRI
data, mirroring the forward pathways from visual stimuli to fMRI recordings. To
do so, two components mimic the inverse processes within the human visual
system: the Reverse Visual Association Cortex (R-VAC) which reverses pathways
of this brain region, extracting semantics from fMRI data; the Reverse Parallel
PKM (R-PKM) component simultaneously predicting color and depth from fMRI
signals. The experiments indicate that our method outperforms the current
state-of-the-art models in terms of the consistency of appearance, structure,
and semantics. Code will be made publicly available to facilitate further
research in this field. | [
"Weihao Xia",
"Raoul de Charette",
"Cengiz Öztireli",
"Jing-Hao Xue"
] | 2023-10-03 17:59:58 | http://arxiv.org/abs/2310.02265v1 | http://arxiv.org/pdf/2310.02265v1 | 2310.02265v1 |
Contrastive Post-training Large Language Models on Data Curriculum | Alignment serves as an important step to steer large language models (LLMs)
towards human preferences. In this paper, we explore contrastive post-training
techniques for alignment by automatically constructing preference pairs from
multiple models of varying strengths (e.g., InstructGPT, ChatGPT and GPT-4). We
carefully compare the contrastive techniques of SLiC and DPO to SFT baselines
and find that DPO provides a step-function improvement even after continueing
SFT saturates. We also explore a data curriculum learning scheme for
contrastive post-training, which starts by learning from "easier" pairs and
transitioning to "harder" ones, which further improves alignment. Finally, we
scale up our experiments to train with more data and larger models like Orca.
Remarkably, contrastive post-training further improves the performance of Orca,
already a state-of-the-art instruction learning model tuned with GPT-4 outputs,
to exceed that of ChatGPT. | [
"Canwen Xu",
"Corby Rosset",
"Luciano Del Corro",
"Shweti Mahajan",
"Julian McAuley",
"Jennifer Neville",
"Ahmed Hassan Awadallah",
"Nikhil Rao"
] | 2023-10-03 17:59:46 | http://arxiv.org/abs/2310.02263v1 | http://arxiv.org/pdf/2310.02263v1 | 2310.02263v1 |
Generalizable Long-Horizon Manipulations with Large Language Models | This work introduces a framework harnessing the capabilities of Large
Language Models (LLMs) to generate primitive task conditions for generalizable
long-horizon manipulations with novel objects and unseen tasks. These task
conditions serve as guides for the generation and adjustment of Dynamic
Movement Primitives (DMP) trajectories for long-horizon task execution. We
further create a challenging robotic manipulation task suite based on Pybullet
for long-horizon task evaluation. Extensive experiments in both simulated and
real-world environments demonstrate the effectiveness of our framework on both
familiar tasks involving new objects and novel but related tasks, highlighting
the potential of LLMs in enhancing robotic system versatility and adaptability.
Project website: https://object814.github.io/Task-Condition-With-LLM/ | [
"Haoyu Zhou",
"Mingyu Ding",
"Weikun Peng",
"Masayoshi Tomizuka",
"Lin Shao",
"Chuang Gan"
] | 2023-10-03 17:59:46 | http://arxiv.org/abs/2310.02264v1 | http://arxiv.org/pdf/2310.02264v1 | 2310.02264v1 |
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation | Several recent advances in AI systems (e.g., Tree-of-Thoughts and
Program-Aided Language Models) solve problems by providing a "scaffolding"
program that structures multiple calls to language models to generate better
outputs. A scaffolding program is written in a programming language such as
Python. In this work, we use a language-model-infused scaffolding program to
improve itself. We start with a seed "improver" that improves an input program
according to a given utility function by querying a language model several
times and returning the best solution. We then run this seed improver to
improve itself. Across a small set of downstream tasks, the resulting improved
improver generates programs with significantly better performance than its seed
improver. Afterward, we analyze the variety of self-improvement strategies
proposed by the language model, including beam search, genetic algorithms, and
simulated annealing. Since the language models themselves are not altered, this
is not full recursive self-improvement. Nonetheless, it demonstrates that a
modern language model, GPT-4 in our proof-of-concept experiments, is capable of
writing code that can call itself to improve itself. We critically consider
concerns around the development of self-improving technologies and evaluate the
frequency with which the generated code bypasses a sandbox. | [
"Eric Zelikman",
"Eliana Lorch",
"Lester Mackey",
"Adam Tauman Kalai"
] | 2023-10-03 17:59:32 | http://arxiv.org/abs/2310.02304v1 | http://arxiv.org/pdf/2310.02304v1 | 2310.02304v1 |
A Neural Scaling Law from Lottery Ticket Ensembling | Neural scaling laws (NSL) refer to the phenomenon where model performance
improves with scale. Sharma & Kaplan analyzed NSL using approximation theory
and predict that MSE losses decay as $N^{-\alpha}$, $\alpha=4/d$, where $N$ is
the number of model parameters, and $d$ is the intrinsic input dimension.
