categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.11619 | null | null | http://arxiv.org/pdf/2405.11619v1 | 2024-05-19T17:18:27Z | 2024-05-19T17:18:27Z | Novel Interpretable and Robust Web-based AI Platform for Phishing Email
Detection | Phishing emails continue to pose a significant threat, causing financial losses and security breaches. This study addresses limitations in existing research, such as reliance on proprietary datasets and lack of real-world application, by proposing a high-performance machine learning model for email classification. Utilizing a comprehensive and largest available public dataset, the model achieves a f1 score of 0.99 and is designed for deployment within relevant applications. Additionally, Explainable AI (XAI) is integrated to enhance user trust. This research offers a practical and highly accurate solution, contributing to the fight against phishing by empowering users with a real-time web-based application for phishing email detection. | [
"['Abdulla Al-Subaiey' 'Mohammed Al-Thani' 'Naser Abdullah Alam'\n 'Kaniz Fatema Antora' 'Amith Khandakar' 'SM Ashfaq Uz Zaman']"
] |
null | null | 2405.11622 | null | null | http://arxiv.org/pdf/2405.11622v2 | 2024-07-05T18:14:48Z | 2024-05-19T17:23:04Z | Continuous Predictive Modeling of Clinical Notes and ICD Codes in
Patient Health Records | Electronic Health Records (EHR) serve as a valuable source of patient information, offering insights into medical histories, treatments, and outcomes. Previous research has developed systems for detecting applicable ICD codes that should be assigned while writing a given EHR document, mainly focusing on discharge summaries written at the end of a hospital stay. In this work, we investigate the potential of predicting these codes for the whole patient stay at different time points during their stay, even before they are officially assigned by clinicians. The development of methods to predict diagnoses and treatments earlier in advance could open opportunities for predictive medicine, such as identifying disease risks sooner, suggesting treatments, and optimizing resource allocation. Our experiments show that predictions regarding final ICD codes can be made already two days after admission and we propose a custom model that improves performance on this early prediction task. | [
"['Mireia Hernandez Caralt' 'Clarence Boon Liang Ng' 'Marek Rei']"
] |
null | null | 2405.11633 | null | null | http://arxiv.org/pdf/2405.11633v1 | 2024-05-19T17:49:33Z | 2024-05-19T17:49:33Z | Geometry-Aware Instrumental Variable Regression | Instrumental variable (IV) regression can be approached through its formulation in terms of conditional moment restrictions (CMR). Building on variants of the generalized method of moments, most CMR estimators are implicitly based on approximating the population data distribution via reweightings of the empirical sample. While for large sample sizes, in the independent identically distributed (IID) setting, reweightings can provide sufficient flexibility, they might fail to capture the relevant information in presence of corrupted data or data prone to adversarial attacks. To address these shortcomings, we propose the Sinkhorn Method of Moments, an optimal transport-based IV estimator that takes into account the geometry of the data manifold through data-derivative information. We provide a simple plug-and-play implementation of our method that performs on par with related estimators in standard settings but improves robustness against data corruption and adversarial attacks. | [
"['Heiner Kremer' 'Bernhard Schölkopf']"
] |
null | null | 2405.11643 | null | null | http://arxiv.org/pdf/2405.11643v1 | 2024-05-19T18:42:36Z | 2024-05-19T18:42:36Z | Morphological Prototyping for Unsupervised Slide Representation Learning
in Computational Pathology | Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However, the slide representations resulting from this approach are highly tailored to specific clinical tasks, which limits their expressivity and generalization, particularly in scenarios with limited data. Instead, we hypothesize that morphological redundancy in tissue can be leveraged to build a task-agnostic slide representation in an unsupervised fashion. To this end, we introduce PANTHER, a prototype-based approach rooted in the Gaussian mixture model that summarizes the set of WSI patches into a much smaller set of morphological prototypes. Specifically, each patch is assumed to have been generated from a mixture distribution, where each mixture component represents a morphological exemplar. Utilizing the estimated mixture parameters, we then construct a compact slide representation that can be readily used for a wide range of downstream tasks. By performing an extensive evaluation of PANTHER on subtyping and survival tasks using 13 datasets, we show that 1) PANTHER outperforms or is on par with supervised MIL baselines and 2) the analysis of morphological prototypes brings new qualitative and quantitative insights into model interpretability. | [
"['Andrew H. Song' 'Richard J. Chen' 'Tong Ding' 'Drew F. K. Williamson'\n 'Guillaume Jaume' 'Faisal Mahmood']"
] |
null | null | 2405.11647 | null | null | http://arxiv.org/pdf/2405.11647v2 | 2024-05-21T02:01:42Z | 2024-05-19T18:57:25Z | Hummer: Towards Limited Competitive Preference Dataset | Preference datasets are essential for incorporating human preferences into pre-trained language models, playing a key role in the success of Reinforcement Learning from Human Feedback. However, these datasets often demonstrate conflicting alignment objectives, leading to increased vulnerability to jailbreak attacks and challenges in adapting downstream tasks to prioritize specific alignment objectives without negatively impacting others. In this work, we introduce a novel statistical metric, Alignment Dimension Conflict, to quantify the degree of conflict within preference datasets. We then present texttt{Hummer} and its fine-grained variant, texttt{Hummer-F}, as innovative pairwise preference datasets with reduced-conflict alignment objectives. texttt{Hummer} is built based on UltraFeedback and is enhanced by AI feedback from GPT-4, marking as the first preference dataset aimed at reducing the competition between alignment objectives. Furthermore, we develop reward models, HummerRM and HummerRM-F, which employ a hybrid sampling approach to balance diverse alignment objectives effectively. This sampling method positions HummerRM as an ideal model for domain-specific further fine-tuning and reducing vulnerabilities to attacks. | [
