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Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models | Claim verification plays a crucial role in combating misinformation. While
existing works on claim verification have shown promising results, a crucial
piece of the puzzle that remains unsolved is to understand how to verify claims
without relying on human-annotated data, which is expensive to create at a
large scale. Additionally, it is important for models to provide comprehensive
explanations that can justify their decisions and assist human fact-checkers.
This paper presents First-Order-Logic-Guided Knowledge-Grounded (FOLK)
Reasoning that can verify complex claims and generate explanations without the
need for annotated evidence using Large Language Models (LLMs). FOLK leverages
the in-context learning ability of LLMs to translate the claim into a
First-Order-Logic (FOL) clause consisting of predicates, each corresponding to
a sub-claim that needs to be verified. Then, FOLK performs FOL-Guided reasoning
over a set of knowledge-grounded question-and-answer pairs to make veracity
predictions and generate explanations to justify its decision-making process.
This process makes our model highly explanatory, providing clear explanations
of its reasoning process in human-readable form. Our experiment results
indicate that FOLK outperforms strong baselines on three datasets encompassing
various claim verification challenges. Our code and data are available. | [
"Haoran Wang",
"Kai Shu"
] | 2023-10-08 18:04:05 | http://arxiv.org/abs/2310.05253v2 | http://arxiv.org/pdf/2310.05253v2 | 2310.05253v2 |
Simplifying GNN Performance with Low Rank Kernel Models | We revisit recent spectral GNN approaches to semi-supervised node
classification (SSNC). We posit that many of the current GNN architectures may
be over-engineered. Instead, simpler, traditional methods from nonparametric
estimation, applied in the spectral domain, could replace many deep-learning
inspired GNN designs. These conventional techniques appear to be well suited
for a variety of graph types reaching state-of-the-art performance on many of
the common SSNC benchmarks. Additionally, we show that recent performance
improvements in GNN approaches may be partially attributed to shifts in
evaluation conventions. Lastly, an ablative study is conducted on the various
hyperparameters associated with GNN spectral filtering techniques. Code
available at: https://github.com/lucianoAvinas/lowrank-gnn-kernels | [
"Luciano Vinas",
"Arash A. Amini"
] | 2023-10-08 17:56:30 | http://arxiv.org/abs/2310.05250v1 | http://arxiv.org/pdf/2310.05250v1 | 2310.05250v1 |
In-Context Convergence of Transformers | Transformers have recently revolutionized many domains in modern machine
learning and one salient discovery is their remarkable in-context learning
capability, where models can solve an unseen task by utilizing task-specific
prompts without further parameters fine-tuning. This also inspired recent
theoretical studies aiming to understand the in-context learning mechanism of
transformers, which however focused only on linear transformers. In this work,
we take the first step toward studying the learning dynamics of a one-layer
transformer with softmax attention trained via gradient descent in order to
in-context learn linear function classes. We consider a structured data model,
where each token is randomly sampled from a set of feature vectors in either
balanced or imbalanced fashion. For data with balanced features, we establish
the finite-time convergence guarantee with near-zero prediction error by
navigating our analysis over two phases of the training dynamics of the
attention map. More notably, for data with imbalanced features, we show that
the learning dynamics take a stage-wise convergence process, where the
transformer first converges to a near-zero prediction error for the query
tokens of dominant features, and then converges later to a near-zero prediction
error for the query tokens of under-represented features, respectively via one
and four training phases. Our proof features new techniques for analyzing the
competing strengths of two types of attention weights, the change of which
determines different training phases. | [
"Yu Huang",
"Yuan Cheng",
"Yingbin Liang"
] | 2023-10-08 17:55:33 | http://arxiv.org/abs/2310.05249v1 | http://arxiv.org/pdf/2310.05249v1 | 2310.05249v1 |
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels | The lack of sufficient flexibility is the key bottleneck of kernel-based
learning that relies on manually designed, pre-given, and non-trainable
kernels. To enhance kernel flexibility, this paper introduces the concept of
Locally-Adaptive-Bandwidths (LAB) as trainable parameters to enhance the Radial
Basis Function (RBF) kernel, giving rise to the LAB RBF kernel. The parameters
in LAB RBF kernels are data-dependent, and its number can increase with the
dataset, allowing for better adaptation to diverse data patterns and enhancing
the flexibility of the learned function. This newfound flexibility also brings
challenges, particularly with regards to asymmetry and the need for an
efficient learning algorithm. To address these challenges, this paper for the
first time establishes an asymmetric kernel ridge regression framework and
introduces an iterative kernel learning algorithm. This novel approach not only
reduces the demand for extensive support data but also significantly improves
generalization by training bandwidths on the available training data.
Experimental results on real datasets underscore the remarkable performance of
the proposed algorithm, showcasing its superior capability in handling
large-scale datasets compared to Nystr\"om approximation-based algorithms.
Moreover, it demonstrates a significant improvement in regression accuracy over
existing kernel-based learning methods and even surpasses residual neural
networks. | [
"Fan He",
"Mingzhen He",
"Lei Shi",
"Xiaolin Huang",
"Johan A. K. Suykens"
] | 2023-10-08 17:08:15 | http://arxiv.org/abs/2310.05236v1 | http://arxiv.org/pdf/2310.05236v1 | 2310.05236v1 |
Global Convergence of Policy Gradient Methods in Reinforcement Learning, Games and Control | Policy gradient methods, where one searches for the policy of interest by
maximizing the value functions using first-order information, become
increasingly popular for sequential decision making in reinforcement learning,
games, and control. Guaranteeing the global optimality of policy gradient
methods, however, is highly nontrivial due to nonconcavity of the value
functions. In this exposition, we highlight recent progresses in understanding
and developing policy gradient methods with global convergence guarantees,
putting an emphasis on their finite-time convergence rates with regard to
salient problem parameters. | [
"Shicong Cen",
"Yuejie Chi"
] | 2023-10-08 16:54:25 | http://arxiv.org/abs/2310.05230v1 | http://arxiv.org/pdf/2310.05230v1 | 2310.05230v1 |
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology | Accurate hydrological understanding and water cycle prediction are crucial
for addressing scientific and societal challenges associated with the
management of water resources, particularly under the dynamic influence of
anthropogenic climate change. Existing reviews predominantly concentrate on the
development of machine learning (ML) in this field, yet there is a clear
distinction between hydrology and ML as separate paradigms. Here, we introduce
physics-aware ML as a transformative approach to overcome the perceived barrier
and revolutionize both fields. Specifically, we present a comprehensive review
of the physics-aware ML methods, building a structured community (PaML) of
existing methodologies that integrate prior physical knowledge or physics-based
modeling into ML. We systematically analyze these PaML methodologies with
respect to four aspects: physical data-guided ML, physics-informed ML,
physics-embedded ML, and physics-aware hybrid learning. PaML facilitates
ML-aided hypotheses, accelerating insights from big data and fostering
scientific discoveries. We first conduct a systematic review of hydrology in
PaML, including rainfall-runoff hydrological processes and hydrodynamic
processes, and highlight the most promising and challenging directions for
different objectives and PaML methods. Finally, a new PaML-based hydrology
platform, termed HydroPML, is released as a foundation for hydrological
applications. HydroPML enhances the explainability and causality of ML and lays
the groundwork for the digital water cycle's realization. The HydroPML platform
is publicly available at https://hydropml.github.io/. | [
"Qingsong Xu",
"Yilei Shi",
"Jonathan Bamber",
"Ye Tuo",
"Ralf Ludwig",
"Xiao Xiang Zhu"
] | 2023-10-08 16:48:29 | http://arxiv.org/abs/2310.05227v2 | http://arxiv.org/pdf/2310.05227v2 | 2310.05227v2 |
Generative Spoken Language Model based on continuous word-sized audio tokens | In NLP, text language models based on words or subwords are known to
outperform their character-based counterparts. Yet, in the speech community,
the standard input of spoken LMs are 20ms or 40ms-long discrete units (shorter
than a phoneme). Taking inspiration from word-based LM, we introduce a
Generative Spoken Language Model (GSLM) based on word-size continuous-valued
audio embeddings that can generate diverse and expressive language output. This
is obtained by replacing lookup table for lexical types with a Lexical
Embedding function, the cross entropy loss by a contrastive loss, and
multinomial sampling by k-NN sampling. The resulting model is the first
generative language model based on word-size continuous embeddings. Its
performance is on par with discrete unit GSLMs regarding generation quality as
measured by automatic metrics and subjective human judgements. Moreover, it is
five times more memory efficient thanks to its large 200ms units. In addition,
the embeddings before and after the Lexical Embedder are phonetically and
semantically interpretable. | [
"Robin Algayres",
"Yossi Adi",
"Tu Anh Nguyen",
"Jade Copet",
"Gabriel Synnaeve",
"Benoit Sagot",
"Emmanuel Dupoux"
] | 2023-10-08 16:46:14 | http://arxiv.org/abs/2310.05224v1 | http://arxiv.org/pdf/2310.05224v1 | 2310.05224v1 |
Accelerating Machine Learning Primitives on Commodity Hardware | Sliding Window Sum algorithms have been successfully used for training and
inference of Deep Neural Networks. We have shown before how both pooling and
convolution 1-D primitives could be expressed as sliding sums and evaluated by
the compute kernels with a shared structure. In this paper, we present an
extensive study of the Sliding Window convolution technique as a more efficient
alternative to the commonly used General Matrix Multiplication (GEMM) based
convolution in Deep Neural Networks (DNNs). The Sliding Window technique
addresses the memory bloating problem and demonstrates a significant speedup in
2-D convolution. We explore the performance of this technique on a range of
implementations, including custom kernels for specific filter sizes. Our
results suggest that the Sliding Window computation kernels can outperform
GEMM-based convolution on a CPU and even on dedicated hardware accelerators.
This could promote a wider adoption of AI on low-power and low-memory devices
without the need for specialized hardware. We also discuss the compatibility of
model compression methods and optimized network architectures with the Sliding
Window technique, encouraging further research in these areas. | [
"Roman Snytsar"
] | 2023-10-08 16:26:18 | http://arxiv.org/abs/2310.05218v1 | http://arxiv.org/pdf/2310.05218v1 | 2310.05218v1 |
Quantifying Zero-shot Coordination Capability with Behavior Preferring Partners | Zero-shot coordination (ZSC) is a new challenge focusing on generalizing
learned coordination skills to unseen partners. Existing methods train the ego
agent with partners from pre-trained or evolving populations. The agent's ZSC
capability is typically evaluated with a few evaluation partners, including
human and agent, and reported by mean returns. Current evaluation methods for
ZSC capability still need to improve in constructing diverse evaluation
partners and comprehensively measuring the ZSC capability. We aim to create a
reliable, comprehensive, and efficient evaluation method for ZSC capability. We
formally define the ideal 'diversity-complete' evaluation partners and propose
the best response (BR) diversity, which is the population diversity of the BRs
to the partners, to approximate the ideal evaluation partners. We propose an
evaluation workflow including 'diversity-complete' evaluation partners
construction and a multi-dimensional metric, the Best Response Proximity
(BR-Prox) metric. BR-Prox quantifies the ZSC capability as the performance
similarity to each evaluation partner's approximate best response,
demonstrating generalization capability and improvement potential. We
re-evaluate strong ZSC methods in the Overcooked environment using the proposed
evaluation workflow. Surprisingly, the results in some of the most used layouts
fail to distinguish the performance of different ZSC methods. Moreover, the
evaluated ZSC methods must produce more diverse and high-performing training
partners. Our proposed evaluation workflow calls for a change in how we
efficiently evaluate ZSC methods as a supplement to human evaluation. | [
"Xihuai Wang",
"Shao Zhang",
"Wenhao Zhang",
"Wentao Dong",
"Jingxiao Chen",
"Ying Wen",
"Weinan Zhang"
] | 2023-10-08 15:49:36 | http://arxiv.org/abs/2310.05208v1 | http://arxiv.org/pdf/2310.05208v1 | 2310.05208v1 |
Boosting Facial Action Unit Detection Through Jointly Learning Facial Landmark Detection and Domain Separation and Reconstruction | Recently how to introduce large amounts of unlabeled facial images in the
wild into supervised Facial Action Unit (AU) detection frameworks has become a
challenging problem. In this paper, we propose a new AU detection framework
where multi-task learning is introduced to jointly learn AU domain separation
and reconstruction and facial landmark detection by sharing the parameters of
homostructural facial extraction modules. In addition, we propose a new feature
alignment scheme based on contrastive learning by simple projectors and an
improved contrastive loss, which adds four additional intermediate supervisors
to promote the feature reconstruction process. Experimental results on two
benchmarks demonstrate our superiority against the state-of-the-art methods for
AU detection in the wild. | [
"Ziqiao Shang",
"Li Yu"
] | 2023-10-08 15:49:26 | http://arxiv.org/abs/2310.05207v1 | http://arxiv.org/pdf/2310.05207v1 | 2310.05207v1 |
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models | This paper introduces a distributed, GPU-centric experience replay system,
GEAR, designed to perform scalable reinforcement learning (RL) with large
sequence models (such as transformers). With such models, existing systems such
as Reverb face considerable bottlenecks in memory, computation, and
communication. GEAR, however, optimizes memory efficiency by enabling the
memory resources on GPU servers (including host memory and device memory) to
manage trajectory data. Furthermore, it facilitates decentralized GPU devices
to expedite various trajectory selection strategies, circumventing
computational bottlenecks. GEAR is equipped with GPU kernels capable of
collecting trajectories using zero-copy access to host memory, along with
remote-directed-memory access over InfiniBand, improving communication
efficiency. Cluster experiments have shown that GEAR can achieve performance
levels up to 6x greater than Reverb when training state-of-the-art large RL
models. GEAR is open-sourced at https://github.com/bigrl-team/gear. | [
"Hanjing Wang",
"Man-Kit Sit",
"Congjie He",
"Ying Wen",
"Weinan Zhang",
"Jun Wang",
"Yaodong Yang",
"Luo Mai"
] | 2023-10-08 15:39:43 | http://arxiv.org/abs/2310.05205v1 | http://arxiv.org/pdf/2310.05205v1 | 2310.05205v1 |
Towards Optimizing with Large Language Models | In this work, we conduct an assessment of the optimization capabilities of
LLMs across various tasks and data sizes. Each of these tasks corresponds to
unique optimization domains, and LLMs are required to execute these tasks with
interactive prompting. That is, in each optimization step, the LLM generates
new solutions from the past generated solutions with their values, and then the
new solutions are evaluated and considered in the next optimization step.