Although their theory works well for some cases (e.g., ReLU networks), we
surprisingly find that a simple 1D problem $y=x^2$ manifests a different
scaling law ($\alpha=1$) from their predictions ($\alpha=4$). We opened the
neural networks and found that the new scaling law originates from lottery
ticket ensembling: a wider network on average has more "lottery tickets", which
are ensembled to reduce the variance of outputs. We support the ensembling
mechanism by mechanistically interpreting single neural networks, as well as
studying them statistically. We attribute the $N^{-1}$ scaling law to the
"central limit theorem" of lottery tickets. Finally, we discuss its potential
implications for large language models and statistical physics-type theories of
learning. | [
"Ziming Liu",
"Max Tegmark"
] | 2023-10-03 17:58:33 | http://arxiv.org/abs/2310.02258v1 | http://arxiv.org/pdf/2310.02258v1 | 2310.02258v1 |
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts | Although Large Language Models (LLMs) and Large Multimodal Models (LMMs)
exhibit impressive skills in various domains, their ability for mathematical
reasoning within visual contexts has not been formally examined. Equipping LLMs
and LMMs with this capability is vital for general-purpose AI assistants and
showcases promising potential in education, data analysis, and scientific
discovery. To bridge this gap, we present MathVista, a benchmark designed to
amalgamate challenges from diverse mathematical and visual tasks. We first
taxonomize the key task types, reasoning skills, and visual contexts from the
literature to guide our selection from 28 existing math-focused and visual
question answering datasets. Then, we construct three new datasets, IQTest,
FunctionQA, and PaperQA, to accommodate for missing types of visual contexts.
The problems featured often require deep visual understanding beyond OCR or
image captioning, and compositional reasoning with rich domain-specific tools,
thus posing a notable challenge to existing models. We conduct a comprehensive
evaluation of 11 prominent open-source and proprietary foundation models (LLMs,
LLMs augmented with tools, and LMMs), and early experiments with GPT-4V. The
best-performing model, Multimodal Bard, achieves only 58% of human performance
(34.8% vs 60.3%), indicating ample room for further improvement. Given this
significant gap, MathVista fuels future research in the development of
general-purpose AI agents capable of tackling mathematically intensive and
visually rich real-world tasks. Preliminary tests show that MathVista also
presents challenges to GPT-4V, underscoring the benchmark's importance. The
project is available at https://mathvista.github.io/. | [
"Pan Lu",
"Hritik Bansal",
"Tony Xia",
"Jiacheng Liu",
"Chunyuan Li",
"Hannaneh Hajishirzi",
"Hao Cheng",
"Kai-Wei Chang",
"Michel Galley",
"Jianfeng Gao"
] | 2023-10-03 17:57:24 | http://arxiv.org/abs/2310.02255v1 | http://arxiv.org/pdf/2310.02255v1 | 2310.02255v1 |
Learning unitaries with quantum statistical queries | We propose several algorithms for learning unitary operators from quantum
statistical queries (QSQs) with respect to their Choi-Jamiolkowski state.