"['Li Jiang' 'Yusen Wu' 'Junwu Xiong' 'Jingqing Ruan' 'Yichuan Ding'\n 'Qingpei Guo' 'Zujie Wen' 'Jun Zhou' 'Xiaotie Deng']"
] |
null | null | 2405.11651 | null | null | http://arxiv.org/pdf/2405.11651v1 | 2024-05-19T19:32:12Z | 2024-05-19T19:32:12Z | Movie Revenue Prediction using Machine Learning Models | In the contemporary film industry, accurately predicting a movie's earnings is paramount for maximizing profitability. This project aims to develop a machine learning model for predicting movie earnings based on input features like the movie name, the MPAA rating of the movie, the genre of the movie, the year of release of the movie, the IMDb Rating, the votes by the watchers, the director, the writer and the leading cast, the country of production of the movie, the budget of the movie, the production company and the runtime of the movie. Through a structured methodology involving data collection, preprocessing, analysis, model selection, evaluation, and improvement, a robust predictive model is constructed. Linear Regression, Decision Trees, Random Forest Regression, Bagging, XGBoosting and Gradient Boosting have been trained and tested. Model improvement strategies include hyperparameter tuning and cross-validation. The resulting model offers promising accuracy and generalization, facilitating informed decision-making in the film industry to maximize profits. | [
"['Vikranth Udandarao' 'Pratyush Gupta']"
] |
null | null | 2405.11657 | null | null | http://arxiv.org/pdf/2405.11657v1 | 2024-05-19T20:06:38Z | 2024-05-19T20:06:38Z | On the Expressivity of Recurrent Neural Cascades with Identity | Recurrent Neural Cascades (RNC) are the class of recurrent neural networks with no cyclic dependencies among recurrent neurons. Their subclass RNC+ with positive recurrent weights has been shown to be closely connected to the star-free regular languages, which are the expressivity of many well-established temporal logics. The existing expressivity results show that the regular languages captured by RNC+ are the star-free ones, and they leave open the possibility that RNC+ may capture languages beyond regular. We exclude this possibility for languages that include an identity element, i.e., an input that can occur an arbitrary number of times without affecting the output. Namely, in the presence of an identity element, we show that the languages captured by RNC+ are exactly the star-free regular languages. Identity elements are ubiquitous in temporal patterns, and hence our results apply to a large number of applications. The implications of our results go beyond expressivity. At their core, we establish a close structural correspondence between RNC+ and semiautomata cascades, showing that every neuron can be equivalently captured by a three-state semiautomaton. A notable consequence of this result is that RNC+ are no more succinct than cascades of three-state semiautomata. | [
"['Nadezda A. Knorozova' 'Alessandro Ronca']"
] |
null | null | 2405.11659 | null | null | http://arxiv.org/pdf/2405.11659v1 | 2024-05-19T20:11:30Z | 2024-05-19T20:11:30Z | Auto-Platoon : Freight by example | The work introduces a bio-inspired leader-follower system based on an innovative mechanism proposed as software latching that aims to improve collaboration and coordination between a leader agent and the associated autonomous followers. The system utilizes software latching to establish real-time communication and synchronization between the leader and followers. A layered architecture is proposed, encompassing perception, decision-making, and control modules. Challenges such as uncertainty, dynamic environments, and communication latency are addressed using Deep learning and real-time data processing pipelines. The follower robot is equipped with sensors and communication modules that enable it to track and trace the agent of interest or avoid obstacles. The followers track the leader and dynamically avoid obstacles while maintaining a safe distance from it. The experimental results demonstrate the proposed system's effectiveness, making it a promising solution for achieving success in tasks that demand multi-robot systems capable of navigating complex dynamic environments. | [
"['Tharun V. Puthanveettil' 'Abhijay Singh' 'Yashveer Jain' 'Vinay Bukka'\n 'Sameer Arjun S']"
] |
null | null | 2405.11667 | null | null | http://arxiv.org/pdf/2405.11667v1 | 2024-05-19T20:20:03Z | 2024-05-19T20:20:03Z | The Limits and Potentials of Local SGD for Distributed Heterogeneous
Learning with Intermittent Communication | Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this success, theoretically proving the dominance of local SGD in settings with reasonable data heterogeneity has been difficult, creating a significant gap between theory and practice. In this paper, we provide new lower bounds for local SGD under existing first-order data heterogeneity assumptions, showing that these assumptions are insufficient to prove the effectiveness of local update steps. Furthermore, under these same assumptions, we demonstrate the min-max optimality of accelerated mini-batch SGD, which fully resolves our understanding of distributed optimization for several problem classes. Our results emphasize the need for better models of data heterogeneity to understand the effectiveness of local SGD in practice. Towards this end, we consider higher-order smoothness and heterogeneity assumptions, providing new upper bounds that imply the dominance of local SGD over mini-batch SGD when data heterogeneity is low. | [
"['Kumar Kshitij Patel' 'Margalit Glasgow' 'Ali Zindari' 'Lingxiao Wang'\n 'Sebastian U. Stich' 'Ziheng Cheng' 'Nirmit Joshi' 'Nathan Srebro']"
] |