Additionally, we introduce three distinct metrics for a comprehensive
assessment of task performance from various perspectives. These metrics offer
the advantage of being applicable for evaluating LLM performance across a broad
spectrum of optimization tasks and are less sensitive to variations in test
samples. By applying these metrics, we observe that LLMs exhibit strong
optimization capabilities when dealing with small-sized samples. However, their
performance is significantly influenced by factors like data size and values,
underscoring the importance of further research in the domain of optimization
tasks for LLMs. | [
"Pei-Fu Guo",
"Ying-Hsuan Chen",
"Yun-Da Tsai",
"Shou-De Lin"
] | 2023-10-08 15:35:00 | http://arxiv.org/abs/2310.05204v1 | http://arxiv.org/pdf/2310.05204v1 | 2310.05204v1 |
A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023 | This paper presents our systems (denoted as T13) for the singing voice
conversion challenge (SVCC) 2023. For both in-domain and cross-domain English
singing voice conversion (SVC) tasks (Task 1 and Task 2), we adopt a
recognition-synthesis approach with self-supervised learning-based
representation. To achieve data-efficient SVC with a limited amount of target
singer/speaker's data (150 to 160 utterances for SVCC 2023), we first train a
diffusion-based any-to-any voice conversion model using publicly available
large-scale 750 hours of speech and singing data. Then, we finetune the model
for each target singer/speaker of Task 1 and Task 2. Large-scale listening
tests conducted by SVCC 2023 show that our T13 system achieves competitive
naturalness and speaker similarity for the harder cross-domain SVC (Task 2),
which implies the generalization ability of our proposed method. Our objective
evaluation results show that using large datasets is particularly beneficial
for cross-domain SVC. | [
"Ryuichi Yamamoto",
"Reo Yoneyama",
"Lester Phillip Violeta",
"Wen-Chin Huang",
"Tomoki Toda"
] | 2023-10-08 15:30:44 | http://arxiv.org/abs/2310.05203v1 | http://arxiv.org/pdf/2310.05203v1 | 2310.05203v1 |
Factuality Challenges in the Era of Large Language Models | The emergence of tools based on Large Language Models (LLMs), such as
OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered
immense public attention. These incredibly useful, natural-sounding tools mark
significant advances in natural language generation, yet they exhibit a
propensity to generate false, erroneous, or misleading content -- commonly
referred to as "hallucinations." Moreover, LLMs can be exploited for malicious
applications, such as generating false but credible-sounding content and
profiles at scale. This poses a significant challenge to society in terms of
the potential deception of users and the increasing dissemination of inaccurate
information. In light of these risks, we explore the kinds of technological
innovations, regulatory reforms, and AI literacy initiatives needed from
fact-checkers, news organizations, and the broader research and policy
communities. By identifying the risks, the imminent threats, and some viable
solutions, we seek to shed light on navigating various aspects of veracity in
the era of generative AI. | [
"Isabelle Augenstein",
"Timothy Baldwin",
"Meeyoung Cha",
"Tanmoy Chakraborty",
"Giovanni Luca Ciampaglia",
"David Corney",
"Renee DiResta",
"Emilio Ferrara",
"Scott Hale",
"Alon Halevy",
"Eduard Hovy",
"Heng Ji",
"Filippo Menczer",
"Ruben Miguez",
"Preslav Nakov",
"Dietram Scheufele",
"Shivam Sharma",
"Giovanni Zagni"
] | 2023-10-08 14:55:02 | http://arxiv.org/abs/2310.05189v2 | http://arxiv.org/pdf/2310.05189v2 | 2310.05189v2 |
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach | Fog computing emerged as a promising paradigm to address the challenges of
processing and managing data generated by the Internet of Things (IoT). Load
balancing (LB) plays a crucial role in Fog computing environments to optimize
the overall system performance. It requires efficient resource allocation to
improve resource utilization, minimize latency, and enhance the quality of
service for end-users. In this work, we improve the performance of
privacy-aware Reinforcement Learning (RL) agents that optimize the execution
delay of IoT applications by minimizing the waiting delay. To maintain privacy,
these agents optimize the waiting delay by minimizing the change in the number
of queued requests in the whole system, i.e., without explicitly observing the
actual number of requests that are queued in each Fog node nor observing the
compute resource capabilities of those nodes. Besides improving the performance
of these agents, we propose in this paper a lifelong learning framework for
these agents, where lightweight inference models are used during deployment to
minimize action delay and only retrained in case of significant environmental
changes. To improve the performance, minimize the training cost, and adapt the
agents to those changes, we explore the application of Transfer Learning (TL).
TL transfers the knowledge acquired from a source domain and applies it to a
target domain, enabling the reuse of learned policies and experiences. TL can
be also used to pre-train the agent in simulation before fine-tuning it in the
real environment; this significantly reduces failure probability compared to
learning from scratch in the real environment. To our knowledge, there are no
existing efforts in the literature that use TL to address lifelong learning for
RL-based Fog LB; this is one of the main obstacles in deploying RL LB solutions
in Fog systems. | [
"Maad Ebrahim",
"Abdelhakim Senhaji Hafid",
"Mohamed Riduan Abid"
] | 2023-10-08 14:49:33 | http://arxiv.org/abs/2310.05187v1 | http://arxiv.org/pdf/2310.05187v1 | 2310.05187v1 |
Unified speech and gesture synthesis using flow matching | As text-to-speech technologies achieve remarkable naturalness in read-aloud
tasks, there is growing interest in multimodal synthesis of verbal and
non-verbal communicative behaviour, such as spontaneous speech and associated
body gestures. This paper presents a novel, unified architecture for jointly
synthesising speech acoustics and skeleton-based 3D gesture motion from text,
trained using optimal-transport conditional flow matching (OT-CFM). The
proposed architecture is simpler than the previous state of the art, has a
smaller memory footprint, and can capture the joint distribution of speech and
gestures, generating both modalities together in one single process. The new
training regime, meanwhile, enables better synthesis quality in much fewer
steps (network evaluations) than before. Uni- and multimodal subjective tests
demonstrate improved speech naturalness, gesture human-likeness, and
cross-modal appropriateness compared to existing benchmarks. | [
"Shivam Mehta",
"Ruibo Tu",
"Simon Alexanderson",
"Jonas Beskow",
"Éva Székely",
"Gustav Eje Henter"
] | 2023-10-08 14:37:28 | http://arxiv.org/abs/2310.05181v1 | http://arxiv.org/pdf/2310.05181v1 | 2310.05181v1 |
Distributional Reinforcement Learning with Online Risk-awareness Adaption | The use of reinforcement learning (RL) in practical applications requires
considering sub-optimal outcomes, which depend on the agent's familiarity with
the uncertain environment. Dynamically adjusting the level of epistemic risk
over the course of learning can tactically achieve reliable optimal policy in
safety-critical environments and tackle the sub-optimality of a static risk
level. In this work, we introduce a novel framework, Distributional RL with
Online Risk Adaption (DRL-ORA), which can quantify the aleatory and epistemic
uncertainties compositely and dynamically select the epistemic risk levels via
solving a total variation minimization problem online. The risk level selection
can be efficiently achieved through grid search using a Follow-The-Leader type
algorithm, and its offline oracle is related to "satisficing measure" (in the
decision analysis community) under a special modification of the loss function.
We show multiple classes of tasks where DRL-ORA outperforms existing methods
that rely on either a fixed risk level or manually predetermined risk level
adaption. Given the simplicity of our modifications, we believe the framework
can be easily incorporated into most RL algorithm variants. | [
"Yupeng Wu",
"Wenjie Huang"
] | 2023-10-08 14:32:23 | http://arxiv.org/abs/2310.05179v1 | http://arxiv.org/pdf/2310.05179v1 | 2310.05179v1 |
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity | Large Language Models (LLMs), renowned for their remarkable performance,
present a challenge due to their colossal model size when it comes to practical
deployment. In response to this challenge, efforts have been directed toward
the application of traditional network pruning techniques to LLMs, uncovering a
massive number of parameters can be pruned in one-shot without hurting
performance. Building upon insights gained from pre-LLM models, prevailing LLM
pruning strategies have consistently adhered to the practice of uniformly
pruning all layers at equivalent sparsity. However, this observation stands in
contrast to the prevailing trends observed in the field of vision models, where
non-uniform layerwise sparsity typically yields substantially improved results.
To elucidate the underlying reasons for this disparity, we conduct a
comprehensive analysis of the distribution of token features within LLMs. In
doing so, we discover a strong correlation with the emergence of outliers,
defined as features exhibiting significantly greater magnitudes compared to
their counterparts in feature dimensions. Inspired by this finding, we
introduce a novel LLM pruning methodology that incorporates a tailored set of
non-uniform layerwise sparsity ratios specifically designed for LLM pruning,
termed as Outlier Weighed Layerwise sparsity (OWL). The sparsity ratio of OWL
is directly proportional to the outlier ratio observed within each layer,
facilitating a more effective alignment between layerwise weight sparsity and
outlier ratios. Our empirical evaluation, conducted across the LLaMA-V1 family
and OPT, spanning various benchmarks, demonstrates the distinct advantages
offered by OWL over previous methods. For instance, our approach exhibits a
remarkable performance gain, surpassing the state-of-the-art Wanda and
SparseGPT by 61.22 and 6.80 perplexity at a high sparsity level of 70%,
respectively. | [
"Lu Yin",
"You Wu",
"Zhenyu Zhang",
"Cheng-Yu Hsieh",
"Yaqing Wang",
"Yiling Jia",
"Mykola Pechenizkiy",
"Yi Liang",
"Zhangyang Wang",
"Shiwei Liu"
] | 2023-10-08 14:22:58 | http://arxiv.org/abs/2310.05175v1 | http://arxiv.org/pdf/2310.05175v1 | 2310.05175v1 |
GSLB: The Graph Structure Learning Benchmark | Graph Structure Learning (GSL) has recently garnered considerable attention
due to its ability to optimize both the parameters of Graph Neural Networks
(GNNs) and the computation graph structure simultaneously. Despite the
proliferation of GSL methods developed in recent years, there is no standard
experimental setting or fair comparison for performance evaluation, which
creates a great obstacle to understanding the progress in this field. To fill
this gap, we systematically analyze the performance of GSL in different
scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB)
curated from 20 diverse graph datasets and 16 distinct GSL algorithms.
Specifically, GSLB systematically investigates the characteristics of GSL in
terms of three dimensions: effectiveness, robustness, and complexity. We
comprehensively evaluate state-of-the-art GSL algorithms in node- and
graph-level tasks, and analyze their performance in robust learning and model
complexity. Further, to facilitate reproducible research, we have developed an
easy-to-use library for training, evaluating, and visualizing different GSL
methods. Empirical results of our extensive experiments demonstrate the ability
of GSL and reveal its potential benefits on various downstream tasks, offering
insights and opportunities for future research. The code of GSLB is available
at: https://github.com/GSL-Benchmark/GSLB. | [
"Zhixun Li",
"Liang Wang",
"Xin Sun",
"Yifan Luo",
"Yanqiao Zhu",
"Dingshuo Chen",
"Yingtao Luo",
"Xiangxin Zhou",
"Qiang Liu",
"Shu Wu",
"Liang Wang",
"Jeffrey Xu Yu"
] | 2023-10-08 14:13:03 | http://arxiv.org/abs/2310.05174v1 | http://arxiv.org/pdf/2310.05174v1 | 2310.05174v1 |
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data | Autonomous driving systems (ADSs) are capable of sensing the environment and
making driving decisions autonomously. These systems are safety-critical, and
testing them is one of the important approaches to ensure their safety.
However, due to the inherent complexity of ADSs and the high dimensionality of
their operating environment, the number of possible test scenarios for ADSs is
infinite. Besides, the operating environment of ADSs is dynamic, continuously
evolving, and full of uncertainties, which requires a testing approach adaptive
to the environment. In addition, existing ADS testing techniques have limited
effectiveness in ensuring the realism of test scenarios, especially the realism
of weather conditions and their changes over time. Recently, reinforcement
learning (RL) has demonstrated great potential in addressing challenging
problems, especially those requiring constant adaptations to dynamic
environments. To this end, we present DeepQTest, a novel ADS testing approach
that uses RL to learn environment configurations with a high chance of
revealing abnormal ADS behaviors. Specifically, DeepQTest employs Deep
Q-Learning and adopts three safety and comfort measures to construct the reward
functions. To ensure the realism of generated scenarios, DeepQTest defines a
set of realistic constraints and introduces real-world weather conditions into
the simulated environment. We employed three comparison baselines, i.e.,
random, greedy, and a state-of-the-art RL-based approach DeepCOllision, for
evaluating DeepQTest on an industrial-scale ADS. Evaluation results show that
DeepQTest demonstrated significantly better effectiveness in terms of
generating scenarios leading to collisions and ensuring scenario realism
compared with the baselines. In addition, among the three reward functions
implemented in DeepQTest, Time-To-Collision is recommended as the best design
according to our study. | [
"Chengjie Lu",
"Tao Yue",
"Man Zhang",
"Shaukat Ali"
] | 2023-10-08 13:59:43 | http://arxiv.org/abs/2310.05170v1 | http://arxiv.org/pdf/2310.05170v1 | 2310.05170v1 |
Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp | Physics Informed Neural Networks (PINNs) have been achieving ever newer feats
of solving complicated PDEs numerically while offering an attractive trade-off
between accuracy and speed of inference. A particularly challenging aspect of
PDEs is that there exist simple PDEs which can evolve into singular solutions
in finite time starting from smooth initial conditions. In recent times some
striking experiments have suggested that PINNs might be good at even detecting
such finite-time blow-ups. In this work, we embark on a program to investigate
this stability of PINNs from a rigorous theoretical viewpoint. Firstly, we
derive generalization bounds for PINNs for Burgers' PDE, in arbitrary
dimensions, under conditions that allow for a finite-time blow-up. Then we
demonstrate via experiments that our bounds are significantly correlated to the
$\ell_2$-distance of the neurally found surrogate from the true blow-up
solution, when computed on sequences of PDEs that are getting increasingly
close to a blow-up. | [
"Dibyakanti Kumar",
"Anirbit Mukherjee"
] | 2023-10-08 13:56:46 | http://arxiv.org/abs/2310.05169v1 | http://arxiv.org/pdf/2310.05169v1 | 2310.05169v1 |
A Corrected Expected Improvement Acquisition Function Under Noisy Observations | Sequential maximization of expected improvement (EI) is one of the most
widely used policies in Bayesian optimization because of its simplicity and
ability to handle noisy observations. In particular, the improvement function
often uses the best posterior mean as the best incumbent in noisy settings.
However, the uncertainty associated with the incumbent solution is often
neglected in many analytic EI-type methods: a closed-form acquisition function
is derived in the noise-free setting, but then applied to the setting with
noisy observations. To address this limitation, we propose a modification of EI
that corrects its closed-form expression by incorporating the covariance
information provided by the Gaussian Process (GP) model. This acquisition
function specializes to the classical noise-free result, and we argue should
replace that formula in Bayesian optimization software packages, tutorials, and
textbooks. This enhanced acquisition provides good generality for noisy and
noiseless settings. We show that our method achieves a sublinear convergence
rate on the cumulative regret bound under heteroscedastic observation noise.
Our empirical results demonstrate that our proposed acquisition function can
outperform EI in the presence of noisy observations on benchmark functions for
black-box optimization, as well as on parameter search for neural network model
compression. | [
"Han Zhou",
"Xingchen Ma",
"Matthew B Blaschko"
] | 2023-10-08 13:50:39 | http://arxiv.org/abs/2310.05166v1 | http://arxiv.org/pdf/2310.05166v1 | 2310.05166v1 |
Recurrent Neural Language Models as Probabilistic Finite-state Automata | Studying language models (LMs) in terms of well-understood formalisms allows
us to precisely characterize their abilities and limitations. Previous work has
investigated the representational capacity of recurrent neural network (RNN)
LMs in terms of their capacity to recognize unweighted formal languages.
However, LMs do not describe unweighted formal languages -- rather, they define
probability distributions over strings. In this work, we study what classes of
such probability distributions RNN LMs can represent, which allows us to make
more direct statements about their capabilities. We show that simple RNNs are
equivalent to a subclass of probabilistic finite-state automata, and can thus
model a strict subset of probability distributions expressible by finite-state
models. Furthermore, we study the space complexity of representing finite-state
LMs with RNNs. We show that, to represent an arbitrary deterministic
finite-state LM with $N$ states over an alphabet $\Sigma$, an RNN requires
$\Omega\left(N |\Sigma|\right)$ neurons. These results present a first step
towards characterizing the classes of distributions RNN LMs can represent and
thus help us understand their capabilities and limitations. | [
"Anej Svete",
"Ryan Cotterell"
] | 2023-10-08 13:36:05 | http://arxiv.org/abs/2310.05161v2 | http://arxiv.org/pdf/2310.05161v2 | 2310.05161v2 |
NeuralFastLAS: Fast Logic-Based Learning from Raw Data | Symbolic rule learners generate interpretable solutions, however they require
the input to be encoded symbolically. Neuro-symbolic approaches overcome this
issue by mapping raw data to latent symbolic concepts using a neural network.
Training the neural and symbolic components jointly is difficult, due to slow
and unstable learning, hence many existing systems rely on hand-engineered
rules to train the network. We introduce NeuralFastLAS, a scalable and fast
end-to-end approach that trains a neural network jointly with a symbolic
learner. For a given task, NeuralFastLAS computes a relevant set of rules,
proved to contain an optimal symbolic solution, trains a neural network using
these rules, and finally finds an optimal symbolic solution to the task while
taking network predictions into account. A key novelty of our approach is
learning a posterior distribution on rules while training the neural network to
improve stability during training. We provide theoretical results for a
sufficient condition on network training to guarantee correctness of the final
solution. Experimental results demonstrate that NeuralFastLAS is able to
achieve state-of-the-art accuracy in arithmetic and logical tasks, with a
training time that is up to two orders of magnitude faster than other jointly
trained neuro-symbolic methods. | [
"Theo Charalambous",
"Yaniv Aspis",
"Alessandra Russo"
] | 2023-10-08 12:33:42 | http://arxiv.org/abs/2310.05145v1 | http://arxiv.org/pdf/2310.05145v1 | 2310.05145v1 |
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning | When personalized federated learning (FL) meets large foundation models, new
challenges arise from various limitations in resources. In addition to typical
limitations such as data, computation, and communication costs, access to the
models is also often limited. This paper endeavors to solve both the challenges
of limited resources and personalization. i.e., distribution shifts between
clients. To do so, we propose a method named ZOOPFL that uses Zeroth-Order
Optimization for Personalized Federated Learning. ZOOPFL avoids direct
interference with the foundation models and instead learns to adapt its inputs
through zeroth-order optimization. In addition, we employ simple yet effective
linear projections to remap its predictions for personalization. To reduce the
computation costs and enhance personalization, we propose input surgery to
incorporate an auto-encoder with low-dimensional and client-specific
embeddings. We provide theoretical support for ZOOPFL to analyze its
convergence. Extensive empirical experiments on computer vision and natural
language processing tasks using popular foundation models demonstrate its
effectiveness for FL on black-box foundation models. | [
"Wang Lu",
"Hao Yu",
"Jindong Wang",
"Damien Teney",
"Haohan Wang",
"Yiqiang Chen",
"Qiang Yang",
"Xing Xie",
"Xiangyang Ji"
] | 2023-10-08 12:26:13 | http://arxiv.org/abs/2310.05143v1 | http://arxiv.org/pdf/2310.05143v1 | 2310.05143v1 |
Transferable Availability Poisoning Attacks | We consider availability data poisoning attacks, where an adversary aims to
degrade the overall test accuracy of a machine learning model by crafting small
perturbations to its training data. Existing poisoning strategies can achieve
the attack goal but assume the victim to employ the same learning method as
what the adversary uses to mount the attack. In this paper, we argue that this
assumption is strong, since the victim may choose any learning algorithm to
train the model as long as it can achieve some targeted performance on clean
data. Empirically, we observe a large decrease in the effectiveness of prior
poisoning attacks if the victim uses a different learning paradigm to train the
model and show marked differences in frequency-level characteristics between
perturbations generated with respect to different learners and attack methods.