Quantum statistical queries capture the capabilities of a learner with limited
quantum resources, which receives as input only noisy estimates of expected
values of measurements. Our methods hinge on a novel technique for estimating
the Fourier mass of a unitary on a subset of Pauli strings with a single
quantum statistical query, generalizing a previous result for uniform quantum
examples. Exploiting this insight, we show that the quantum Goldreich-Levin
algorithm can be implemented with quantum statistical queries, whereas the
prior version of the algorithm involves oracle access to the unitary and its
inverse. Moreover, we prove that $\mathcal{O}(\log n)$-juntas and quantum
Boolean functions with constant total influence are efficiently learnable in
our model, and constant-depth circuits are learnable sample-efficiently with
quantum statistical queries. On the other hand, all previous algorithms for
these tasks require direct access to the Choi-Jamiolkowski state or oracle
access to the unitary. In addition, our upper bounds imply that the actions of
those classes of unitaries on locally scrambled ensembles can be efficiently
learned. We also demonstrate that, despite these positive results, quantum
statistical queries lead to an exponentially larger sample complexity for
certain tasks, compared to separable measurements to the Choi-Jamiolkowski
state. In particular, we show an exponential lower bound for learning a class
of phase-oracle unitaries and a double exponential lower bound for testing the
unitarity of channels, adapting to our setting previous arguments for quantum
states. Finally, we propose a new definition of average-case surrogate models,
showing a potential application of our results to hybrid quantum machine
learning. | [
"Armando Angrisani"
] | 2023-10-03 17:56:07 | http://arxiv.org/abs/2310.02254v1 | http://arxiv.org/pdf/2310.02254v1 | 2310.02254v1 |
Why do autoencoders work? | Deep neural network autoencoders are routinely used computationally for model
reduction. They allow recognizing the intrinsic dimension of data that lie in a
$k$-dimensional subset $K$ of an input Euclidean space $\mathbb{R}^n$. The
underlying idea is to obtain both an encoding layer that maps $\mathbb{R}^n$
into $\mathbb{R}^k$ (called the bottleneck layer or the space of latent
variables) and a decoding layer that maps $\mathbb{R}^k$ back into
$\mathbb{R}^n$, in such a way that the input data from the set $K$ is recovered
when composing the two maps. This is achieved by adjusting parameters (weights)
in the network to minimize the discrepancy between the input and the
reconstructed output. Since neural networks (with continuous activation
functions) compute continuous maps, the existence of a network that achieves
perfect reconstruction would imply that $K$ is homeomorphic to a
$k$-dimensional subset of $\mathbb{R}^k$, so clearly there are topological
obstructions to finding such a network. On the other hand, in practice the
technique is found to "work" well, which leads one to ask if there is a way to
explain this effectiveness. We show that, up to small errors, indeed the method
is guaranteed to work. This is done by appealing to certain facts from
differential geometry. A computational example is also included to illustrate
the ideas. | [
"Matthew D. Kvalheim",
"Eduardo D. Sontag"
] | 2023-10-03 17:53:43 | http://arxiv.org/abs/2310.02250v2 | http://arxiv.org/pdf/2310.02250v2 | 2310.02250v2 |
Harnessing Pre-Trained Sentence Transformers for Offensive Language Detection in Indian Languages | In our increasingly interconnected digital world, social media platforms have
emerged as powerful channels for the dissemination of hate speech and offensive
content. This work delves into the domain of hate speech detection, placing
specific emphasis on three low-resource Indian languages: Bengali, Assamese,
and Gujarati. The challenge is framed as a text classification task, aimed at
discerning whether a tweet contains offensive or non-offensive content.
Leveraging the HASOC 2023 datasets, we fine-tuned pre-trained BERT and SBERT
models to evaluate their effectiveness in identifying hate speech. Our findings
underscore the superiority of monolingual sentence-BERT models, particularly in
the Bengali language, where we achieved the highest ranking. However, the
performance in Assamese and Gujarati languages signifies ongoing opportunities
for enhancement. Our goal is to foster inclusive online spaces by countering
hate speech proliferation. | [
"Ananya Joshi",
"Raviraj Joshi"
] | 2023-10-03 17:53:09 | http://arxiv.org/abs/2310.02249v1 | http://arxiv.org/pdf/2310.02249v1 | 2310.02249v1 |
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances | Solving a linear system $Ax=b$ is a fundamental scientific computing
primitive for which numerous solvers and preconditioners have been developed.
These come with parameters whose optimal values depend on the system being
solved and are often impossible or too expensive to identify; thus in practice
sub-optimal heuristics are used. We consider the common setting in which many
related linear systems need to be solved, e.g. during a single numerical
simulation. In this scenario, can we sequentially choose parameters that attain
a near-optimal overall number of iterations, without extra matrix computations?