null | null | 2405.11669 | null | null | http://arxiv.org/pdf/2405.11669v1 | 2024-05-19T20:33:21Z | 2024-05-19T20:33:21Z | Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning | Reinforcement Learning (RL) for control has become increasingly popular due to its ability to learn rich feedback policies that take into account uncertainty and complex representations of the environment. When considering safety constraints, constrained optimization approaches, where agents are penalized for constraint violations, are commonly used. In such methods, if agents are initialized in, or must visit, states where constraint violation might be inevitable, it is unclear how much they should be penalized. We address this challenge by formulating a constraint on the counterfactual harm of the learned policy compared to a default, safe policy. In a philosophical sense this formulation only penalizes the learner for constraint violations that it caused; in a practical sense it maintains feasibility of the optimal control problem. We present simulation studies on a rover with uncertain road friction and a tractor-trailer parking environment that demonstrate our constraint formulation enables agents to learn safer policies than contemporary constrained RL methods. | [
"['Sean Vaskov' 'Wilko Schwarting' 'Chris L. Baker']"
] |
null | null | 2405.11672 | null | null | http://arxiv.org/pdf/2405.11672v2 | 2024-05-21T01:54:29Z | 2024-05-19T20:39:46Z | Interpretable Machine Learning Enhances Disease Prognosis: Applications
on COVID-19 and Onward | In response to the COVID-19 pandemic, the integration of interpretable machine learning techniques has garnered significant attention, offering transparent and understandable insights crucial for informed clinical decision making. This literature review delves into the applications of interpretable machine learning in predicting the prognosis of respiratory diseases, particularly focusing on COVID-19 and its implications for future research and clinical practice. We reviewed various machine learning models that are not only capable of incorporating existing clinical domain knowledge but also have the learning capability to explore new information from the data. These models and experiences not only aid in managing the current crisis but also hold promise for addressing future disease outbreaks. By harnessing interpretable machine learning, healthcare systems can enhance their preparedness and response capabilities, thereby improving patient outcomes and mitigating the impact of respiratory diseases in the years to come. | [
"['Jinzhi Shen' 'Ke Ma']"
] |
null | null | 2405.11677 | null | null | http://arxiv.org/abs/2405.11677v1 | 2024-05-19T21:35:12Z | 2024-05-19T21:35:12Z | Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging
Geometries | Accurate 6-DoF pose estimation of surgical instruments during minimally invasive surgeries can substantially improve treatment strategies and eventual surgical outcome. Existing deep learning methods have achieved accurate results, but they require custom approaches for each object and laborious setup and training environments often stretching to extensive simulations, whilst lacking real-time computation. We propose a general-purpose approach of data acquisition for 6-DoF pose estimation tasks in X-ray systems, a novel and general purpose YOLOv5-6D pose architecture for accurate and fast object pose estimation and a complete method for surgical screw pose estimation under acquisition geometry consideration from a monocular cone-beam X-ray image. The proposed YOLOv5-6D pose model achieves competitive results on public benchmarks whilst being considerably faster at 42 FPS on GPU. In addition, the method generalizes across varying X-ray acquisition geometry and semantic image complexity to enable accurate pose estimation over different domains. Finally, the proposed approach is tested for bone-screw pose estimation for computer-aided guidance during spine surgeries. The model achieves a 92.41% by the 0.1 ADD-S metric, demonstrating a promising approach for enhancing surgical precision and patient outcomes. The code for YOLOv5-6D is publicly available at https://github.com/cviviers/YOLOv5-6D-Pose | [
"['Christiaan G. A. Viviers' 'Lena Filatova' 'Maurice Termeer'\n 'Peter H. N. de With' 'Fons van der Sommen']"
] |
null | null | 2405.11683 | null | null | http://arxiv.org/pdf/2405.11683v1 | 2024-05-19T21:53:36Z | 2024-05-19T21:53:36Z | Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count
Data via Data Augmentation | Gaussian process factor analysis (GPFA) is a latent variable modeling technique commonly used to identify smooth, low-dimensional latent trajectories underlying high-dimensional neural recordings. Specifically, researchers model spiking rates as Gaussian observations, resulting in tractable inference. Recently, GPFA has been extended to model spike count data. However, due to the non-conjugacy of the likelihood, the inference becomes intractable. Prior works rely on either black-box inference techniques, numerical integration or polynomial approximations of the likelihood to handle intractability. To overcome this challenge, we propose a conditionally-conjugate Gaussian process factor analysis (ccGPFA) resulting in both analytically and computationally tractable inference for modeling neural activity from spike count data. In particular, we develop a novel data augmentation based method that renders the model conditionally conjugate. Consequently, our model enjoys the advantage of simple closed-form updates using a variational EM algorithm. Furthermore, due to its conditional conjugacy, we show our model can be readily scaled using sparse Gaussian Processes and accelerated inference via natural gradients. To validate our method, we empirically demonstrate its efficacy through experiments. | [
"['Yididiya Y. Nadew' 'Xuhui Fan' 'Christopher J. Quinn']"
] |