To enhance the attack transferability, we propose Transferable Poisoning, which
generates high-frequency poisoning perturbations by alternately leveraging the
gradient information with two specific algorithms selected from supervised and
unsupervised contrastive learning paradigms. Through extensive experiments on
benchmark image datasets, we show that our transferable poisoning attack can
produce poisoned samples with significantly improved transferability, not only
applicable to the two learners used to devise the attack but also for learning
algorithms and even paradigms beyond. | [
"Yiyong Liu",
"Michael Backes",
"Xiao Zhang"
] | 2023-10-08 12:22:50 | http://arxiv.org/abs/2310.05141v1 | http://arxiv.org/pdf/2310.05141v1 | 2310.05141v1 |
Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT | Large Language Models (LLMs) such as GPT-3.5, Bard, and Claude exhibit
applicability across numerous tasks. One domain of interest is their use in
algorithmic hiring, specifically in matching resumes with job categories. Yet,
this introduces issues of bias on protected attributes like gender, race and
maternity status. The seminal work of Bertrand & Mullainathan (2003) set the
gold-standard for identifying hiring bias via field experiments where the
response rate for identical resumes that differ only in protected attributes,
e.g., racially suggestive names such as Emily or Lakisha, is compared. We
replicate this experiment on state-of-art LLMs (GPT-3.5, Bard, Claude and
Llama) to evaluate bias (or lack thereof) on gender, race, maternity status,
pregnancy status, and political affiliation. We evaluate LLMs on two tasks: (1)
matching resumes to job categories; and (2) summarizing resumes with employment
relevant information. Overall, LLMs are robust across race and gender. They
differ in their performance on pregnancy status and political affiliation. We
use contrastive input decoding on open-source LLMs to uncover potential sources
of bias. | [
"Akshaj Kumar Veldanda",
"Fabian Grob",
"Shailja Thakur",
"Hammond Pearce",
"Benjamin Tan",
"Ramesh Karri",
"Siddharth Garg"
] | 2023-10-08 12:08:48 | http://arxiv.org/abs/2310.05135v1 | http://arxiv.org/pdf/2310.05135v1 | 2310.05135v1 |
Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation | We present a novel approach to perform 3D semantic segmentation solely from
2D supervision by leveraging Neural Radiance Fields (NeRFs). By extracting
features along a surface point cloud, we achieve a compact representation of
the scene which is sample-efficient and conducive to 3D reasoning. Learning
this feature space in an unsupervised manner via masked autoencoding enables
few-shot segmentation. Our method is agnostic to the scene parameterization,
working on scenes fit with any type of NeRF. | [
"Dominik Hollidt",
"Clinton Wang",
"Polina Golland",
"Marc Pollefeys"
] | 2023-10-08 11:48:19 | http://arxiv.org/abs/2310.05133v1 | http://arxiv.org/pdf/2310.05133v1 | 2310.05133v1 |
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification | Hierarchical multi-label text classification (HMTC) aims at utilizing a label
hierarchy in multi-label classification. Recent approaches to HMTC deal with
the problem of imposing an over-constrained premise on the output space by
using contrastive learning on generated samples in a semi-supervised manner to
bring text and label embeddings closer. However, the generation of samples
tends to introduce noise as it ignores the correlation between similar samples
in the same batch. One solution to this issue is supervised contrastive
learning, but it remains an underexplored topic in HMTC due to its complex
structured labels. To overcome this challenge, we propose $\textbf{HJCL}$, a
$\textbf{H}$ierarchy-aware $\textbf{J}$oint Supervised $\textbf{C}$ontrastive
$\textbf{L}$earning method that bridges the gap between supervised contrastive
learning and HMTC. Specifically, we employ both instance-wise and label-wise
contrastive learning techniques and carefully construct batches to fulfill the
contrastive learning objective. Extensive experiments on four multi-path HMTC
datasets demonstrate that HJCL achieves promising results and the effectiveness
of Contrastive Learning on HMTC. | [
"Simon Chi Lok U",
"Jie He",
"Víctor Gutiérrez-Basulto",
"Jeff Z. Pan"
] | 2023-10-08 11:36:45 | http://arxiv.org/abs/2310.05128v2 | http://arxiv.org/pdf/2310.05128v2 | 2310.05128v2 |
How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space | A long-standing goal in deep learning has been to characterize the learning
behavior of black-box models in a more interpretable manner. For graph neural
networks (GNNs), considerable advances have been made in formalizing what
functions they can represent, however it remains less clear whether and how
GNNs learn desired functions during the optimization process. To fill this
critical gap, we study the learning dynamics of GNNs in function space via the
analytic framework of overparameterization. In particular, we find that the
seemingly complicated training process of GNNs can be re-cast into a more
familiar label propagation framework, due to the graph inductive bias implicit
in this process. From this vantage point, we provide explanations for why the
learned GNN functions successfully generalize and for their pathological
behavior on heterophilic graphs, which are consistent with observations.
Practically, sparsifying and implementing the learning dynamics lead to a
minimalist semi-supervised learning algorithm with the efficiency of classic
algorithms and the effectiveness of modern GNNs. | [
"Chenxiao Yang",
"Qitian Wu",
"David Wipf",
"Ruoyu Sun",
"Junchi Yan"
] | 2023-10-08 10:19:56 | http://arxiv.org/abs/2310.05105v1 | http://arxiv.org/pdf/2310.05105v1 | 2310.05105v1 |
Zero-Shot Detection of Machine-Generated Codes | This work proposes a training-free approach for the detection of
LLMs-generated codes, mitigating the risks associated with their indiscriminate
usage. To the best of our knowledge, our research is the first to investigate
zero-shot detection techniques applied to code generated by advanced black-box
LLMs like ChatGPT. Firstly, we find that existing training-based or zero-shot
text detectors are ineffective in detecting code, likely due to the unique
statistical properties found in code structures. We then modify the previous
zero-shot text detection method, DetectGPT (Mitchell et al., 2023) by utilizing
a surrogate white-box model to estimate the probability of the rightmost
tokens, allowing us to identify code snippets generated by language models.
Through extensive experiments conducted on the python codes of the CodeContest
and APPS dataset, our approach demonstrates its effectiveness by achieving
state-of-the-art detection results on text-davinci-003, GPT-3.5, and GPT-4
models. Moreover, our method exhibits robustness against revision attacks and
generalizes well to Java codes. We also find that the smaller code language
model like PolyCoder-160M performs as a universal code detector, outperforming
the billion-scale counterpart. The codes will be available at
https://github.com/ Xianjun-Yang/Code_detection.git | [
"Xianjun Yang",
"Kexun Zhang",
"Haifeng Chen",
"Linda Petzold",
"William Yang Wang",
"Wei Cheng"
] | 2023-10-08 10:08:21 | http://arxiv.org/abs/2310.05103v1 | http://arxiv.org/pdf/2310.05103v1 | 2310.05103v1 |
Asymmetrically Decentralized Federated Learning | To address the communication burden and privacy concerns associated with the
centralized server in Federated Learning (FL), Decentralized Federated Learning
(DFL) has emerged, which discards the server with a peer-to-peer (P2P)
communication framework. However, most existing DFL algorithms are based on
symmetric topologies, such as ring and grid topologies, which can easily lead
to deadlocks and are susceptible to the impact of network link quality in
practice. To address these issues, this paper proposes the DFedSGPSM algorithm,
which is based on asymmetric topologies and utilizes the Push-Sum protocol to
effectively solve consensus optimization problems. To further improve algorithm
performance and alleviate local heterogeneous overfitting in Federated Learning
(FL), our algorithm combines the Sharpness Aware Minimization (SAM) optimizer
and local momentum. The SAM optimizer employs gradient perturbations to
generate locally flat models and searches for models with uniformly low loss
values, mitigating local heterogeneous overfitting. The local momentum
accelerates the optimization process of the SAM optimizer. Theoretical analysis
proves that DFedSGPSM achieves a convergence rate of
$\mathcal{O}(\frac{1}{\sqrt{T}})$ in a non-convex smooth setting under mild
assumptions. This analysis also reveals that better topological connectivity
achieves tighter upper bounds. Empirically, extensive experiments are conducted
on the MNIST, CIFAR10, and CIFAR100 datasets, demonstrating the superior
performance of our algorithm compared to state-of-the-art optimizers. | [
"Qinglun Li",
"Miao Zhang",
"Nan Yin",
"Quanjun Yin",
"Li Shen"
] | 2023-10-08 09:46:26 | http://arxiv.org/abs/2310.05093v1 | http://arxiv.org/pdf/2310.05093v1 | 2310.05093v1 |
FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score | Detecting out-of-distribution (OOD) instances is crucial for NLP models in
practical applications. Although numerous OOD detection methods exist, most of
them are empirical. Backed by theoretical analysis, this paper advocates for
the measurement of the "OOD-ness" of a test case $\boldsymbol{x}$ through the
likelihood ratio between out-distribution $\mathcal P_{\textit{out}}$ and
in-distribution $\mathcal P_{\textit{in}}$. We argue that the state-of-the-art
(SOTA) feature-based OOD detection methods, such as Maha and KNN, are
suboptimal since they only estimate in-distribution density
$p_{\textit{in}}(\boldsymbol{x})$. To address this issue, we propose FLatS, a
principled solution for OOD detection based on likelihood ratio. Moreover, we
demonstrate that FLatS can serve as a general framework capable of enhancing
other OOD detection methods by incorporating out-distribution density
$p_{\textit{out}}(\boldsymbol{x})$ estimation. Experiments show that FLatS
establishes a new SOTA on popular benchmarks. Our code is publicly available at
https://github.com/linhaowei1/FLatS. | [
"Haowei Lin",
"Yuntian Gu"
] | 2023-10-08 09:16:46 | http://arxiv.org/abs/2310.05083v1 | http://arxiv.org/pdf/2310.05083v1 | 2310.05083v1 |
Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference? | The inference of Large language models (LLMs) requires immense computation
and memory resources. To curtail these costs, quantisation has merged as a
promising solution, but existing LLM quantisation mainly focuses on 8-bit. In
this work, we explore the statistical and learning properties of the LLM layer
and attribute the bottleneck of LLM quantisation to numerical scaling offsets.
To address this, we adapt block quantisations for LLMs, a family of methods
that share scaling factors across packed numbers. Block quantisations
efficiently reduce the numerical scaling offsets solely from an arithmetic
perspective, without additional treatments in the computational path. Our
nearly-lossless quantised 6-bit LLMs achieve a $19\times$ higher arithmetic
density and $5\times$ memory density than the float32 baseline, surpassing the
prior art 8-bit quantisation by $2.5\times$ in arithmetic density and
$1.2\times$ in memory density, without requiring any data calibration or
re-training. We also share our insights into sub-8-bit LLM quantisation,
including the mismatch between activation and weight distributions, optimal
fine-tuning strategies, and a lower quantisation granularity inherent in the
statistical properties of LLMs. The latter two tricks enable nearly-lossless
4-bit LLMs on downstream tasks. Our code is open-sourced. | [
"Cheng Zhang",
"Jianyi Cheng",
"Ilia Shumailov",
"George A. Constantinides",
"Yiren Zhao"
] | 2023-10-08 09:05:14 | http://arxiv.org/abs/2310.05079v2 | http://arxiv.org/pdf/2310.05079v2 | 2310.05079v2 |
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning | Federated learning (FL) typically faces data heterogeneity, i.e.,
distribution shifting among clients. Sharing clients' information has shown
great potentiality in mitigating data heterogeneity, yet incurs a dilemma in
preserving privacy and promoting model performance. To alleviate the dilemma,
we raise a fundamental question: \textit{Is it possible to share partial
features in the data to tackle data heterogeneity?} In this work, we give an
affirmative answer to this question by proposing a novel approach called
{\textbf{Fed}erated \textbf{Fe}ature \textbf{d}istillation} (FedFed).
Specifically, FedFed partitions data into performance-sensitive features (i.e.,
greatly contributing to model performance) and performance-robust features
(i.e., limitedly contributing to model performance). The performance-sensitive
features are globally shared to mitigate data heterogeneity, while the
performance-robust features are kept locally. FedFed enables clients to train
models over local and shared data. Comprehensive experiments demonstrate the
efficacy of FedFed in promoting model performance. | [
"Zhiqin Yang",
"Yonggang Zhang",
"Yu Zheng",
"Xinmei Tian",
"Hao Peng",
"Tongliang Liu",
"Bo Han"
] | 2023-10-08 09:00:59 | http://arxiv.org/abs/2310.05077v1 | http://arxiv.org/pdf/2310.05077v1 | 2310.05077v1 |
Towards Scalable Wireless Federated Learning: Challenges and Solutions | The explosive growth of smart devices (e.g., mobile phones, vehicles, drones)
with sensing, communication, and computation capabilities gives rise to an
unprecedented amount of data. The generated massive data together with the
rapid advancement of machine learning (ML) techniques spark a variety of
intelligent applications. To distill intelligence for supporting these
applications, federated learning (FL) emerges as an effective distributed ML
framework, given its potential to enable privacy-preserving model training at
the network edge. In this article, we discuss the challenges and solutions of
achieving scalable wireless FL from the perspectives of both network design and
resource orchestration. For network design, we discuss how task-oriented model
aggregation affects the performance of wireless FL, followed by proposing
effective wireless techniques to enhance the communication scalability via
reducing the model aggregation distortion and improving the device
participation. For resource orchestration, we identify the limitations of the
existing optimization-based algorithms and propose three task-oriented learning
algorithms to enhance the algorithmic scalability via achieving
computation-efficient resource allocation for wireless FL. We highlight several
potential research issues that deserve further study. | [
"Yong Zhou",
"Yuanming Shi",
"Haibo Zhou",
"Jingjing Wang",
"Liqun Fu",
"Yang Yang"
] | 2023-10-08 08:55:03 | http://arxiv.org/abs/2310.05076v1 | http://arxiv.org/pdf/2310.05076v1 | 2310.05076v1 |
Robust-GBDT: A Novel Gradient Boosting Model for Noise-Robust Classification | Robust boosting algorithms have emerged as alternative solutions to
traditional boosting techniques for addressing label noise in classification
tasks. However, these methods have predominantly focused on binary
classification, limiting their applicability to multi-class tasks. Furthermore,
they encounter challenges with imbalanced datasets, missing values, and
computational efficiency. In this paper, we establish that the loss function
employed in advanced Gradient Boosting Decision Trees (GBDT), particularly
Newton's method-based GBDT, need not necessarily exhibit global convexity.
Instead, the loss function only requires convexity within a specific region.
Consequently, these GBDT models can leverage the benefits of nonconvex robust
loss functions, making them resilient to noise. Building upon this theoretical
insight, we introduce a new noise-robust boosting model called Robust-GBDT,
which seamlessly integrates the advanced GBDT framework with robust losses.