We answer in the affirmative for Successive Over-Relaxation (SOR), a standard
solver whose parameter $\omega$ has a strong impact on its runtime. For this
method, we prove that a bandit online learning algorithm -- using only the
number of iterations as feedback -- can select parameters for a sequence of
instances such that the overall cost approaches that of the best fixed $\omega$
as the sequence length increases. Furthermore, when given additional structural
information, we show that a contextual bandit method asymptotically achieves
the performance of the instance-optimal policy, which selects the best $\omega$
for each instance. Our work provides the first learning-theoretic treatment of
high-precision linear system solvers and the first end-to-end guarantees for
data-driven scientific computing, demonstrating theoretically the potential to
speed up numerical methods using well-understood learning algorithms. | [
"Mikhail Khodak",
"Edmond Chow",
"Maria-Florina Balcan",
"Ameet Talwalkar"
] | 2023-10-03 17:51:42 | http://arxiv.org/abs/2310.02246v1 | http://arxiv.org/pdf/2310.02246v1 | 2310.02246v1 |
Learning quantum Hamiltonians at any temperature in polynomial time | We study the problem of learning a local quantum Hamiltonian $H$ given copies
of its Gibbs state $\rho = e^{-\beta H}/\textrm{tr}(e^{-\beta H})$ at a known
inverse temperature $\beta>0$. Anshu, Arunachalam, Kuwahara, and Soleimanifar
(arXiv:2004.07266) gave an algorithm to learn a Hamiltonian on $n$ qubits to
precision $\epsilon$ with only polynomially many copies of the Gibbs state, but
which takes exponential time. Obtaining a computationally efficient algorithm
has been a major open problem [Alhambra'22 (arXiv:2204.08349)], [Anshu,
Arunachalam'22 (arXiv:2204.08349)], with prior work only resolving this in the
limited cases of high temperature [Haah, Kothari, Tang'21 (arXiv:2108.04842)]
or commuting terms [Anshu, Arunachalam, Kuwahara, Soleimanifar'21]. We fully
resolve this problem, giving a polynomial time algorithm for learning $H$ to
precision $\epsilon$ from polynomially many copies of the Gibbs state at any
constant $\beta > 0$.
Our main technical contribution is a new flat polynomial approximation to the
exponential function, and a translation between multi-variate scalar
polynomials and nested commutators. This enables us to formulate Hamiltonian
learning as a polynomial system. We then show that solving a low-degree
sum-of-squares relaxation of this polynomial system suffices to accurately
learn the Hamiltonian. | [
"Ainesh Bakshi",
"Allen Liu",
"Ankur Moitra",
"Ewin Tang"
] | 2023-10-03 17:50:26 | http://arxiv.org/abs/2310.02243v1 | http://arxiv.org/pdf/2310.02243v1 | 2310.02243v1 |
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness | Deep neural network ensembles hold the potential of improving generalization
performance for complex learning tasks. This paper presents formal analysis and
empirical evaluation to show that heterogeneous deep ensembles with high
ensemble diversity can effectively leverage model learning heterogeneity to
boost ensemble robustness. We first show that heterogeneous DNN models trained
for solving the same learning problem, e.g., object detection, can
significantly strengthen the mean average precision (mAP) through our weighted
bounding box ensemble consensus method. Second, we further compose ensembles of
heterogeneous models for solving different learning problems, e.g., object
detection and semantic segmentation, by introducing the connected component
labeling (CCL) based alignment. We show that this two-tier heterogeneity driven
ensemble construction method can compose an ensemble team that promotes high
ensemble diversity and low negative correlation among member models of the
ensemble, strengthening ensemble robustness against both negative examples and
adversarial attacks. Third, we provide a formal analysis of the ensemble
robustness in terms of negative correlation. Extensive experiments validate the
enhanced robustness of heterogeneous ensembles in both benign and adversarial
settings. The source codes are available on GitHub at
https://github.com/git-disl/HeteRobust. | [
"Yanzhao Wu",
"Ka-Ho Chow",
"Wenqi Wei",
"Ling Liu"
] | 2023-10-03 17:47:25 | http://arxiv.org/abs/2310.02237v1 | http://arxiv.org/pdf/2310.02237v1 | 2310.02237v1 |
Automatic Quality Assessment of Wikipedia Articles -- A Systematic Literature Review | Wikipedia is the world's largest online encyclopedia, but maintaining article