null | null | 2405.11684 | null | null | http://arxiv.org/pdf/2405.11684v2 | 2024-06-06T15:41:38Z | 2024-05-19T22:04:11Z | Learning Regularities from Data using Spiking Functions: A Theory | Deep neural networks trained in an end-to-end manner are proven to be efficient in a wide range of machine learning tasks. However, there is one drawback of end-to-end learning: The learned features and information are implicitly represented in neural network parameters, which cannot be used as regularities, concepts or knowledge to explicitly represent the data probability distribution. To resolve this issue, we propose in this paper a new machine learning theory, which defines in mathematics what are regularities. Briefly, regularities are concise representations of the non-random features, or 'non-randomness' in the data probability distribution. Combining this with information theory, we claim that regularities can also be regarded as a small amount of information encoding a large amount of information. Our theory is based on spiking functions. That is, if a function can react to, or spike on specific data samples more frequently than random noise inputs, we say that such a function discovers non-randomness from the data distribution. Also, we say that the discovered non-randomness is encoded into regularities if the function is simple enough. Our theory also discusses applying multiple spiking functions to the same data distribution. In this process, we claim that the 'best' regularities, or the optimal spiking functions, are those who can capture the largest amount of information from the data distribution, and then encode the captured information in the most concise way. Theorems and hypotheses are provided to describe in mathematics what are 'best' regularities and optimal spiking functions. Finally, we propose a machine learning approach, which can potentially obtain the optimal spiking functions regarding the given dataset in practice. | [
"['Canlin Zhang' 'Xiuwen Liu']"
] |
null | null | 2405.11696 | null | null | http://arxiv.org/pdf/2405.11696v1 | 2024-05-19T23:04:09Z | 2024-05-19T23:04:09Z | Approximation and Gradient Descent Training with Neural Networks | It is well understood that neural networks with carefully hand-picked weights provide powerful function approximation and that they can be successfully trained in over-parametrized regimes. Since over-parametrization ensures zero training error, these two theories are not immediately compatible. Recent work uses the smoothness that is required for approximation results to extend a neural tangent kernel (NTK) optimization argument to an under-parametrized regime and show direct approximation bounds for networks trained by gradient flow. Since gradient flow is only an idealization of a practical method, this paper establishes analogous results for networks trained by gradient descent. | [
"['G. Welper']"
] |
null | null | 2405.11703 | null | null | http://arxiv.org/pdf/2405.11703v1 | 2024-05-20T00:01:36Z | 2024-05-20T00:01:36Z | QComp: A QSAR-Based Data Completion Framework for Drug Discovery | In drug discovery, in vitro and in vivo experiments reveal biochemical activities related to the efficacy and toxicity of compounds. The experimental data accumulate into massive, ever-evolving, and sparse datasets. Quantitative Structure-Activity Relationship (QSAR) models, which predict biochemical activities using only the structural information of compounds, face challenges in integrating the evolving experimental data as studies progress. We develop QSAR-Complete (QComp), a data completion framework to address this issue. Based on pre-existing QSAR models, QComp utilizes the correlation inherent in experimental data to enhance prediction accuracy across various tasks. Moreover, QComp emerges as a promising tool for guiding the optimal sequence of experiments by quantifying the reduction in statistical uncertainty for specific endpoints, thereby aiding in rational decision-making throughout the drug discovery process. | [
"['Bingjia Yang' 'Yunsie Chung' 'Archer Y. Yang' 'Bo Yuan' 'Xiang Yu']"
] |
null | null | 2405.11704 | null | null | http://arxiv.org/pdf/2405.11704v1 | 2024-05-20T00:10:00Z | 2024-05-20T00:10:00Z | Efficiency optimization of large-scale language models based on deep
learning in natural language processing tasks | The internal structure and operation mechanism of large-scale language models are analyzed theoretically, especially how Transformer and its derivative architectures can restrict computing efficiency while capturing long-term dependencies. Further, we dig deep into the efficiency bottleneck of the training phase, and evaluate in detail the contribution of adaptive optimization algorithms (such as AdamW), massively parallel computing techniques, and mixed precision training strategies to accelerate convergence and reduce memory footprint. By analyzing the mathematical principles and implementation details of these algorithms, we reveal how they effectively improve training efficiency in practice. In terms of model deployment and inference optimization, this paper systematically reviews the latest advances in model compression techniques, focusing on strategies such as quantification, pruning, and knowledge distillation. By comparing the theoretical frameworks of these techniques and their effects in different application scenarios, we demonstrate their ability to significantly reduce model size and inference delay while maintaining model prediction accuracy. In addition, this paper critically examines the limitations of current efficiency optimization methods, such as the increased risk of overfitting, the control of performance loss after compression, and the problem of algorithm generality, and proposes some prospects for future research. In conclusion, this study provides a comprehensive theoretical framework for understanding the efficiency optimization of large-scale language models. | [
"['Taiyuan Mei' 'Yun Zi' 'Xiaohan Cheng' 'Zijun Gao' 'Qi Wang'\n 'Haowei Yang']"
] |