Additionally, we enhance the existing robust loss functions and introduce a
novel robust loss function, Robust Focal Loss, designed to address class
imbalance. As a result, Robust-GBDT generates more accurate predictions,
significantly enhancing its generalization capabilities, especially in
scenarios marked by label noise and class imbalance. Furthermore, Robust-GBDT
is user-friendly and can easily integrate existing open-source code, enabling
it to effectively handle complex datasets while improving computational
efficiency. Numerous experiments confirm the superiority of Robust-GBDT over
other noise-robust methods. | [
"Jiaqi Luo",
"Yuedong Quan",
"Shixin Xu"
] | 2023-10-08 08:28:40 | http://arxiv.org/abs/2310.05067v1 | http://arxiv.org/pdf/2310.05067v1 | 2310.05067v1 |
Guideline Learning for In-context Information Extraction | Large language models (LLMs) can perform a new task by merely conditioning on
task instructions and a few input-output examples, without optimizing any
parameters. This is called In-Context Learning (ICL). In-context Information
Extraction (IE) has recently garnered attention in the research community.
However, the performance of In-context IE generally lags behind the
state-of-the-art supervised expert models. We highlight a key reason for this
shortfall: underspecified task description. The limited-length context
struggles to thoroughly express the intricate IE task instructions and various
edge cases, leading to misalignment in task comprehension with humans. In this
paper, we propose a Guideline Learning (GL) framework for In-context IE which
reflectively learns and follows guidelines. During the learning phrase, GL
automatically synthesizes a set of guidelines based on a few error cases, and
during inference, GL retrieves helpful guidelines for better ICL. Moreover, we
propose a self-consistency-based active learning method to enhance the
efficiency of GL. Experiments on event extraction and relation extraction show
that GL can significantly improve the performance of in-context IE. | [
"Chaoxu Pang",
"Yixuan Cao",
"Qiang Ding",
"Ping Luo"
] | 2023-10-08 08:25:16 | http://arxiv.org/abs/2310.05066v2 | http://arxiv.org/pdf/2310.05066v2 | 2310.05066v2 |
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain | Time series has been left behind in the era of pre-training and transfer
learning. While research in the fields of natural language processing and
computer vision are enjoying progressively larger datasets to train massive
models, the most popular time series datasets consist of only tens of thousands
of time steps, limiting our ability to study the effectiveness of pre-training
and scaling. Recent studies have also cast doubt on the need for expressive
models and scale. To alleviate these issues, we introduce three large-scale
time series forecasting datasets from the cloud operations (CloudOps) domain,
the largest having billions of observations, enabling further study into
pre-training and scaling of time series models. We build the empirical
groundwork for studying pre-training and scaling of time series models and pave
the way for future research by identifying a promising candidate architecture.
We show that it is a strong zero-shot baseline and benefits from further
scaling, both in model and dataset size. Accompanying these datasets and
results is a suite of comprehensive benchmark results comparing classical and
deep learning baselines to our pre-trained method - achieving a 27% reduction
in error on the largest dataset. Code and datasets will be released. | [
"Gerald Woo",
"Chenghao Liu",
"Akshat Kumar",
"Doyen Sahoo"
] | 2023-10-08 08:09:51 | http://arxiv.org/abs/2310.05063v2 | http://arxiv.org/pdf/2310.05063v2 | 2310.05063v2 |
FP3O: Enabling Proximal Policy Optimization in Multi-Agent Cooperation with Parameter-Sharing Versatility | Existing multi-agent PPO algorithms lack compatibility with different types
of parameter sharing when extending the theoretical guarantee of PPO to
cooperative multi-agent reinforcement learning (MARL). In this paper, we
propose a novel and versatile multi-agent PPO algorithm for cooperative MARL to
overcome this limitation. Our approach is achieved upon the proposed
full-pipeline paradigm, which establishes multiple parallel optimization
pipelines by employing various equivalent decompositions of the advantage
function. This procedure successfully formulates the interconnections among
agents in a more general manner, i.e., the interconnections among pipelines,
making it compatible with diverse types of parameter sharing. We provide a
solid theoretical foundation for policy improvement and subsequently develop a
practical algorithm called Full-Pipeline PPO (FP3O) by several approximations.
Empirical evaluations on Multi-Agent MuJoCo and StarCraftII tasks demonstrate
that FP3O outperforms other strong baselines and exhibits remarkable
versatility across various parameter-sharing configurations. | [
"Lang Feng",
"Dong Xing",
"Junru Zhang",
"Gang Pan"
] | 2023-10-08 07:26:35 | http://arxiv.org/abs/2310.05053v1 | http://arxiv.org/pdf/2310.05053v1 | 2310.05053v1 |
Learning Intra- and Inter-Cell Differences for Accurate Battery Lifespan Prediction across Diverse Conditions | Battery life prediction holds significant practical value for battery
research and development. Currently, many data-driven models rely on early
electrical signals from specific target batteries to predict their lifespan. A
common shortfall is that most existing methods are developed based on specific
aging conditions, which not only limits their model's capability but also
diminishes their effectiveness in predicting degradation under varied
conditions. As a result, these models often miss out on fully benefiting from
the rich historical data available under other conditions. Here, to address
above, we introduce an approach that explicitly captures differences between
electrical signals of a target battery and a reference battery, irrespective of
their materials and aging conditions, to forecast the target battery life.
Through this inter-cell difference, we not only enhance the feature space but
also pave the way for a universal battery life prediction framework.
Remarkably, our model that combines the inter- and intra-cell differences
shines across diverse conditions, standing out in its efficiency and accuracy
using all accessible datasets. An essential application of our approach is its
capability to leverage data from older batteries effectively, enabling newer
batteries to capitalize on insights gained from past batteries. This work not
only enriches the battery data utilization strategy but also sets the stage for
smarter battery management system in the future. | [
"Han Zhang",
"Yuqi Li",
"Shun Zheng",
"Ziheng Lu",
"Xiaofan Gui",
"Wei Xu",
"Jiang Bian"
] | 2023-10-08 07:25:27 | http://arxiv.org/abs/2310.05052v2 | http://arxiv.org/pdf/2310.05052v2 | 2310.05052v2 |
Online Learning in Contextual Second-Price Pay-Per-Click Auctions | We study online learning in contextual pay-per-click auctions where at each
of the $T$ rounds, the learner receives some context along with a set of ads
and needs to make an estimate on their click-through rate (CTR) in order to run
a second-price pay-per-click auction. The learner's goal is to minimize her
regret, defined as the gap between her total revenue and that of an oracle
strategy that always makes perfect CTR predictions. We first show that
$\sqrt{T}$-regret is obtainable via a computationally inefficient algorithm and
that it is unavoidable since our algorithm is no easier than the classical
multi-armed bandit problem. A by-product of our results is a $\sqrt{T}$-regret
bound for the simpler non-contextual setting, improving upon a recent work of
[Feng et al., 2023] by removing the inverse CTR dependency that could be
arbitrarily large. Then, borrowing ideas from recent advances on efficient
contextual bandit algorithms, we develop two practically efficient contextual
auction algorithms: the first one uses the exponential weight scheme with
optimistic square errors and maintains the same $\sqrt{T}$-regret bound, while
the second one reduces the problem to online regression via a simple
epsilon-greedy strategy, albeit with a worse regret bound. Finally, we conduct
experiments on a synthetic dataset to showcase the effectiveness and superior
performance of our algorithms. | [
"Mengxiao Zhang",
"Haipeng Luo"
] | 2023-10-08 07:04:22 | http://arxiv.org/abs/2310.05047v1 | http://arxiv.org/pdf/2310.05047v1 | 2310.05047v1 |
Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks | Supporting ultra-high data rates and flexible reconfigurability, Terahertz
(THz) mesh networks are attractive for next-generation wireless backhaul
systems that empower the integrated access and backhaul (IAB). In THz mesh
backhaul networks, the efficient cross-layer routing and long-term resource
allocation is yet an open problem due to dynamic traffic demands as well as
possible link failures caused by the high directivity and high
non-line-of-sight (NLoS) path loss of THz spectrum. In addition, unpredictable
data traffic and the mixed integer programming property with the NP-hard nature
further challenge the effective routing and long-term resource allocation
design. In this paper, a deep reinforcement learning (DRL) based cross-layer
design in THz mesh backhaul networks (DEFLECT) is proposed, by considering
dynamic traffic demands and possible sudden link failures. In DEFLECT, a
heuristic routing metric is first devised to facilitate resource efficiency
(RE) enhancement regarding energy and sub-array usages. Furthermore, a DRL
based resource allocation algorithm is developed to realize long-term RE
maximization and fast recovery from broken links. Specifically in the DRL
method, the exploited multi-task structure cooperatively benefits joint power
and sub-array allocation. Additionally, the leveraged hierarchical architecture
realizes tailored resource allocation for each base station and learned
knowledge transfer for fast recovery. Simulation results show that DEFLECT
routing consumes less resource, compared to the minimal hop-count metric.
Moreover, unlike conventional DRL methods causing packet loss and second-level
latency, DEFLECT DRL realizes the long-term RE maximization with no packet loss
and millisecond-level latency, and recovers resource-efficient backhaul from
broken links within 1s. | [
"Zhifeng Hu",
"Chong Han",
"Xudong Wang"
] | 2023-10-08 06:36:00 | http://arxiv.org/abs/2310.05034v1 | http://arxiv.org/pdf/2310.05034v1 | 2310.05034v1 |
Hybrid Quantum-Classical Machine Learning for Sentiment Analysis | The collaboration between quantum computing and classical machine learning
offers potential advantages in natural language processing, particularly in the
sentiment analysis of human emotions and opinions expressed in large-scale
datasets. In this work, we propose a methodology for sentiment analysis using
hybrid quantum-classical machine learning algorithms. We investigate quantum
kernel approaches and variational quantum circuit-based classifiers and
integrate them with classical dimension reduction techniques such as PCA and
Haar wavelet transform. The proposed methodology is evaluated using two
distinct datasets, based on English and Bengali languages. Experimental results
show that after dimensionality reduction of the data, performance of the
quantum-based hybrid algorithms were consistent and better than classical
methods. | [
"Abu Kaisar Mohammad Masum",
"Anshul Maurya",
"Dhruthi Sridhar Murthy",
"Pratibha",
"Naveed Mahmud"
] | 2023-10-08 05:45:22 | http://arxiv.org/abs/2310.10672v1 | http://arxiv.org/pdf/2310.10672v1 | 2310.10672v1 |
Compressed online Sinkhorn | The use of optimal transport (OT) distances, and in particular
entropic-regularised OT distances, is an increasingly popular evaluation metric
in many areas of machine learning and data science. Their use has largely been
driven by the availability of efficient algorithms such as the Sinkhorn
algorithm. One of the drawbacks of the Sinkhorn algorithm for large-scale data
processing is that it is a two-phase method, where one first draws a large
stream of data from the probability distributions, before applying the Sinkhorn
algorithm to the discrete probability measures. More recently, there have been
several works developing stochastic versions of Sinkhorn that directly handle
continuous streams of data. In this work, we revisit the recently introduced
online Sinkhorn algorithm of [Mensch and Peyr\'e, 2020]. Our contributions are
twofold: We improve the convergence analysis for the online Sinkhorn algorithm,
the new rate that we obtain is faster than the previous rate under certain
parameter choices. We also present numerical results to verify the sharpness of
our result. Secondly, we propose the compressed online Sinkhorn algorithm which
combines measure compression techniques with the online Sinkhorn algorithm. We
provide numerical experiments to show practical numerical gains, as well as
theoretical guarantees on the efficiency of our approach. | [
"Fengpei Wang",
"Clarice Poon",
"Tony Shardlow"
] | 2023-10-08 05:33:32 | http://arxiv.org/abs/2310.05019v1 | http://arxiv.org/pdf/2310.05019v1 | 2310.05019v1 |
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy | Machine learning methods are progressively gaining acceptance in the electron
microscopy community for de-noising, semantic segmentation, and dimensionality
reduction of data post-acquisition. The introduction of the APIs by major
instrument manufacturers now allows the deployment of ML workflows in
microscopes, not only for data analytics but also for real-time decision-making
and feedback for microscope operation. However, the number of use cases for
real-time ML remains remarkably small. Here, we discuss some considerations in
designing ML-based active experiments and pose that the likely strategy for the
next several years will be human-in-the-loop automated experiments (hAE). In
this paradigm, the ML learning agent directly controls beam position and image
and spectroscopy acquisition functions, and human operator monitors experiment
progression in real- and feature space of the system and tunes the policies of
the ML agent to steer the experiment towards specific objectives. | [
"Sergei V. Kalinin",
"Yongtao Liu",
"Arpan Biswas",
"Gerd Duscher",
"Utkarsh Pratiush",
"Kevin Roccapriore",
"Maxim Ziatdinov",
"Rama Vasudevan"
] | 2023-10-08 05:26:32 | http://arxiv.org/abs/2310.05018v1 | http://arxiv.org/pdf/2310.05018v1 | 2310.05018v1 |
Data Augmentation through Pseudolabels in Automatic Region Based Coronary Artery Segmentation for Disease Diagnosis | Coronary Artery Diseases(CADs) though preventable are one of the leading
causes of death and disability. Diagnosis of these diseases is often difficult
and resource intensive. Segmentation of arteries in angiographic images has
evolved as a tool for assistance, helping clinicians in making accurate
diagnosis. However, due to the limited amount of data and the difficulty in
curating a dataset, the task of segmentation has proven challenging. In this
study, we introduce the idea of using pseudolabels as a data augmentation
technique to improve the performance of the baseline Yolo model. This method
increases the F1 score of the baseline by 9% in the validation dataset and by
3% in the test dataset. | [
"Sandesh Pokhrel",
"Sanjay Bhandari",
"Eduard Vazquez",
"Yash Raj Shrestha",
"Binod Bhattarai"
] | 2023-10-08 04:54:12 | http://arxiv.org/abs/2310.05990v1 | http://arxiv.org/pdf/2310.05990v1 | 2310.05990v1 |
The Reinforce Policy Gradient Algorithm Revisited | We revisit the Reinforce policy gradient algorithm from the literature. Note
that this algorithm typically works with cost returns obtained over random
length episodes obtained from either termination upon reaching a goal state (as
with episodic tasks) or from instants of visit to a prescribed recurrent state
(in the case of continuing tasks). We propose a major enhancement to the basic
algorithm. We estimate the policy gradient using a function measurement over a
perturbed parameter by appealing to a class of random search approaches. This
has advantages in the case of systems with infinite state and action spaces as
it relax some of the regularity requirements that would otherwise be needed for
proving convergence of the Reinforce algorithm. Nonetheless, we observe that
even though we estimate the gradient of the performance objective using the
performance objective itself (and not via the sample gradient), the algorithm
converges to a neighborhood of a local minimum. We also provide a proof of
convergence for this new algorithm. | [
"Shalabh Bhatnagar"
] | 2023-10-08 04:05:13 | http://arxiv.org/abs/2310.05000v1 | http://arxiv.org/pdf/2310.05000v1 | 2310.05000v1 |
Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning | Joint entity and relation extraction is a process that identifies entity
pairs and their relations using a single model. We focus on the problem of
training these models on distantly-labeled data, which is generated by aligning
entity mentions in a text corpus with their corresponding entity and relation
types in a knowledge base. One key challenge here is the presence of noisy
labels, which arises from both entity and relation annotations, and
significantly impair the effectiveness of supervised learning applications.
However, existing research primarily addresses only one type of noise, thereby
limiting the effectiveness of noise reduction. To fill this gap, we introduce a
new noise-robust approach, that 1)~incorporates a pre-trained GPT-2 into a
sequence tagging scheme for simultaneous entity and relation detection, and
2)~employs a noise-robust learning framework which includes a new loss function
that penalizes inconsistency with both significant relation patterns and
entity-relation dependencies, as well as a self-adaptive learning step that
iteratively selects and trains on high-quality instances. Experiments on two
datasets show that our method outperforms the existing state-of-the-art methods
in both joint extraction performance and noise reduction effect. | [
"Yufei Li",
"Xiao Yu",
"Yanghong Guo",
"Yanchi Liu",
"Haifeng Chen",
"Cong Liu"
] | 2023-10-08 03:42:15 | http://arxiv.org/abs/2310.04994v1 | http://arxiv.org/pdf/2310.04994v1 | 2310.04994v1 |
Prompt-augmented Temporal Point Process for Streaming Event Sequence | Neural Temporal Point Processes (TPPs) are the prevalent paradigm for
modeling continuous-time event sequences, such as user activities on the web
and financial transactions. In real-world applications, event data is typically
received in a \emph{streaming} manner, where the distribution of patterns may
shift over time. Additionally, \emph{privacy and memory constraints} are
commonly observed in practical scenarios, further compounding the challenges.