quality through collaboration is challenging. Wikipedia designed a quality
scale, but with such a manual assessment process, many articles remain
unassessed. We review existing methods for automatically measuring the quality
of Wikipedia articles, identifying and comparing machine learning algorithms,
article features, quality metrics, and used datasets, examining 149 distinct
studies, and exploring commonalities and gaps in them. The literature is
extensive, and the approaches follow past technological trends. However,
machine learning is still not widely used by Wikipedia, and we hope that our
analysis helps future researchers change that reality. | [
"Pedro Miguel Moás",
"Carla Teixeira Lopes"
] | 2023-10-03 17:45:39 | http://arxiv.org/abs/2310.02235v1 | http://arxiv.org/pdf/2310.02235v1 | 2310.02235v1 |
MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection | Deepfakes are synthetic media generated using deep generative algorithms and
have posed a severe societal and political threat. Apart from facial
manipulation and synthetic voice, recently, a novel kind of deepfakes has
emerged with either audio or visual modalities manipulated. In this regard, a
new generation of multimodal audio-visual deepfake detectors is being
investigated to collectively focus on audio and visual data for multimodal
manipulation detection. Existing multimodal (audio-visual) deepfake detectors
are often based on the fusion of the audio and visual streams from the video.
Existing studies suggest that these multimodal detectors often obtain
equivalent performances with unimodal audio and visual deepfake detectors. We
conjecture that the heterogeneous nature of the audio and visual signals
creates distributional modality gaps and poses a significant challenge to
effective fusion and efficient performance. In this paper, we tackle the
problem at the representation level to aid the fusion of audio and visual
streams for multimodal deepfake detection. Specifically, we propose the joint
use of modality (audio and visual) invariant and specific representations. This
ensures that the common patterns and patterns specific to each modality
representing pristine or fake content are preserved and fused for multimodal
deepfake manipulation detection. Our experimental results on FakeAVCeleb and
KoDF audio-visual deepfake datasets suggest the enhanced accuracy of our
proposed method over SOTA unimodal and multimodal audio-visual deepfake
detectors by $17.8$% and $18.4$%, respectively. Thus, obtaining
state-of-the-art performance. | [
"Vinaya Sree Katamneni",
"Ajita Rattani"
] | 2023-10-03 17:43:24 | http://arxiv.org/abs/2310.02234v2 | http://arxiv.org/pdf/2310.02234v2 | 2310.02234v2 |
Generalized Schrödinger Bridge Matching | Modern distribution matching algorithms for training diffusion or flow models
directly prescribe the time evolution of the marginal distributions between two
boundary distributions. In this work, we consider a generalized distribution
matching setup, where these marginals are only implicitly described as a
solution to some task-specific objective function. The problem setup, known as
the Generalized Schr\"odinger Bridge (GSB), appears prevalently in many
scientific areas both within and without machine learning. We propose
Generalized Schr\"odinger Bridge Matching (GSBM), a new matching algorithm
inspired by recent advances, generalizing them beyond kinetic energy
minimization and to account for task-specific state costs. We show that such a
generalization can be cast as solving conditional stochastic optimal control,
for which efficient variational approximations can be used, and further
debiased with the aid of path integral theory. Compared to prior methods for
solving GSB problems, our GSBM algorithm always preserves a feasible transport
map between the boundary distributions throughout training, thereby enabling
stable convergence and significantly improved scalability. We empirically
validate our claims on an extensive suite of experimental setups, including
crowd navigation, opinion depolarization, LiDAR manifolds, and image domain
transfer. Our work brings new algorithmic opportunities for training diffusion
models enhanced with task-specific optimality structures. | [
"Guan-Horng Liu",
"Yaron Lipman",
"Maximilian Nickel",
"Brian Karrer",
"Evangelos A. Theodorou",
"Ricky T. Q. Chen"
] | 2023-10-03 17:42:11 | http://arxiv.org/abs/2310.02233v1 | http://arxiv.org/pdf/2310.02233v1 | 2310.02233v1 |
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