null | null | 2405.11708 | null | null | http://arxiv.org/pdf/2405.11708v2 | 2024-05-27T00:38:08Z | 2024-05-20T00:58:53Z | Adaptive Batch Normalization Networks for Adversarial Robustness | Deep networks are vulnerable to adversarial examples. Adversarial Training (AT) has been a standard foundation of modern adversarial defense approaches due to its remarkable effectiveness. However, AT is extremely time-consuming, refraining it from wide deployment in practical applications. In this paper, we aim at a non-AT defense: How to design a defense method that gets rid of AT but is still robust against strong adversarial attacks? To answer this question, we resort to adaptive Batch Normalization (BN), inspired by the recent advances in test-time domain adaptation. We propose a novel defense accordingly, referred to as the Adaptive Batch Normalization Network (ABNN). ABNN employs a pre-trained substitute model to generate clean BN statistics and sends them to the target model. The target model is exclusively trained on clean data and learns to align the substitute model's BN statistics. Experimental results show that ABNN consistently improves adversarial robustness against both digital and physically realizable attacks on both image and video datasets. Furthermore, ABNN can achieve higher clean data performance and significantly lower training time complexity compared to AT-based approaches. | [
"['Shao-Yuan Lo' 'Vishal M. Patel']"
] |
null | null | 2405.11715 | null | null | http://arxiv.org/pdf/2405.11715v1 | 2024-05-20T01:29:45Z | 2024-05-20T01:29:45Z | Semantic Trajectory Data Mining with LLM-Informed POI Classification | Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based approaches without the integration of semantic information show a limitation in both efficiency and accuracy. Semantic information, such as activity types inferred from Points of Interest (POI) data, can significantly enhance the quality of trajectory mining. However, integrating these insights is challenging, as many POIs have incomplete feature information, and current learning-based POI algorithms require the integrity of datasets to do the classification. In this paper, we introduce a novel pipeline for human travel trajectory mining. Our approach first leverages the strong inferential and comprehension capabilities of large language models (LLMs) to annotate POI with activity types and then uses a Bayesian-based algorithm to infer activity for each stay point in a trajectory. In our evaluation using the OpenStreetMap (OSM) POI dataset, our approach achieves a 93.4% accuracy and a 96.1% F-1 score in POI classification, and a 91.7% accuracy with a 92.3% F-1 score in activity inference. | [
"['Yifan Liu' 'Chenchen Kuai' 'Haoxuan Ma' 'Xishun Liao'\n 'Brian Yueshuai He' 'Jiaqi Ma']"
] |
null | null | 2405.11718 | null | null | http://arxiv.org/pdf/2405.11718v2 | 2024-06-13T06:18:25Z | 2024-05-20T01:37:21Z | Feasibility Consistent Representation Learning for Safe Reinforcement
Learning | In the field of safe reinforcement learning (RL), finding a balance between satisfying safety constraints and optimizing reward performance presents a significant challenge. A key obstacle in this endeavor is the estimation of safety constraints, which is typically more difficult than estimating a reward metric due to the sparse nature of the constraint signals. To address this issue, we introduce a novel framework named Feasibility Consistent Safe Reinforcement Learning (FCSRL). This framework combines representation learning with feasibility-oriented objectives to identify and extract safety-related information from the raw state for safe RL. Leveraging self-supervised learning techniques and a more learnable safety metric, our approach enhances the policy learning and constraint estimation. Empirical evaluations across a range of vector-state and image-based tasks demonstrate that our method is capable of learning a better safety-aware embedding and achieving superior performance than previous representation learning baselines. | [
"['Zhepeng Cen' 'Yihang Yao' 'Zuxin Liu' 'Ding Zhao']"
] |
null | null | 2405.11727 | null | null | http://arxiv.org/pdf/2405.11727v1 | 2024-05-20T02:09:07Z | 2024-05-20T02:09:07Z | Highway Graph to Accelerate Reinforcement Learning | Reinforcement Learning (RL) algorithms often suffer from low training efficiency. A strategy to mitigate this issue is to incorporate a model-based planning algorithm, such as Monte Carlo Tree Search (MCTS) or Value Iteration (VI), into the environmental model. The major limitation of VI is the need to iterate over a large tensor. These still lead to intensive computations. We focus on improving the training efficiency of RL algorithms by improving the efficiency of the value learning process. For the deterministic environments with discrete state and action spaces, a non-branching sequence of transitions moves the agent without deviating from intermediate states, which we call a highway. On such non-branching highways, the value-updating process can be merged as a one-step process instead of iterating the value step-by-step. Based on this observation, we propose a novel graph structure, named highway graph, to model the state transition. Our highway graph compresses the transition model into a concise graph, where edges can represent multiple state transitions to support value propagation across multiple time steps in each iteration. We thus can obtain a more efficient value learning approach by facilitating the VI algorithm on highway graphs. By integrating the highway graph into RL (as a model-based off-policy RL method), the RL training can be remarkably accelerated in the early stages (within 1 million frames). Comparison against various baselines on four categories of environments reveals that our method outperforms both representative and novel model-free and model-based RL algorithms, demonstrating 10 to more than 150 times more efficiency while maintaining an equal or superior expected return, as confirmed by carefully conducted analyses. Moreover, a deep neural network-based agent is trained using the highway graph, resulting in better generalization and lower storage costs. | [
"['Zidu Yin' 'Zhen Zhang' 'Dong Gong' 'Stefano V. Albrecht' 'Javen Q. Shi']"
] |
null | null | 2405.11729 | null | null | http://arxiv.org/pdf/2405.11729v1 | 2024-05-20T02:21:01Z | 2024-05-20T02:21:01Z | Optimization of Worker Scheduling at Logistics Depots Using Genetic