Therefore, the continuous monitoring of a TPP to learn the streaming event
sequence is an important yet under-explored problem. Our work paper addresses
this challenge by adopting Continual Learning (CL), which makes the model
capable of continuously learning a sequence of tasks without catastrophic
forgetting under realistic constraints. Correspondingly, we propose a simple
yet effective framework, PromptTPP\footnote{Our code is available at {\small
\url{ https://github.com/yanyanSann/PromptTPP}}}, by integrating the base TPP
with a continuous-time retrieval prompt pool. The prompts, small learnable
parameters, are stored in a memory space and jointly optimized with the base
TPP, ensuring that the model learns event streams sequentially without
buffering past examples or task-specific attributes. We present a novel and
realistic experimental setup for modeling event streams, where PromptTPP
consistently achieves state-of-the-art performance across three real user
behavior datasets. | [
"Siqiao Xue",
"Yan Wang",
"Zhixuan Chu",
"Xiaoming Shi",
"Caigao Jiang",
"Hongyan Hao",
"Gangwei Jiang",
"Xiaoyun Feng",
"James Y. Zhang",
"Jun Zhou"
] | 2023-10-08 03:41:16 | http://arxiv.org/abs/2310.04993v2 | http://arxiv.org/pdf/2310.04993v2 | 2310.04993v2 |
Waveformer for modelling dynamical systems | Neural operators have gained recognition as potent tools for learning
solutions of a family of partial differential equations. The state-of-the-art
neural operators excel at approximating the functional relationship between
input functions and the solution space, potentially reducing computational
costs and enabling real-time applications. However, they often fall short when
tackling time-dependent problems, particularly in delivering accurate long-term
predictions. In this work, we propose "waveformer", a novel operator learning
approach for learning solutions of dynamical systems. The proposed waveformer
exploits wavelet transform to capture the spatial multi-scale behavior of the
solution field and transformers for capturing the long horizon dynamics. We
present four numerical examples involving Burgers's equation, KS-equation,
Allen Cahn equation, and Navier Stokes equation to illustrate the efficacy of
the proposed approach. Results obtained indicate the capability of the proposed
waveformer in learning the solution operator and show that the proposed
Waveformer can learn the solution operator with high accuracy, outperforming
existing state-of-the-art operator learning algorithms by up to an order, with
its advantage particularly visible in the extrapolation region | [
"N Navaneeth",
"Souvik Chakraborty"
] | 2023-10-08 03:34:59 | http://arxiv.org/abs/2310.04990v1 | http://arxiv.org/pdf/2310.04990v1 | 2310.04990v1 |
Data-centric Graph Learning: A Survey | The history of artificial intelligence (AI) has witnessed the significant
impact of high-quality data on various deep learning models, such as ImageNet
for AlexNet and ResNet. Recently, instead of designing more complex neural
architectures as model-centric approaches, the attention of AI community has
shifted to data-centric ones, which focuses on better processing data to
strengthen the ability of neural models. Graph learning, which operates on
ubiquitous topological data, also plays an important role in the era of deep
learning. In this survey, we comprehensively review graph learning approaches
from the data-centric perspective, and aim to answer two crucial questions: (1)
when to modify graph data and (2) how to modify graph data to unlock the
potential of various graph models. Accordingly, we propose a novel taxonomy
based on the stages in the graph learning pipeline, and highlight the
processing methods for different data structures in the graph data, i.e.,
topology, feature and label. Furthermore, we analyze some potential problems
embedded in graph data and discuss how to solve them in a data-centric manner.
Finally, we provide some promising future directions for data-centric graph
learning. | [
"Cheng Yang",
"Deyu Bo",
"Jixi Liu",
"Yufei Peng",
"Boyu Chen",
"Haoran Dai",
"Ao Sun",
"Yue Yu",
"Yixin Xiao",
"Qi Zhang",
"Chunchen Wang",
"Yuxin Guo",
"Chuan Shi"
] | 2023-10-08 03:17:22 | http://arxiv.org/abs/2310.04987v1 | http://arxiv.org/pdf/2310.04987v1 | 2310.04987v1 |
Model-adapted Fourier sampling for generative compressed sensing | We study generative compressed sensing when the measurement matrix is
randomly subsampled from a unitary matrix (with the DFT as an important special
case). It was recently shown that $\textit{O}(kdn\|
\boldsymbol{\alpha}\|_{\infty}^{2})$ uniformly random Fourier measurements are
sufficient to recover signals in the range of a neural network $G:\mathbb{R}^k
\to \mathbb{R}^n$ of depth $d$, where each component of the so-called local
coherence vector $\boldsymbol{\alpha}$ quantifies the alignment of a
corresponding Fourier vector with the range of $G$. We construct a
model-adapted sampling strategy with an improved sample complexity of
$\textit{O}(kd\| \boldsymbol{\alpha}\|_{2}^{2})$ measurements. This is enabled
by: (1) new theoretical recovery guarantees that we develop for nonuniformly
random sampling distributions and then (2) optimizing the sampling distribution
to minimize the number of measurements needed for these guarantees. This
development offers a sample complexity applicable to natural signal classes,
which are often almost maximally coherent with low Fourier frequencies.
Finally, we consider a surrogate sampling scheme, and validate its performance
in recovery experiments using the CelebA dataset. | [
"Aaron Berk",
"Simone Brugiapaglia",
"Yaniv Plan",
"Matthew Scott",
"Xia Sheng",
"Ozgur Yilmaz"
] | 2023-10-08 03:13:16 | http://arxiv.org/abs/2310.04984v1 | http://arxiv.org/pdf/2310.04984v1 | 2310.04984v1 |
Compositional Semantics for Open Vocabulary Spatio-semantic Representations | General-purpose mobile robots need to complete tasks without exact human
instructions. Large language models (LLMs) is a promising direction for
realizing commonsense world knowledge and reasoning-based planning.
Vision-language models (VLMs) transform environment percepts into
vision-language semantics interpretable by LLMs. However, completing complex
tasks often requires reasoning about information beyond what is currently
perceived. We propose latent compositional semantic embeddings z* as a
principled learning-based knowledge representation for queryable
spatio-semantic memories. We mathematically prove that z* can always be found,
and the optimal z* is the centroid for any set Z. We derive a probabilistic
bound for estimating separability of related and unrelated semantics. We prove
that z* is discoverable by iterative optimization by gradient descent from
visual appearance and singular descriptions. We experimentally verify our
findings on four embedding spaces incl. CLIP and SBERT. Our results show that
z* can represent up to 10 semantics encoded by SBERT, and up to 100 semantics
for ideal uniformly distributed high-dimensional embeddings. We demonstrate
that a simple dense VLM trained on the COCO-Stuff dataset can learn z* for 181
overlapping semantics by 42.23 mIoU, while improving conventional
non-overlapping open-vocabulary segmentation performance by +3.48 mIoU compared
with a popular SOTA model. | [
"Robin Karlsson",
"Francisco Lepe-Salazar",
"Kazuya Takeda"
] | 2023-10-08 03:07:14 | http://arxiv.org/abs/2310.04981v1 | http://arxiv.org/pdf/2310.04981v1 | 2310.04981v1 |
TopicAdapt- An Inter-Corpora Topics Adaptation Approach | Topic models are popular statistical tools for detecting latent semantic
topics in a text corpus. They have been utilized in various applications across
different fields. However, traditional topic models have some limitations,
including insensitivity to user guidance, sensitivity to the amount and quality
of data, and the inability to adapt learned topics from one corpus to another.
To address these challenges, this paper proposes a neural topic model,
TopicAdapt, that can adapt relevant topics from a related source corpus and
also discover new topics in a target corpus that are absent in the source
corpus. The proposed model offers a promising approach to improve topic
modeling performance in practical scenarios. Experiments over multiple datasets
from diverse domains show the superiority of the proposed model against the
state-of-the-art topic models. | [
"Pritom Saha Akash",
"Trisha Das",
"Kevin Chen-Chuan Chang"
] | 2023-10-08 02:56:44 | http://arxiv.org/abs/2310.04978v1 | http://arxiv.org/pdf/2310.04978v1 | 2310.04978v1 |
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift | Recently, multimodal contrastive learning (MMCL) approaches, such as CLIP,
have achieved a remarkable success in learning representations that are robust
against distribution shift and generalize to new domains. Despite the empirical
success, the mechanism behind learning such generalizable representations is
not understood. In this work, we rigorously analyze this problem and uncover
two mechanisms behind MMCL's robustness: \emph{intra-class contrasting}, which
allows the model to learn features with a high variance, and \emph{inter-class
feature sharing}, where annotated details in one class help learning other
classes better. Both mechanisms prevent spurious features that are
over-represented in the training data to overshadow the generalizable core
features. This yields superior zero-shot classification accuracy under
distribution shift. Furthermore, we theoretically demonstrate the benefits of
using rich captions on robustness and explore the effect of annotating
different types of details in the captions. We validate our theoretical
findings through experiments, including a well-designed synthetic experiment
and an experiment involving training CLIP on MS COCO and evaluating the model
on variations of shifted ImageNet. | [
"Yihao Xue",
"Siddharth Joshi",
"Dang Nguyen",
"Baharan Mirzasoleiman"
] | 2023-10-08 02:25:52 | http://arxiv.org/abs/2310.04971v1 | http://arxiv.org/pdf/2310.04971v1 | 2310.04971v1 |
Improved Active Learning via Dependent Leverage Score Sampling | We show how to obtain improved active learning methods in the agnostic
(adversarial noise) setting by combining marginal leverage score sampling with
non-independent sampling strategies that promote spatial coverage. In
particular, we propose an easily implemented method based on the pivotal
sampling algorithm, which we test on problems motivated by learning-based
methods for parametric PDEs and uncertainty quantification. In comparison to
independent sampling, our method reduces the number of samples needed to reach
a given target accuracy by up to $50\%$. We support our findings with two
theoretical results. First, we show that any non-independent leverage score
sampling method that obeys a weak one-sided $\ell_{\infty}$ independence
condition (which includes pivotal sampling) can actively learn $d$ dimensional
linear functions with $O(d\log d)$ samples, matching independent sampling. This
result extends recent work on matrix Chernoff bounds under $\ell_{\infty}$
independence, and may be of interest for analyzing other sampling strategies
beyond pivotal sampling. Second, we show that, for the important case of
polynomial regression, our pivotal method obtains an improved bound of $O(d)$
samples. | [
"Atsushi Shimizu",
"Xiaoou Cheng",
"Christopher Musco",
"Jonathan Weare"
] | 2023-10-08 01:51:30 | http://arxiv.org/abs/2310.04966v1 | http://arxiv.org/pdf/2310.04966v1 | 2310.04966v1 |
MULTISCRIPT: Multimodal Script Learning for Supporting Open Domain Everyday Tasks | Automatically generating scripts (i.e. sequences of key steps described in
text) from video demonstrations and reasoning about the subsequent steps are
crucial to the modern AI virtual assistants to guide humans to complete
everyday tasks, especially unfamiliar ones. However, current methods for
generative script learning rely heavily on well-structured preceding steps
described in text and/or images or are limited to a certain domain, resulting
in a disparity with real-world user scenarios. To address these limitations, we
present a new benchmark challenge -- MultiScript, with two new tasks on
task-oriented multimodal script learning: (1) multimodal script generation, and
(2) subsequent step prediction. For both tasks, the input consists of a target
task name and a video illustrating what has been done to complete the target
task, and the expected output is (1) a sequence of structured step descriptions
in text based on the demonstration video, and (2) a single text description for
the subsequent step, respectively. Built from WikiHow, MultiScript covers
multimodal scripts in videos and text descriptions for over 6,655 human
everyday tasks across 19 diverse domains. To establish baseline performance on
MultiScript, we propose two knowledge-guided multimodal generative frameworks
that incorporate the task-related knowledge prompted from large language models
such as Vicuna. Experimental results show that our proposed approaches
significantly improve over the competitive baselines. | [
"Jingyuan Qi",
"Minqian Liu",
"Ying Shen",
"Zhiyang Xu",
"Lifu Huang"
] | 2023-10-08 01:51:17 | http://arxiv.org/abs/2310.04965v1 | http://arxiv.org/pdf/2310.04965v1 | 2310.04965v1 |
Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing | Deep learning has seen a rapid adoption in a variety of wireless
communications applications, including at the physical layer. While it has
delivered impressive performance in tasks such as channel equalization and
receive processing/symbol detection, it leaves much to be desired when it comes
to explaining this superior performance. In this work, we investigate the
specific task of channel equalization by applying a popular learning-based
technique known as Reservoir Computing (RC), which has shown superior
performance compared to conventional methods and other learning-based
approaches. Specifically, we apply the echo state network (ESN) as a channel
equalizer and provide a first principles-based signal processing understanding
of its operation. With this groundwork, we incorporate the available domain
knowledge in the form of the statistics of the wireless channel directly into
the weights of the ESN model. This paves the way for optimized initialization
of the ESN model weights, which are traditionally untrained and randomly
initialized. Finally, we show the improvement in receive processing/symbol
detection performance with this optimized initialization through simulations.
This is a first step towards explainable machine learning (XML) and assigning
practical model interpretability that can be utilized together with the
available domain knowledge to improve performance and enhance detection
reliability. | [
"Shashank Jere",
"Karim Said",
"Lizhong Zheng",
"Lingjia Liu"
] | 2023-10-08 00:44:35 | http://arxiv.org/abs/2310.04956v1 | http://arxiv.org/pdf/2310.04956v1 | 2310.04956v1 |
Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks | Ensuring a neural network is not relying on protected attributes (e.g., race,
sex, age) for predictions is crucial in advancing fair and trustworthy AI.
While several promising methods for removing attribute bias in neural networks
have been proposed, their limitations remain under-explored. In this work, we
mathematically and empirically reveal an important limitation of attribute bias
removal methods in presence of strong bias. Specifically, we derive a general
non-vacuous information-theoretical upper bound on the performance of any
attribute bias removal method in terms of the bias strength. We provide
extensive experiments on synthetic, image, and census datasets to verify the
theoretical bound and its consequences in practice. Our findings show that
existing attribute bias removal methods are effective only when the inherent
bias in the dataset is relatively weak, thus cautioning against the use of
these methods in smaller datasets where strong attribute bias can occur, and
advocating the need for methods that can overcome this limitation. | [
"Jiazhi Li",
"Mahyar Khayatkhoei",
"Jiageng Zhu",
"Hanchen Xie",
"Mohamed E. Hussein",
"Wael AbdAlmageed"
] | 2023-10-08 00:39:11 | http://arxiv.org/abs/2310.04955v1 | http://arxiv.org/pdf/2310.04955v1 | 2310.04955v1 |
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion | Applying half-quadratic optimization to loss functions can yield the
corresponding regularizers, while these regularizers are usually not
sparsity-inducing regularizers (SIRs). To solve this problem, we devise a
framework to generate an SIR with closed-form proximity operator. Besides, we
specify our framework using several commonly-used loss functions, and produce
the corresponding SIRs, which are then adopted as nonconvex rank surrogates for
low-rank matrix completion. Furthermore, algorithms based on the alternating
direction method of multipliers are developed. Extensive numerical results show
the effectiveness of our methods in terms of recovery performance and runtime. | [
"Zhi-Yong Wang",
"Hing Cheung So"
] | 2023-10-08 00:35:54 | http://arxiv.org/abs/2310.04954v1 | http://arxiv.org/pdf/2310.04954v1 | 2310.04954v1 |
Safe Deep Policy Adaptation | A critical goal of autonomy and artificial intelligence is enabling
autonomous robots to rapidly adapt in dynamic and uncertain environments.
Classic adaptive control and safe control provide stability and safety
guarantees but are limited to specific system classes. In contrast, policy
adaptation based on reinforcement learning (RL) offers versatility and
generalizability but presents safety and robustness challenges. We propose
SafeDPA, a novel RL and control framework that simultaneously tackles the
problems of policy adaptation and safe reinforcement learning. SafeDPA jointly
learns adaptive policy and dynamics models in simulation, predicts environment
configurations, and fine-tunes dynamics models with few-shot real-world data. A
safety filter based on the Control Barrier Function (CBF) on top of the RL
policy is introduced to ensure safety during real-world deployment. We provide
theoretical safety guarantees of SafeDPA and show the robustness of SafeDPA
against learning errors and extra perturbations. Comprehensive experiments on
(1) classic control problems (Inverted Pendulum), (2) simulation benchmarks
(Safety Gym), and (3) a real-world agile robotics platform (RC Car) demonstrate
great superiority of SafeDPA in both safety and task performance, over
state-of-the-art baselines. Particularly, SafeDPA demonstrates notable
generalizability, achieving a 300% increase in safety rate compared to the
baselines, under unseen disturbances in real-world experiments. | [
"Wenli Xiao",
"Tairan He",
"John Dolan",
"Guanya Shi"
] | 2023-10-08 00:32:59 | http://arxiv.org/abs/2310.08602v1 | http://arxiv.org/pdf/2310.08602v1 | 2310.08602v1 |
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting | The past decade has witnessed significant advances in time series modeling
with deep learning. While achieving state-of-the-art results, the
best-performing architectures vary highly across applications and domains.