Algorithms and Simulated Annealing | This paper addresses the optimization of scheduling for workers at a logistics depot using a combination of genetic algorithm and simulated annealing algorithm. The efficient scheduling of permanent and temporary workers is crucial for optimizing the efficiency of the logistics depot while minimizing labor usage. The study begins by establishing a 0-1 integer linear programming model, with decision variables determining the scheduling of permanent and temporary workers for each time slot on a given day. The objective function aims to minimize person-days, while constraints ensure fulfillment of hourly labor requirements, limit workers to one time slot per day, cap consecutive working days for permanent workers, and maintain non-negativity and integer constraints. The model is then solved using genetic algorithms and simulated annealing. Results indicate that, for this problem, genetic algorithms outperform simulated annealing in terms of solution quality. The optimal solution reveals a minimum of 29857 person-days. | [
"['Jinxin Xu' 'Haixin Wu' 'Yu Cheng' 'Liyang Wang' 'Xin Yang' 'Xintong Fu'\n 'Yuelong Su']"
] |
null | null | 2405.11730 | null | null | http://arxiv.org/pdf/2405.11730v1 | 2024-05-20T02:24:36Z | 2024-05-20T02:24:36Z | Degree of Irrationality: Sentiment and Implied Volatility Surface | In this study, we constructed daily high-frequency sentiment data and used the VAR method to attempt to predict the next day's implied volatility surface. We utilized 630,000 text data entries from the East Money Stock Forum from 2014 to 2023 and employed deep learning methods such as BERT and LSTM to build daily market sentiment indicators. By applying FFT and EMD methods for sentiment decomposition, we found that high-frequency sentiment had a stronger correlation with at-the-money (ATM) options' implied volatility, while low-frequency sentiment was more strongly correlated with deep out-of-the-money (DOTM) options' implied volatility. Further analysis revealed that the shape of the implied volatility surface contains richer market sentiment information beyond just market panic. We demonstrated that incorporating this sentiment information can improve the accuracy of implied volatility surface predictions. | [
"['Jiahao Weng' 'Yan Xie']"
] |
null | null | 2405.11739 | null | null | http://arxiv.org/pdf/2405.11739v1 | 2024-05-20T02:41:21Z | 2024-05-20T02:41:21Z | Contactless Polysomnography: What Radio Waves Tell Us about Sleep | The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases. | [
"['Hao He' 'Chao Li' 'Wolfgang Ganglberger' 'Kaileigh Gallagher'\n 'Rumen Hristov' 'Michail Ouroutzoglou' 'Haoqi Sun' 'Jimeng Sun'\n 'Brandon Westover' 'Dina Katabi']"
] |
null | null | 2405.11740 | null | null | http://arxiv.org/pdf/2405.11740v1 | 2024-05-20T02:43:04Z | 2024-05-20T02:43:04Z | Learning Future Representation with Synthetic Observations for
Sample-efficient Reinforcement Learning | In visual Reinforcement Learning (RL), upstream representation learning largely determines the effect of downstream policy learning. Employing auxiliary tasks allows the agent to enhance visual representation in a targeted manner, thereby improving the sample efficiency and performance of downstream RL. Prior advanced auxiliary tasks all focus on how to extract as much information as possible from limited experience (including observations, actions, and rewards) through their different auxiliary objectives, whereas in this article, we first start from another perspective: auxiliary training data. We try to improve auxiliary representation learning for RL by enriching auxiliary training data, proposing textbf{L}earning textbf{F}uture representation with textbf{S}ynthetic observations textbf{(LFS)}, a novel self-supervised RL approach. Specifically, we propose a training-free method to synthesize observations that may contain future information, as well as a data selection approach to eliminate unqualified synthetic noise. The remaining synthetic observations and real observations then serve as the auxiliary data to achieve a clustering-based temporal association task for representation learning. LFS allows the agent to access and learn observations that have not yet appeared in advance, so as to quickly understand and exploit them when they occur later. In addition, LFS does not rely on rewards or actions, which means it has a wider scope of application (e.g., learning from video) than recent advanced auxiliary tasks. Extensive experiments demonstrate that our LFS exhibits state-of-the-art RL sample efficiency on challenging continuous control and enables advanced visual pre-training based on action-free video demonstrations. | [
"['Xin Liu' 'Yaran Chen' 'Dongbin Zhao']"
] |
null | null | 2405.11743 | null | null | http://arxiv.org/pdf/2405.11743v1 | 2024-05-20T03:01:43Z | 2024-05-20T03:01:43Z | A General Theory for Compositional Generalization | Compositional Generalization (CG) embodies the ability to comprehend novel combinations of familiar concepts, representing a significant cognitive leap in human intellectual advancement. Despite its critical importance, the deep neural network (DNN) faces challenges in addressing the compositional generalization problem, prompting considerable research interest. However, existing theories often rely on task-specific assumptions, constraining the comprehensive understanding of CG. This study aims to explore compositional generalization from a task-agnostic perspective, offering a complementary viewpoint to task-specific analyses. The primary challenge is to define CG without overly restricting its scope, a feat achieved by identifying its fundamental characteristics and basing the definition on them. Using this definition, we seek to answer the question "what does the ultimate solution to CG look like?" through the following theoretical findings: 1) the first No Free Lunch theorem in CG, indicating the absence of general solutions; 2) a novel generalization bound applicable to any CG problem, specifying the conditions for an effective CG solution; and 3) the introduction of the generative effect to enhance understanding of CG problems and their solutions. This paper's significance lies in providing a general theory for CG problems, which, when combined with prior theorems under task-specific scenarios, can lead to a comprehensive understanding of CG. | [
"['Jingwen Fu' 'Zhizheng Zhang' 'Yan Lu' 'Nanning Zheng']"
] |
null | null | 2405.11746 | null | null | http://arxiv.org/pdf/2405.11746v1 | 2024-05-20T03:10:22Z | 2024-05-20T03:10:22Z | Configurable Mirror Descent: Towards a Unification of Decision Making | Decision-making problems, categorized as single-agent, e.g., Atari, cooperative multi-agent, e.g., Hanabi, competitive multi-agent, e.g., Hold'em poker, and mixed cooperative and competitive, e.g., football, are ubiquitous in the real world. Various methods are proposed to address the specific decision-making problems. Despite the successes in specific categories, these methods typically evolve independently and cannot generalize to other categories. Therefore, a fundamental question for decision-making is: emph{Can we develop textbf{a single algorithm} to tackle textbf{ALL} categories of decision-making problems?} There are several main challenges to address this question: i) different decision-making categories involve different numbers of agents and different relationships between agents, ii) different categories have different solution concepts and evaluation measures, and iii) there lacks a comprehensive benchmark covering all the categories. This work presents a preliminary attempt to address the question with three main contributions. i) We propose the generalized mirror descent (GMD), a generalization of MD variants, which considers multiple historical policies and works with a broader class of Bregman divergences. ii) We propose the configurable mirror descent (CMD) where a meta-controller is introduced to dynamically adjust the hyper-parameters in GMD conditional on the evaluation measures. iii) We construct the textsc{GameBench} with 15 academic-friendly games across different decision-making categories. Extensive experiments demonstrate that CMD achieves empirically competitive or better outcomes compared to baselines while providing the capability of exploring diverse dimensions of decision making. | [
"['Pengdeng Li' 'Shuxin Li' 'Chang Yang' 'Xinrun Wang' 'Shuyue Hu'\n 'Xiao Huang' 'Hau Chan' 'Bo An']"
] |
null | null | 2405.11751 | null | null | http://arxiv.org/pdf/2405.11751v1 | 2024-05-20T03:24:24Z | 2024-05-20T03:24:24Z | Asymptotic theory of in-context learning by linear attention | Transformers have a remarkable ability to learn and execute tasks based on examples provided within the input itself, without explicit prior training. It has been argued that this capability, known as in-context learning (ICL), is a cornerstone of Transformers' success, yet questions about the necessary sample complexity, pretraining task diversity, and context length for successful ICL remain unresolved. Here, we provide a precise answer to these questions in an exactly solvable model of ICL of a linear regression task by linear attention. We derive sharp asymptotics for the learning curve in a phenomenologically-rich scaling regime where the token dimension is taken to infinity; the context length and pretraining task diversity scale proportionally with the token dimension; and the number of pretraining examples scales quadratically. We demonstrate a double-descent learning curve with increasing pretraining examples, and uncover a phase transition in the model's behavior between low and high task diversity regimes: In the low diversity regime, the model tends toward memorization of training tasks, whereas in the high diversity regime, it achieves genuine in-context learning and generalization beyond the scope of pretrained tasks. These theoretical insights are empirically validated through experiments with both linear attention and full nonlinear Transformer architectures. | [
"['Yue M. Lu' 'Mary I. Letey' 'Jacob A. Zavatone-Veth' 'Anindita Maiti'\n 'Cengiz Pehlevan']"
] |
null | null | 2405.11752 | null | null | http://arxiv.org/pdf/2405.11752v1 | 2024-05-20T03:26:58Z | 2024-05-20T03:26:58Z | Foundation Model for Chemical Process Modeling: Meta-Learning with
Physics-Informed Adaptation | In this work, we introduce a novel application of foundation models in the domain of nonlinear chemical process modeling. Given the challenges of obtaining accurate first-principles models for real-world chemical processes and the inefficiency of rebuilding and retraining models for new chemical processes, we pose a pivotal question: What if we could develop a single, universal neural network (i.e., foundation model) capable of rapidly adapting to modeling any new chemical process? To address this question, we propose a meta-learning-based approach using Reptile to construct the foundation model, followed by physics-informed adaptation to fine-tune it to new modeling tasks using only a few data samples. To assess the effectiveness of our methodology, we construct a foundation model for various chemical reactions in three classical generic reactors, including continuous stirred tank reactors (CSTRs), batch reactors (BRs), and plug flow reactors (PFRs). Our approach outperforms conventional methods such as data-driven learning, physics-informed learning, transfer learning, and pure meta-learning in a few-shot setting. Furthermore, our method achieves rapid adaptation to new CSTRs, BRs, and PFRs using only a few data samples from the designated tasks. Source code is available at https://github.com/killingbear999/chemical-process-foundation-model. | [
"['Zihao Wang' 'Zhe Wu']"
] |
null | null | 2405.11756 | null | null | http://arxiv.org/pdf/2405.11756v1 | 2024-05-20T03:33:12Z | 2024-05-20T03:33:12Z | Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised
Learning | Semi-supervised learning (SSL) has witnessed remarkable progress, resulting in the emergence of numerous method variations. However, practitioners often encounter challenges when attempting to deploy these methods due to their subpar performance. In this paper, we present a novel SSL approach named FineSSL that significantly addresses this limitation by adapting pre-trained foundation models. We identify the aggregated biases and cognitive deviation problems inherent in foundation models, and propose a simple yet effective solution by imposing balanced margin softmax and decoupled label smoothing. Through extensive experiments, we demonstrate that FineSSL sets a new state of the art for SSL on multiple benchmark datasets, reduces the training cost by over six times, and can seamlessly integrate various fine-tuning and modern SSL algorithms. The source code is available at https://github.com/Gank0078/FineSSL. | [