Meanwhile, for natural language processing, the Generative Pre-trained
Transformer (GPT) has demonstrated impressive performance via training one
general-purpose model across various textual datasets. It is intriguing to
explore whether GPT-type architectures can be effective for time series,
capturing the intrinsic dynamic attributes and leading to significant accuracy
improvements. In this paper, we propose a novel framework, TEMPO, that can
effectively learn time series representations. We focus on utilizing two
essential inductive biases of the time series task for pre-trained models: (i)
decomposition of the complex interaction between trend, seasonal and residual
components; and (ii) introducing the selection-based prompts to facilitate
distribution adaptation in non-stationary time series. TEMPO expands the
capability for dynamically modeling real-world temporal phenomena from data
within diverse domains. Our experiments demonstrate the superior performance of
TEMPO over state-of-the-art methods on a number of time series benchmark
datasets. This performance gain is observed not only in standard supervised
learning settings but also in scenarios involving previously unseen datasets as
well as in scenarios with multi-modal inputs. This compelling finding
highlights TEMPO's potential to constitute a foundational model-building
framework. | [
"Defu Cao",
"Furong Jia",
"Sercan O Arik",
"Tomas Pfister",
"Yixiang Zheng",
"Wen Ye",
"Yan Liu"
] | 2023-10-08 00:02:25 | http://arxiv.org/abs/2310.04948v2 | http://arxiv.org/pdf/2310.04948v2 | 2310.04948v2 |
Transferable Deep Clustering Model | Deep learning has shown remarkable success in the field of clustering
recently. However, how to transfer a trained clustering model on a source
domain to a target domain by leveraging the acquired knowledge to guide the
clustering process remains challenging. Existing deep clustering methods often
lack generalizability to new domains because they typically learn a group of
fixed cluster centroids, which may not be optimal for the new domain
distributions. In this paper, we propose a novel transferable deep clustering
model that can automatically adapt the cluster centroids according to the
distribution of data samples. Rather than learning a fixed set of centroids,
our approach introduces a novel attention-based module that can adapt the
centroids by measuring their relationship with samples. In addition, we
theoretically show that our model is strictly more powerful than some classical
clustering algorithms such as k-means or Gaussian Mixture Model (GMM).
Experimental results on both synthetic and real-world datasets demonstrate the
effectiveness and efficiency of our proposed transfer learning framework, which
significantly improves the performance on target domain and reduces the
computational cost. | [
"Zheng Zhang",
"Liang Zhao"
] | 2023-10-07 23:35:17 | http://arxiv.org/abs/2310.04946v1 | http://arxiv.org/pdf/2310.04946v1 | 2310.04946v1 |
Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data | Large language models (LLMs) have achieved impressive performance on many
natural language processing tasks. However, their capabilities on
graph-structured data remain relatively unexplored. In this paper, we conduct a
series of experiments benchmarking leading LLMs on diverse graph prediction
tasks spanning node, edge, and graph levels. We aim to assess whether LLMs can
effectively process graph data and leverage topological structures to enhance
performance, compared to specialized graph neural networks. Through varied
prompt formatting and task/dataset selection, we analyze how well LLMs can
interpret and utilize graph structures. By comparing LLMs' performance with
specialized graph models, we offer insights into the strengths and limitations
of employing LLMs for graph analytics. Our findings provide insights into LLMs'
capabilities and suggest avenues for further exploration in applying them to
graph analytics. | [
"Yuntong Hu",
"Zheng Zhang",
"Liang Zhao"
] | 2023-10-07 23:25:22 | http://arxiv.org/abs/2310.04944v1 | http://arxiv.org/pdf/2310.04944v1 | 2310.04944v1 |
Large Language Models for Spatial Trajectory Patterns Mining | Identifying anomalous human spatial trajectory patterns can indicate dynamic
changes in mobility behavior with applications in domains like infectious
disease monitoring and elderly care. Recent advancements in large language
models (LLMs) have demonstrated their ability to reason in a manner akin to
humans. This presents significant potential for analyzing temporal patterns in
human mobility. In this paper, we conduct empirical studies to assess the
capabilities of leading LLMs like GPT-4 and Claude-2 in detecting anomalous
behaviors from mobility data, by comparing to specialized methods. Our key
findings demonstrate that LLMs can attain reasonable anomaly detection
performance even without any specific cues. In addition, providing contextual
clues about potential irregularities could further enhances their prediction
efficacy. Moreover, LLMs can provide reasonable explanations for their
judgments, thereby improving transparency. Our work provides insights on the
strengths and limitations of LLMs for human spatial trajectory analysis. | [
"Zheng Zhang",
"Hossein Amiri",
"Zhenke Liu",
"Andreas Züfle",
"Liang Zhao"
] | 2023-10-07 23:21:29 | http://arxiv.org/abs/2310.04942v1 | http://arxiv.org/pdf/2310.04942v1 | 2310.04942v1 |
Reliable Test-Time Adaptation via Agreement-on-the-Line | Test-time adaptation (TTA) methods aim to improve robustness to distribution
shifts by adapting models using unlabeled data from the shifted test
distribution. However, there remain unresolved challenges that undermine the
reliability of TTA, which include difficulties in evaluating TTA performance,
miscalibration after TTA, and unreliable hyperparameter tuning for adaptation.
In this work, we make a notable and surprising observation that TTAed models
strongly show the agreement-on-the-line phenomenon (Baek et al., 2022) across a
wide range of distribution shifts. We find such linear trends occur
consistently in a wide range of models adapted with various hyperparameters,
and persist in distributions where the phenomenon fails to hold in vanilla
models (i.e., before adaptation). We leverage these observations to make TTA
methods more reliable in three perspectives: (i) estimating OOD accuracy
(without labeled data) to determine when TTA helps and when it hurts, (ii)
calibrating TTAed models without label information, and (iii) reliably
determining hyperparameters for TTA without any labeled validation data.
Through extensive experiments, we demonstrate that various TTA methods can be
precisely evaluated, both in terms of their improvements and degradations.
Moreover, our proposed methods on unsupervised calibration and hyperparameters
tuning for TTA achieve results close to the ones assuming access to
ground-truth labels, in terms of both OOD accuracy and calibration error. | [
"Eungyeup Kim",
"Mingjie Sun",
"Aditi Raghunathan",
"Zico Kolter"
] | 2023-10-07 23:21:25 | http://arxiv.org/abs/2310.04941v1 | http://arxiv.org/pdf/2310.04941v1 | 2310.04941v1 |
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory | Since their inception, Variational Autoencoders (VAEs) have become central in
machine learning. Despite their widespread use, numerous questions regarding
their theoretical properties remain open. Using PAC-Bayesian theory, this work
develops statistical guarantees for VAEs. First, we derive the first
PAC-Bayesian bound for posterior distributions conditioned on individual
samples from the data-generating distribution. Then, we utilize this result to
develop generalization guarantees for the VAE's reconstruction loss, as well as
upper bounds on the distance between the input and the regenerated
distributions. More importantly, we provide upper bounds on the Wasserstein
distance between the input distribution and the distribution defined by the
VAE's generative model. | [
"Sokhna Diarra Mbacke",
"Florence Clerc",
"Pascal Germain"
] | 2023-10-07 22:35:26 | http://arxiv.org/abs/2310.04935v1 | http://arxiv.org/pdf/2310.04935v1 | 2310.04935v1 |
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation | The capability to transfer mastered skills to accomplish a range of similar
yet novel tasks is crucial for intelligent robots. In this work, we introduce
$\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics
simulation to efficiently transfer robotic skills. Specifically,
$\textit{Diff-Transfer}$ discovers a feasible path within the task space that
brings the source task to the target task. At each pair of adjacent points
along this task path, which is two sub-tasks, $\textit{Diff-Transfer}$ adapts
known actions from one sub-task to tackle the other sub-task successfully. The
adaptation is guided by the gradient information from differentiable physics
simulations. We propose a novel path-planning method to generate sub-tasks,
leveraging $Q$-learning with a task-level state and reward. We implement our
framework in simulation experiments and execute four challenging transfer tasks
on robotic manipulation, demonstrating the efficacy of $\textit{Diff-Transfer}$
through comprehensive experiments. Supplementary and Videos are on the website
https://sites.google.com/view/difftransfer | [
"Yuqi Xiang",
"Feitong Chen",
"Qinsi Wang",
"Yang Gang",
"Xiang Zhang",
"Xinghao Zhu",
"Xingyu Liu",
"Lin Shao"
] | 2023-10-07 22:01:49 | http://arxiv.org/abs/2310.04930v2 | http://arxiv.org/pdf/2310.04930v2 | 2310.04930v2 |
DISCOVER: Making Vision Networks Interpretable via Competition and Dissection | Modern deep networks are highly complex and their inferential outcome very
hard to interpret. This is a serious obstacle to their transparent deployment
in safety-critical or bias-aware applications. This work contributes to
post-hoc interpretability, and specifically Network Dissection. Our goal is to
present a framework that makes it easier to discover the individual
functionality of each neuron in a network trained on a vision task; discovery
is performed in terms of textual description generation. To achieve this
objective, we leverage: (i) recent advances in multimodal vision-text models
and (ii) network layers founded upon the novel concept of stochastic local
competition between linear units. In this setting, only a small subset of layer
neurons are activated for a given input, leading to extremely high activation
sparsity (as low as only $\approx 4\%$). Crucially, our proposed method infers
(sparse) neuron activation patterns that enables the neurons to
activate/specialize to inputs with specific characteristics, diversifying their
individual functionality. This capacity of our method supercharges the
potential of dissection processes: human understandable descriptions are
generated only for the very few active neurons, thus facilitating the direct
investigation of the network's decision process. As we experimentally show, our
approach: (i) yields Vision Networks that retain or improve classification
performance, and (ii) realizes a principled framework for text-based
description and examination of the generated neuronal representations. | [
"Konstantinos P. Panousis",
"Sotirios Chatzis"
] | 2023-10-07 21:57:23 | http://arxiv.org/abs/2310.04929v1 | http://arxiv.org/pdf/2310.04929v1 | 2310.04929v1 |
Crystal-GFN: sampling crystals with desirable properties and constraints | Accelerating material discovery holds the potential to greatly help mitigate
the climate crisis. Discovering new solid-state crystals such as
electrocatalysts, ionic conductors or photovoltaics can have a crucial impact,
for instance, in improving the efficiency of renewable energy production and
storage. In this paper, we introduce Crystal-GFlowNet, a generative model of
crystal structures that sequentially samples a crystal's composition, space
group and lattice parameters. This domain-inspired approach enables the
flexible incorporation of physical and geometrical constraints, as well as the
use of any available predictive model of a desired property as an objective
function. We evaluate the capabilities of Crystal-GFlowNet by using as
objective the formation energy of a crystal structure, as predicted by a new
proxy model trained on MatBench. The results demonstrate that Crystal-GFlowNet
is able to sample diverse crystals with low formation energy. | [
"Mila AI4Science",
"Alex Hernandez-Garcia",
"Alexandre Duval",
"Alexandra Volokhova",
"Yoshua Bengio",
"Divya Sharma",
"Pierre Luc Carrier",
"Michał Koziarski",
"Victor Schmidt"
] | 2023-10-07 21:36:55 | http://arxiv.org/abs/2310.04925v1 | http://arxiv.org/pdf/2310.04925v1 | 2310.04925v1 |
Crystal: Introspective Reasoners Reinforced with Self-Feedback | Extensive work has shown that the performance and interpretability of
commonsense reasoning can be improved via knowledge-augmented reasoning
methods, where the knowledge that underpins the reasoning process is explicitly
verbalized and utilized. However, existing implementations, including
"chain-of-thought" and its variants, fall short in capturing the introspective
nature of knowledge required in commonsense reasoning, and in accounting for
the mutual adaptation between the generation and utilization of knowledge. We
propose a novel method to develop an introspective commonsense reasoner,
Crystal. To tackle commonsense problems, it first introspects for knowledge
statements related to the given question, and subsequently makes an informed
prediction that is grounded in the previously introspected knowledge. The
knowledge introspection and knowledge-grounded reasoning modes of the model are
tuned via reinforcement learning to mutually adapt, where the reward derives
from the feedback given by the model itself. Experiments show that Crystal
significantly outperforms both the standard supervised finetuning and
chain-of-thought distilled methods, and enhances the transparency of the
commonsense reasoning process. Our work ultimately validates the feasibility
and potential of reinforcing a neural model with self-feedback. | [
"Jiacheng Liu",
"Ramakanth Pasunuru",
"Hannaneh Hajishirzi",
"Yejin Choi",
"Asli Celikyilmaz"
] | 2023-10-07 21:23:58 | http://arxiv.org/abs/2310.04921v2 | http://arxiv.org/pdf/2310.04921v2 | 2310.04921v2 |
The Conditional Prediction Function: A Novel Technique to Control False Discovery Rate for Complex Models | In modern scientific research, the objective is often to identify which
variables are associated with an outcome among a large class of potential
predictors. This goal can be achieved by selecting variables in a manner that
controls the the false discovery rate (FDR), the proportion of irrelevant
predictors among the selections. Knockoff filtering is a cutting-edge approach
to variable selection that provides FDR control. Existing knockoff statistics
frequently employ linear models to assess relationships between features and
the response, but the linearity assumption is often violated in real world
applications. This may result in poor power to detect truly prognostic
variables. We introduce a knockoff statistic based on the conditional
prediction function (CPF), which can pair with state-of-art machine learning
predictive models, such as deep neural networks. The CPF statistics can capture
the nonlinear relationships between predictors and outcomes while also
accounting for correlation between features. We illustrate the capability of
the CPF statistics to provide superior power over common knockoff statistics
with continuous, categorical, and survival outcomes using repeated simulations.
Knockoff filtering with the CPF statistics is demonstrated using (1) a
residential building dataset to select predictors for the actual sales prices
and (2) the TCGA dataset to select genes that are correlated with disease
staging in lung cancer patients. | [
"Yushu Shi",
"Michael Martens"
] | 2023-10-07 21:16:09 | http://arxiv.org/abs/2310.04919v1 | http://arxiv.org/pdf/2310.04919v1 | 2310.04919v1 |
Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks | The reliable deployment of neural networks in control systems requires
rigorous robustness guarantees. In this paper, we obtain tight robustness
certificates over convex attack sets for min-max representations of ReLU neural
networks by developing a convex reformulation of the nonconvex certification
problem. This is done by "lifting" the problem to an infinite-dimensional
optimization over probability measures, leveraging recent results in
distributionally robust optimization to solve for an optimal discrete
distribution, and proving that solutions of the original nonconvex problem are
generated by the discrete distribution under mild boundedness, nonredundancy,
and Slater conditions. As a consequence, optimal (worst-case) attacks against
the model may be solved for exactly. This contrasts prior state-of-the-art that
either requires expensive branch-and-bound schemes or loose relaxation
techniques. Experiments on robust control and MNIST image classification
examples highlight the benefits of our approach. | [
"Brendon G. Anderson",
"Samuel Pfrommer",
"Somayeh Sojoudi"
] | 2023-10-07 21:07:45 | http://arxiv.org/abs/2310.04916v1 | http://arxiv.org/pdf/2310.04916v1 | 2310.04916v1 |
On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space | Diffusion-based generative models in SE(3)-invariant space have demonstrated
promising performance in molecular conformation generation, but typically
require solving stochastic differential equations (SDEs) with thousands of
update steps. Till now, it remains unclear how to effectively accelerate this
procedure explicitly in SE(3)-invariant space, which greatly hinders its wide
application in the real world. In this paper, we systematically study the
diffusion mechanism in SE(3)-invariant space via the lens of approximate errors
induced by existing methods. Thereby, we develop more precise approximate in
SE(3) in the context of projected differential equations. Theoretical analysis
is further provided as well as empirical proof relating hyper-parameters with
such errors. Altogether, we propose a novel acceleration scheme for generating
molecular conformations in SE(3)-invariant space. Experimentally, our scheme
can generate high-quality conformations with 50x--100x speedup compared to
existing methods. | [
"Zihan Zhou",
"Ruiying Liu",
"Tianshu Yu"
] | 2023-10-07 21:00:14 | http://arxiv.org/abs/2310.04915v1 | http://arxiv.org/pdf/2310.04915v1 | 2310.04915v1 |
A Dual Latent State Learning Approach: Exploiting Regional Network Similarities for QoS Prediction | Individual objects, whether users or services, within a specific region often
exhibit similar network states due to their shared origin from the same city or
autonomous system (AS). Despite this regional network similarity, many existing
techniques overlook its potential, resulting in subpar performance arising from
challenges such as data sparsity and label imbalance. In this paper, we
introduce the regional-based dual latent state learning network(R2SL), a novel
deep learning framework designed to overcome the pitfalls of traditional
individual object-based prediction techniques in Quality of Service (QoS)
prediction. Unlike its predecessors, R2SL captures the nuances of regional
network behavior by deriving two distinct regional network latent states: the
city-network latent state and the AS-network latent state. These states are
constructed utilizing aggregated data from common regions rather than
individual object data. Furthermore, R2SL adopts an enhanced Huber loss
function that adjusts its linear loss component, providing a remedy for
prevalent label imbalance issues. To cap off the prediction process, a
multi-scale perception network is leveraged to interpret the integrated feature
map, a fusion of regional network latent features and other pertinent
information, ultimately accomplishing the QoS prediction. Through rigorous
testing on real-world QoS datasets, R2SL demonstrates superior performance
compared to prevailing state-of-the-art methods. Our R2SL approach ushers in an
innovative avenue for precise QoS predictions by fully harnessing the regional
network similarities inherent in objects. | [
"Ziliang Wang",
"Xiaohong Zhang",
"Meng Yan"
] | 2023-10-07 19:35:07 | http://arxiv.org/abs/2310.05988v1 | http://arxiv.org/pdf/2310.05988v1 | 2310.05988v1 |
Cell Tracking-by-detection using Elliptical Bounding Boxes | Cell detection and tracking are paramount for bio-analysis. Recent approaches
rely on the tracking-by-model evolution paradigm, which usually consists of
training end-to-end deep learning models to detect and track the cells on the
frames with promising results. However, such methods require extensive amounts
of annotated data, which is time-consuming to obtain and often requires
specialized annotators. This work proposes a new approach based on the
classical tracking-by-detection paradigm that alleviates the requirement of
annotated data. More precisely, it approximates the cell shapes as oriented
ellipses and then uses generic-purpose oriented object detectors to identify
the cells in each frame. We then rely on a global data association algorithm
that explores temporal cell similarity using probability distance metrics,
considering that the ellipses relate to two-dimensional Gaussian distributions.
Our results show that our method can achieve detection and tracking results
competitively with state-of-the-art techniques that require considerably more
extensive data annotation. Our code is available at:
https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB. | [
"Lucas N. Kirsten",
"Cláudio R. Jung"
] | 2023-10-07 18:47:17 | http://arxiv.org/abs/2310.04895v2 | http://arxiv.org/pdf/2310.04895v2 | 2310.04895v2 |
GradXKG: A Universal Explain-per-use Temporal Knowledge Graph Explainer | Temporal knowledge graphs (TKGs) have shown promise for reasoning tasks by
incorporating a temporal dimension to represent how facts evolve over time.
However, existing TKG reasoning (TKGR) models lack explainability due to their
black-box nature. Recent work has attempted to address this through customized
model architectures that generate reasoning paths, but these recent approaches
have limited generalizability and provide sparse explanatory output. To enable
interpretability for most TKGR models, we propose GradXKG, a novel two-stage
gradient-based approach for explaining Relational Graph Convolution Network
(RGCN)-based TKGR models. First, a Grad-CAM-inspired RGCN explainer tracks
gradients to quantify each node's contribution across timesteps in an efficient
"explain-per-use" fashion. Second, an integrated gradients explainer
consolidates importance scores for RGCN outputs, extending compatibility across
diverse TKGR architectures based on RGCN. Together, the two explainers
highlight the most critical nodes at each timestep for a given prediction. Our
extensive experiments demonstrated that, by leveraging gradient information,
GradXKG provides insightful explanations grounded in the model's logic in a
timely manner for most RGCN-based TKGR models. This helps address the lack of
interpretability in existing TKGR models and provides a universal explanation
approach applicable across various models. | [
"Chenhan Yuan",
"Hoda Eldardiry"
] | 2023-10-07 18:21:35 | http://arxiv.org/abs/2310.04889v1 | http://arxiv.org/pdf/2310.04889v1 | 2310.04889v1 |
Regret Analysis of Repeated Delegated Choice | We present a study on a repeated delegated choice problem, which is the first
to consider an online learning variant of Kleinberg and Kleinberg, EC'18. In
this model, a principal interacts repeatedly with an agent who possesses an
exogenous set of solutions to search for efficient ones. Each solution can
yield varying utility for both the principal and the agent, and the agent may
propose a solution to maximize its own utility in a selfish manner. To mitigate
this behavior, the principal announces an eligible set which screens out a
certain set of solutions. The principal, however, does not have any information
on the distribution of solutions in advance. Therefore, the principal
dynamically announces various eligible sets to efficiently learn the
distribution. The principal's objective is to minimize cumulative regret
compared to the optimal eligible set in hindsight. We explore two dimensions of
the problem setup, whether the agent behaves myopically or strategizes across
the rounds, and whether the solutions yield deterministic or stochastic
utility. Our analysis mainly characterizes some regimes under which the
principal can recover the sublinear regret, thereby shedding light on the rise
and fall of the repeated delegation procedure in various regimes. | [
"MohammadTaghi Hajiaghayi",
"Mohammad Mahdavi",
"Keivan Rezaei",
"Suho Shin"
] | 2023-10-07 17:54:36 | http://arxiv.org/abs/2310.04884v2 | http://arxiv.org/pdf/2310.04884v2 | 2310.04884v2 |
Question-focused Summarization by Decomposing Articles into Facts and Opinions and Retrieving Entities | This research focuses on utilizing natural language processing techniques to
predict stock price fluctuations, with a specific interest in early detection
of economic, political, social, and technological changes that can be leveraged
for capturing market opportunities. The proposed approach includes the
identification of salient facts and events from news articles, then use these
facts to form tuples with entities which can be used to get summaries of market
changes for particular entity and then finally combining all the summaries to
form a final abstract summary of the whole article. The research aims to
establish relationships between companies and entities through the analysis of
Wikipedia data and articles from the Economist. Large Language Model GPT 3.5 is
used for getting the summaries and also forming the final summary. The ultimate
goal of this research is to develop a comprehensive system that can provide
financial analysts and investors with more informed decision-making tools by
enabling early detection of market trends and events. | [
"Krutika Sarode",
"Shashidhar Reddy Javaji",
"Vishal Kalakonnavar"
] | 2023-10-07 17:37:48 | http://arxiv.org/abs/2310.04880v1 | http://arxiv.org/pdf/2310.04880v1 | 2310.04880v1 |
Hybrid Recommendation System using Graph Neural Network and BERT Embeddings | Recommender systems have emerged as a crucial component of the modern web
ecosystem. The effectiveness and accuracy of such systems are critical for
providing users with personalized recommendations that meet their specific
interests and needs. In this paper, we introduce a novel model that utilizes a
Graph Neural Network (GNN) in conjunction with sentence transformer embeddings
to predict anime recommendations for different users. Our model employs the
task of link prediction to create a recommendation system that considers both
the features of anime and user interactions with different anime. The
hybridization of the GNN and transformer embeddings enables us to capture both
inter-level and intra-level features of anime data.Our model not only
recommends anime to users but also predicts the rating a specific user would
give to an anime. We utilize the GraphSAGE network for model building and
weighted root mean square error (RMSE) to evaluate the performance of the
model. Our approach has the potential to significantly enhance the accuracy and
effectiveness of anime recommendation systems and can be extended to other
domains that require personalized recommendations. | [
"Shashidhar Reddy Javaji",
"Krutika Sarode"
] | 2023-10-07 17:24:41 | http://arxiv.org/abs/2310.04878v1 | http://arxiv.org/pdf/2310.04878v1 | 2310.04878v1 |
Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models | In this paper, we present a groundbreaking paradigm for human-computer
interaction that revolutionizes the traditional notion of an operating system.
Within this innovative framework, user requests issued to the machine are
handled by an interconnected ecosystem of generative AI models that seamlessly
integrate with or even replace traditional software applications. At the core
of this paradigm shift are large generative models, such as language and
diffusion models, which serve as the central interface between users and
computers. This pioneering approach leverages the abilities of advanced
language models, empowering users to engage in natural language conversations
with their computing devices. Users can articulate their intentions, tasks, and
inquiries directly to the system, eliminating the need for explicit commands or
complex navigation. The language model comprehends and interprets the user's
prompts, generating and displaying contextual and meaningful responses that
facilitate seamless and intuitive interactions.
This paradigm shift not only streamlines user interactions but also opens up
new possibilities for personalized experiences. Generative models can adapt to
individual preferences, learning from user input and continuously improving
their understanding and response generation. Furthermore, it enables enhanced
accessibility, as users can interact with the system using speech or text,
accommodating diverse communication preferences.
However, this visionary concept raises significant challenges, including
privacy, security, trustability, and the ethical use of generative models.
Robust safeguards must be in place to protect user data and prevent potential
misuse or manipulation of the language model.
While the full realization of this paradigm is still far from being achieved,
this paper serves as a starting point for envisioning this transformative
potential. | [
"Gabriele Tolomei",
"Cesare Campagnano",
"Fabrizio Silvestri",
"Giovanni Trappolini"
] | 2023-10-07 17:16:34 | http://arxiv.org/abs/2310.04875v1 | http://arxiv.org/pdf/2310.04875v1 | 2310.04875v1 |
Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging | Mitral regurgitation (MR) is a heart valve disease with potentially fatal
consequences that can only be forestalled through timely diagnosis and
treatment. Traditional diagnosis methods are expensive, labor-intensive and
require clinical expertise, posing a barrier to screening for MR. To overcome
this impediment, we propose a new semi-supervised model for MR classification
called CUSSP. CUSSP operates on cardiac imaging slices of the 4-chamber view of
the heart. It uses standard computer vision techniques and contrastive models
to learn from large amounts of unlabeled data, in conjunction with specialized
classifiers to establish the first ever automated MR classification system.
Evaluated on a test set of 179 labeled -- 154 non-MR and 25 MR -- sequences,
CUSSP attains an F1 score of 0.69 and a ROC-AUC score of 0.88, setting the
first benchmark result for this new task. | [
"Ke Xiao",
"Erik Learned-Miller",
"Evangelos Kalogerakis",
"James Priest",
"Madalina Fiterau"
] | 2023-10-07 16:48:24 | http://arxiv.org/abs/2310.04871v1 | http://arxiv.org/pdf/2310.04871v1 | 2310.04871v1 |
Lemur: Integrating Large Language Models in Automated Program Verification | The demonstrated code-understanding capability of LLMs raises the question of
whether they can be used for automated program verification, a task that often
demands high-level abstract reasoning about program properties, which is
challenging for verification tools. We propose a general methodology to combine
the power of LLMs and automated reasoners for automated program verification.
We formally describe this methodology as a set of derivation rules and prove
its soundness. We instantiate the calculus as a sound automated verification
procedure, which led to practical improvements on a set of synthetic and
competition benchmarks. | [
"Haoze Wu",
"Clark Barrett",
"Nina Narodytska"
] | 2023-10-07 16:44:53 | http://arxiv.org/abs/2310.04870v2 | http://arxiv.org/pdf/2310.04870v2 | 2310.04870v2 |
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks | Training neural networks sequentially in time to approximate solution fields
of time-dependent partial differential equations can be beneficial for
preserving causality and other physics properties; however, the
sequential-in-time training is numerically challenging because training errors
quickly accumulate and amplify over time. This work introduces Neural Galerkin
schemes that update randomized sparse subsets of network parameters at each
time step. The randomization avoids overfitting locally in time and so helps
prevent the error from accumulating quickly over the sequential-in-time
training, which is motivated by dropout that addresses a similar issue of
overfitting due to neuron co-adaptation. The sparsity of the update reduces the
computational costs of training without losing expressiveness because many of
the network parameters are redundant locally at each time step. In numerical
experiments with a wide range of evolution equations, the proposed scheme with
randomized sparse updates is up to two orders of magnitude more accurate at a
fixed computational budget and up to two orders of magnitude faster at a fixed
accuracy than schemes with dense updates. | [
"Jules Berman",
"Benjamin Peherstorfer"
] | 2023-10-07 16:27:00 | http://arxiv.org/abs/2310.04867v1 | http://arxiv.org/pdf/2310.04867v1 | 2310.04867v1 |
ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding | Developing text mining approaches to mine aspects from customer reviews has
been well-studied due to its importance in understanding customer needs and
product attributes. In contrast, it remains unclear how to predict the future
emerging aspects of a new product that currently has little review information.
This task, which we named product aspect forecasting, is critical for
recommending new products, but also challenging because of the missing reviews.
Here, we propose ForeSeer, a novel textual mining and product embedding
approach progressively trained on temporal product graphs for this novel
product aspect forecasting task. ForeSeer transfers reviews from similar
products on a large product graph and exploits these reviews to predict aspects
that might emerge in future reviews. A key novelty of our method is to jointly
provide review, product, and aspect embeddings that are both time-sensitive and
less affected by extremely imbalanced aspect frequencies. We evaluated ForeSeer
on a real-world product review system containing 11,536,382 reviews and 11,000
products over 3 years. We observe that ForeSeer substantially outperformed
existing approaches with at least 49.1\% AUPRC improvement under the real
setting where aspect associations are not given. ForeSeer further improves
future link prediction on the product graph and the review aspect association
prediction. Collectively, Foreseer offers a novel framework for review
forecasting by effectively integrating review text, product network, and
temporal information, opening up new avenues for online shopping recommendation
and e-commerce applications. | [
"Zixuan Liu",
"Gaurush Hiranandani",
"Kun Qian",
"Eddie W. Huang",
"Yi Xu",
"Belinda Zeng",
"Karthik Subbian",
"Sheng Wang"
] | 2023-10-07 16:21:04 | http://arxiv.org/abs/2310.04865v1 | http://arxiv.org/pdf/2310.04865v1 | 2310.04865v1 |
Uncovering hidden geometry in Transformers via disentangling position and context | Transformers are widely used to extract complex semantic meanings from input
tokens, yet they usually operate as black-box models. In this paper, we present
a simple yet informative decomposition of hidden states (or embeddings) of
trained transformers into interpretable components. For any layer, embedding
vectors of input sequence samples are represented by a tensor $\boldsymbol{h}
\in \mathbb{R}^{C \times T \times d}$. Given embedding vector
$\boldsymbol{h}_{c,t} \in \mathbb{R}^d$ at sequence position $t \le T$ in a
sequence (or context) $c \le C$, extracting the mean effects yields the
decomposition \[ \boldsymbol{h}_{c,t} = \boldsymbol{\mu} + \mathbf{pos}_t +
\mathbf{ctx}_c + \mathbf{resid}_{c,t} \] where $\boldsymbol{\mu}$ is the global
mean vector, $\mathbf{pos}_t$ and $\mathbf{ctx}_c$ are the mean vectors across
contexts and across positions respectively, and $\mathbf{resid}_{c,t}$ is the
residual vector. For popular transformer architectures and diverse text
datasets, empirically we find pervasive mathematical structure: (1)
$(\mathbf{pos}_t)_{t}$ forms a low-dimensional, continuous, and often spiral
shape across layers, (2) $(\mathbf{ctx}_c)_c$ shows clear cluster structure
that falls into context topics, and (3) $(\mathbf{pos}_t)_{t}$ and
$(\mathbf{ctx}_c)_c$ are mutually incoherent -- namely $\mathbf{pos}_t$ is
almost orthogonal to $\mathbf{ctx}_c$ -- which is canonical in compressed
sensing and dictionary learning. This decomposition offers structural insights
about input formats in in-context learning (especially for induction heads) and
in arithmetic tasks. | [
"Jiajun Song",
"Yiqiao Zhong"
] | 2023-10-07 15:50:26 | http://arxiv.org/abs/2310.04861v1 | http://arxiv.org/pdf/2310.04861v1 | 2310.04861v1 |
Universal Graph Random Features | We propose a novel random walk-based algorithm for unbiased estimation of
arbitrary functions of a weighted adjacency matrix, coined universal graph
random features (u-GRFs). This includes many of the most popular examples of
kernels defined on the nodes of a graph. Our algorithm enjoys subquadratic time
complexity with respect to the number of nodes, overcoming the notoriously
prohibitive cubic scaling of exact graph kernel evaluation. It can also be
trivially distributed across machines, permitting learning on much larger
networks. At the heart of the algorithm is a modulation function which
upweights or downweights the contribution from different random walks depending
on their lengths. We show that by parameterising it with a neural network we
can obtain u-GRFs that give higher-quality kernel estimates or perform
efficient, scalable kernel learning. We provide robust theoretical analysis and
support our findings with experiments including pointwise estimation of fixed
graph kernels, solving non-homogeneous graph ordinary differential equations,
node clustering and kernel regression on triangular meshes. | [
"Isaac Reid",
"Krzysztof Choromanski",
"Eli Berger",
"Adrian Weller"
] | 2023-10-07 15:47:31 | http://arxiv.org/abs/2310.04859v2 | http://arxiv.org/pdf/2310.04859v2 | 2310.04859v2 |
LIPEx -- Locally Interpretable Probabilistic Explanations -- To Look Beyond The True Class | In this work, we instantiate a novel perturbation-based multi-class
explanation framework, LIPEx (Locally Interpretable Probabilistic Explanation).
We demonstrate that LIPEx not only locally replicates the probability
distributions output by the widely used complex classification models but also
provides insight into how every feature deemed to be important affects the
prediction probability for each of the possible classes. We achieve this by
defining the explanation as a matrix obtained via regression with respect to
the Hellinger distance in the space of probability distributions. Ablation
tests on text and image data, show that LIPEx-guided removal of important
features from the data causes more change in predictions for the underlying
model than similar tests on other saliency-based or feature importance-based
XAI methods. It is also shown that compared to LIME, LIPEx is much more data
efficient in terms of the number of perturbations needed for reliable
evaluation of the explanation. | [
"Hongbo Zhu",
"Angelo Cangelosi",
"Procheta Sen",
"Anirbit Mukherjee"
] | 2023-10-07 15:31:38 | http://arxiv.org/abs/2310.04856v1 | http://arxiv.org/pdf/2310.04856v1 | 2310.04856v1 |
Epsilon non-Greedy: A Bandit Approach for Unbiased Recommendation via Uniform Data | Often, recommendation systems employ continuous training, leading to a
self-feedback loop bias in which the system becomes biased toward its previous
recommendations. Recent studies have attempted to mitigate this bias by
collecting small amounts of unbiased data. While these studies have
successfully developed less biased models, they ignore the crucial fact that
the recommendations generated by the model serve as the training data for
subsequent training sessions. To address this issue, we propose a framework
that learns an unbiased estimator using a small amount of uniformly collected
data and focuses on generating improved training data for subsequent training
iterations. To accomplish this, we view recommendation as a contextual
multi-arm bandit problem and emphasize on exploring items that the model has a
limited understanding of. We introduce a new offline sequential training schema
that simulates real-world continuous training scenarios in recommendation
systems, offering a more appropriate framework for studying self-feedback bias.
We demonstrate the superiority of our model over state-of-the-art debiasing
methods by conducting extensive experiments using the proposed training schema. | [
"S. M. F. Sani",
"Seyed Abbas Hosseini",
"Hamid R. Rabiee"
] | 2023-10-07 15:31:15 | http://arxiv.org/abs/2310.04855v1 | http://arxiv.org/pdf/2310.04855v1 | 2310.04855v1 |
Repelling Random Walks | We present a novel quasi-Monte Carlo mechanism to improve graph-based
sampling, coined repelling random walks. By inducing correlations between the
trajectories of an interacting ensemble such that their marginal transition
probabilities are unmodified, we are able to explore the graph more
efficiently, improving the concentration of statistical estimators whilst
leaving them unbiased. The mechanism has a trivial drop-in implementation. We
showcase the effectiveness of repelling random walks in a range of settings
including estimation of graph kernels, the PageRank vector and graphlet
concentrations. We provide detailed experimental evaluation and robust
theoretical guarantees. To our knowledge, repelling random walks constitute the
first rigorously studied quasi-Monte Carlo scheme correlating the directions of
walkers on a graph, inviting new research in this exciting nascent domain. | [
"Isaac Reid",
"Eli Berger",
"Krzysztof Choromanski",
"Adrian Weller"
] | 2023-10-07 15:30:23 | http://arxiv.org/abs/2310.04854v1 | http://arxiv.org/pdf/2310.04854v1 | 2310.04854v1 |
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations | The discovery of governing differential equations from data is an open
frontier in machine learning. The sparse identification of nonlinear dynamics
(SINDy) \citep{brunton_discovering_2016} framework enables data-driven
discovery of interpretable models in the form of sparse, deterministic
governing laws. Recent works have sought to adapt this approach to the
stochastic setting, though these adaptations are severely hampered by the curse
of dimensionality. On the other hand, Bayesian-inspired deep learning methods
have achieved widespread success in high-dimensional probabilistic modeling via
computationally efficient approximate inference techniques, suggesting the use
of these techniques for efficient stochastic equation discovery. Here, we
introduce HyperSINDy, a framework for modeling stochastic dynamics via a deep
generative model of sparse governing equations whose parametric form is
discovered from data. HyperSINDy employs a variational encoder to approximate
the distribution of observed states and derivatives. A hypernetwork
\citep{ha_hypernetworks_2016} transforms samples from this distribution into
the coefficients of a differential equation whose sparse form is learned
simultaneously using a trainable binary mask \citep{louizos_learning_2018}.
Once trained, HyperSINDy generates stochastic dynamics via a differential
equation whose coefficients are driven by a Gaussian white noise. In
experiments, HyperSINDy accurately recovers ground truth stochastic governing
equations, with learned stochasticity scaling to match that of the data.
Finally, HyperSINDy provides uncertainty quantification that scales to
high-dimensional systems. Taken together, HyperSINDy offers a promising
framework for model discovery and uncertainty quantification in real-world
systems, integrating sparse equation discovery methods with advances in
statistical machine learning and deep generative modeling. | [
"Mozes Jacobs",
"Bingni W. Brunton",
"Steven L. Brunton",
"J. Nathan Kutz",
"Ryan V. Raut"
] | 2023-10-07 14:41:59 | http://arxiv.org/abs/2310.04832v1 | http://arxiv.org/pdf/2310.04832v1 | 2310.04832v1 |
Extract-Transform-Load for Video Streams | Social media, self-driving cars, and traffic cameras produce video streams at
large scales and cheap cost. However, storing and querying video at such scales
is prohibitively expensive. We propose to treat large-scale video analytics as
a data warehousing problem: Video is a format that is easy to produce but needs
to be transformed into an application-specific format that is easy to query.
Analogously, we define the problem of Video Extract-Transform-Load (V-ETL).
V-ETL systems need to reduce the cost of running a user-defined V-ETL job while
also giving throughput guarantees to keep up with the rate at which data is
produced. We find that no current system sufficiently fulfills both needs and
therefore propose Skyscraper, a system tailored to V-ETL. Skyscraper can
execute arbitrary video ingestion pipelines and adaptively tunes them to reduce
cost at minimal or no quality degradation, e.g., by adjusting sampling rates
and resolutions to the ingested content. Skyscraper can hereby be provisioned
with cheap on-premises compute and uses a combination of buffering and cloud
bursting to deal with peaks in workload caused by expensive processing
configurations. In our experiments, we find that Skyscraper significantly
reduces the cost of V-ETL ingestion compared to adaptions of current SOTA
systems, while at the same time giving robustness guarantees that these systems
are lacking. | [
"Ferdinand Kossmann",
"Ziniu Wu",
"Eugenie Lai",
"Nesime Tatbul",
"Lei Cao",
"Tim Kraska",
"Samuel Madden"
] | 2023-10-07 14:38:43 | http://arxiv.org/abs/2310.04830v1 | http://arxiv.org/pdf/2310.04830v1 | 2310.04830v1 |
Rethink Baseline of Integrated Gradients from the Perspective of Shapley Value | Numerous approaches have attempted to interpret deep neural networks (DNNs)
by attributing the prediction of DNN to its input features. One of the
well-studied attribution methods is Integrated Gradients (IG). Specifically,
the choice of baselines for IG is a critical consideration for generating
meaningful and unbiased explanations for model predictions in different
scenarios. However, current practice of exploiting a single baseline fails to
fulfill this ambition, thus demanding multiple baselines. Fortunately, the
inherent connection between IG and Aumann-Shapley Value forms a unique
perspective to rethink the design of baselines. Under certain hypothesis, we
theoretically analyse that a set of baseline aligns with the coalitions in
Shapley Value. Thus, we propose a novel baseline construction method called
Shapley Integrated Gradients (SIG) that searches for a set of baselines by
proportional sampling to partly simulate the computation path of Shapley Value.
Simulations on GridWorld show that SIG approximates the proportion of Shapley
Values. Furthermore, experiments conducted on various image tasks demonstrate
that compared to IG using other baseline methods, SIG exhibits an improved
estimation of feature's contribution, offers more consistent explanations
across diverse applications, and is generic to distinct data types or instances
with insignificant computational overhead. | [
"Shuyang Liu",
"Zixuan Chen",
"Ge Shi",
"Ji Wang",
"Changjie Fan",
"Yu Xiong",
"Runze Wu Yujing Hu",
"Ze Ji",
"Yang Gao"
] | 2023-10-07 14:19:07 | http://arxiv.org/abs/2310.04821v2 | http://arxiv.org/pdf/2310.04821v2 | 2310.04821v2 |
Hacking Generative Models with Differentiable Network Bending | In this work, we propose a method to 'hack' generative models, pushing their
outputs away from the original training distribution towards a new objective.
We inject a small-scale trainable module between the intermediate layers of the
model and train it for a low number of iterations, keeping the rest of the
network frozen. The resulting output images display an uncanny quality, given
by the tension between the original and new objectives that can be exploited
for artistic purposes. | [
"Giacomo Aldegheri",
"Alina Rogalska",
"Ahmed Youssef",
"Eugenia Iofinova"
] | 2023-10-07 14:13:14 | http://arxiv.org/abs/2310.04816v1 | http://arxiv.org/pdf/2310.04816v1 | 2310.04816v1 |
Critique Ability of Large Language Models | Critical thinking is essential for rational decision-making and
problem-solving. This skill hinges on the ability to provide precise and
reasoned critiques and is a hallmark of human intelligence. In the era of large
language models (LLMs), this study explores the ability of LLMs to deliver
accurate critiques across various tasks. We are interested in this topic as a
capable critic model could not only serve as a reliable evaluator, but also as
a source of supervised signals for model tuning. Particularly, if a model can
self-critique, it has the potential for autonomous self-improvement. To examine
this, we introduce a unified evaluation framework for assessing the critique
abilities of LLMs. We develop a benchmark called CriticBench, which comprises
3K high-quality natural language queries and corresponding model responses; and
annotate the correctness of these responses. The benchmark cover tasks such as
math problem-solving, code completion, and question answering. We evaluate
multiple LLMs on the collected dataset and our analysis reveals several
noteworthy insights: (1) Critique is generally challenging for most LLMs, and
this capability often emerges only when models are sufficiently large. (2) In
particular, self-critique is especially difficult. Even top-performing LLMs
struggle to achieve satisfactory performance. (3) Models tend to have lower
critique accuracy on problems where they are most uncertain. To this end, we
introduce a simple yet effective baseline named self-check, which leverages
self-critique to improve task performance for various models. We hope this
study serves as an initial exploration into understanding the critique
abilities of LLMs, and aims to inform future research, including the
development of more proficient critic models and the application of critiques
across diverse tasks. | [
"Liangchen Luo",
"Zi Lin",
"Yinxiao Liu",
"Lei Shu",
"Yun Zhu",
"Jingbo Shang",
"Lei Meng"
] | 2023-10-07 14:12:15 | http://arxiv.org/abs/2310.04815v1 | http://arxiv.org/pdf/2310.04815v1 | 2310.04815v1 |
Applications of Littlestone dimension to query learning and to compression | In this paper we give several applications of Littlestone dimension. The
first is to the model of \cite{angluin2017power}, where we extend their results
for learning by equivalence queries with random counterexamples. Second, we
extend that model to infinite concept classes with an additional source of
randomness. Third, we give improved results on the relationship of Littlestone
dimension to classes with extended $d$-compression schemes, proving a strong
version of a conjecture of \cite{floyd1995sample} for Littlestone dimension. | [
"Hunter Chase",
"James Freitag",
"Lev Reyzin"
] | 2023-10-07 14:04:18 | http://arxiv.org/abs/2310.04812v1 | http://arxiv.org/pdf/2310.04812v1 | 2310.04812v1 |
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning | Learning Nash equilibrium (NE) in complex zero-sum games with multi-agent
reinforcement learning (MARL) can be extremely computationally expensive.
Curriculum learning is an effective way to accelerate learning, but an
under-explored dimension for generating a curriculum is the difficulty-to-learn
of the subgames -- games induced by starting from a specific state. In this
work, we present a novel subgame curriculum learning framework for zero-sum
games. It adopts an adaptive initial state distribution by resetting agents to
some previously visited states where they can quickly learn to improve
performance. Building upon this framework, we derive a subgame selection metric
that approximates the squared distance to NE values and further adopt a
particle-based state sampler for subgame generation. Integrating these
techniques leads to our new algorithm, Subgame Automatic Curriculum Learning
(SACL), which is a realization of the subgame curriculum learning framework.
SACL can be combined with any MARL algorithm such as MAPPO. Experiments in the
particle-world environment and Google Research Football environment show SACL
produces much stronger policies than baselines. In the challenging
hide-and-seek quadrant environment, SACL produces all four emergent stages and
uses only half the samples of MAPPO with self-play. The project website is at
https://sites.google.com/view/sacl-rl. | [
"Jiayu Chen",
"Zelai Xu",
"Yunfei Li",
"Chao Yu",
"Jiaming Song",
"Huazhong Yang",
"Fei Fang",
"Yu Wang",
"Yi Wu"
] | 2023-10-07 13:09:37 | http://arxiv.org/abs/2310.04796v1 | http://arxiv.org/pdf/2310.04796v1 | 2310.04796v1 |
Conditional Diffusion Model for Target Speaker Extraction | We propose DiffSpEx, a generative target speaker extraction method based on
score-based generative modelling through stochastic differential equations.
DiffSpEx deploys a continuous-time stochastic diffusion process in the complex
short-time Fourier transform domain, starting from the target speaker source
and converging to a Gaussian distribution centred on the mixture of sources.
For the reverse-time process, a parametrised score function is conditioned on a
target speaker embedding to extract the target speaker from the mixture of
sources. We utilise ECAPA-TDNN target speaker embeddings and condition the
score function alternately on the SDE time embedding and the target speaker
embedding. The potential of DiffSpEx is demonstrated with the WSJ0-2mix
dataset, achieving an SI-SDR of 12.9 dB and a NISQA score of 3.56. Moreover, we
show that fine-tuning a pre-trained DiffSpEx model to a specific speaker
further improves performance, enabling personalisation in target speaker
extraction. | [
"Theodor Nguyen",
"Guangzhi Sun",
"Xianrui Zheng",
"Chao Zhang",
"Philip C Woodland"
] | 2023-10-07 12:48:54 | http://arxiv.org/abs/2310.04791v1 | http://arxiv.org/pdf/2310.04791v1 | 2310.04791v1 |
HNS: An Efficient Hermite Neural Solver for Solving Time-Fractional Partial Differential Equations | Neural network solvers represent an innovative and promising approach for
tackling time-fractional partial differential equations by utilizing deep
learning techniques. L1 interpolation approximation serves as the standard
method for addressing time-fractional derivatives within neural network
solvers. However, we have discovered that neural network solvers based on L1
interpolation approximation are unable to fully exploit the benefits of neural
networks, and the accuracy of these models is constrained to interpolation
errors. In this paper, we present the high-precision Hermite Neural Solver
(HNS) for solving time-fractional partial differential equations. Specifically,
we first construct a high-order explicit approximation scheme for fractional
derivatives using Hermite interpolation techniques, and rigorously analyze its
approximation accuracy. Afterward, taking into account the infinitely
differentiable properties of deep neural networks, we integrate the high-order
Hermite interpolation explicit approximation scheme with deep neural networks
to propose the HNS. The experimental results show that HNS achieves higher
accuracy than methods based on the L1 scheme for both forward and inverse
problems, as well as in high-dimensional scenarios. This indicates that HNS has
significantly improved accuracy and flexibility compared to existing L1-based
methods, and has overcome the limitations of explicit finite difference
approximation methods that are often constrained to function value
interpolation. As a result, the HNS is not a simple combination of numerical
computing methods and neural networks, but rather achieves a complementary and
mutually reinforcing advantages of both approaches. The data and code can be
found at \url{https://github.com/hsbhc/HNS}. | [
"Jie Hou",
"Zhiying Ma",
"Shihui Ying",
"Ying Li"
] | 2023-10-07 12:44:47 | http://arxiv.org/abs/2310.04789v1 | http://arxiv.org/pdf/2310.04789v1 | 2310.04789v1 |
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