"['Kai Gan' 'Tong Wei']"
] |
null | null | 2405.11758 | null | null | http://arxiv.org/pdf/2405.11758v1 | 2024-05-20T03:35:13Z | 2024-05-20T03:35:13Z | Fed-Credit: Robust Federated Learning with Credibility Management | Aiming at privacy preservation, Federated Learning (FL) is an emerging machine learning approach enabling model training on decentralized devices or data sources. The learning mechanism of FL relies on aggregating parameter updates from individual clients. However, this process may pose a potential security risk due to the presence of malicious devices. Existing solutions are either costly due to the use of compute-intensive technology, or restrictive for reasons of strong assumptions such as the prior knowledge of the number of attackers and how they attack. Few methods consider both privacy constraints and uncertain attack scenarios. In this paper, we propose a robust FL approach based on the credibility management scheme, called Fed-Credit. Unlike previous studies, our approach does not require prior knowledge of the nodes and the data distribution. It maintains and employs a credibility set, which weighs the historical clients' contributions based on the similarity between the local models and global model, to adjust the global model update. The subtlety of Fed-Credit is that the time decay and attitudinal value factor are incorporated into the dynamic adjustment of the reputation weights and it boasts a computational complexity of O(n) (n is the number of the clients). We conducted extensive experiments on the MNIST and CIFAR-10 datasets under 5 types of attacks. The results exhibit superior accuracy and resilience against adversarial attacks, all while maintaining comparatively low computational complexity. Among these, on the Non-IID CIFAR-10 dataset, our algorithm exhibited performance enhancements of 19.5% and 14.5%, respectively, in comparison to the state-of-the-art algorithm when dealing with two types of data poisoning attacks. | [
"['Jiayan Chen' 'Zhirong Qian' 'Tianhui Meng' 'Xitong Gao' 'Tian Wang'\n 'Weijia Jia']"
] |
null | null | 2405.11762 | null | null | http://arxiv.org/pdf/2405.11762v2 | 2024-05-29T13:02:11Z | 2024-05-20T03:46:42Z | Interpretability of Statistical, Machine Learning, and Deep Learning
Models for Landslide Susceptibility Mapping in Three Gorges Reservoir Area | Landslide susceptibility mapping (LSM) is crucial for identifying high-risk areas and informing prevention strategies. This study investigates the interpretability of statistical, machine learning (ML), and deep learning (DL) models in predicting landslide susceptibility. This is achieved by incorporating various relevant interpretation methods and two types of input factors: a comprehensive set of 19 contributing factors that are statistically relevant to landslides, as well as a dedicated set of 9 triggering factors directly associated with triggering landslides. Given that model performance is a crucial metric in LSM, our investigations into interpretability naturally involve assessing and comparing LSM accuracy across different models considered. In our investigation, the convolutional neural network model achieved the highest accuracy (0.8447 with 19 factors; 0.8048 with 9 factors), while Extreme Gradient Boosting and Support Vector Machine also demonstrated strong predictive capabilities, outperforming conventional statistical models. These findings indicate that DL and sophisticated ML algorithms can effectively capture the complex relationships between input factors and landslide occurrence. However, the interpretability of predictions varied among different models, particularly when using the broader set of 19 contributing factors. Explanation methods like SHAP, LIME, and DeepLIFT also led to variations in interpretation results. Using a comprehensive set of 19 contributing factors improved prediction accuracy but introduced complexities and inconsistency in model interpretations. Focusing on a dedicated set of 9 triggering factors sacrificed some predictive power but enhanced interpretability, as evidenced by more consistent key factors identified across various models and alignment with the findings of field investigation reports.... | [
"['Cheng Chen' 'Lei Fan']"
] |
null | null | 2405.11766 | null | null | http://arxiv.org/pdf/2405.11766v1 | 2024-05-20T03:52:41Z | 2024-05-20T03:52:41Z | From SHAP Scores to Feature Importance Scores | A central goal of eXplainable Artificial Intelligence (XAI) is to assign relative importance to the features of a Machine Learning (ML) model given some prediction. The importance of this task of explainability by feature attribution is illustrated by the ubiquitous recent use of tools such as SHAP and LIME. Unfortunately, the exact computation of feature attributions, using the game-theoretical foundation underlying SHAP and LIME, can yield manifestly unsatisfactory results, that tantamount to reporting misleading relative feature importance. Recent work targeted rigorous feature attribution, by studying axiomatic aggregations of features based on logic-based definitions of explanations by feature selection. This paper shows that there is an essential relationship between feature attribution and a priori voting power, and that those recently proposed axiomatic aggregations represent a few instantiations of the range of power indices studied in the past. Furthermore, it remains unclear how some of the most widely used power indices might be exploited as feature importance scores (FISs), i.e. the use of power indices in XAI, and which of these indices would be the best suited for the purposes of XAI by feature attribution, namely in terms of not producing results that could be deemed as unsatisfactory. This paper proposes novel desirable properties that FISs should exhibit. In addition, the paper also proposes novel FISs exhibiting the proposed properties. Finally, the paper conducts a rigorous analysis of the best-known power indices in terms of the proposed properties. | [
"['Olivier Letoffe' 'Xuanxiang Huang' 'Nicholas Asher' 'Joao Marques-Silva']"
] |
null | null | 2405.11769 | null | null | http://arxiv.org/pdf/2405.11769v1 | 2024-05-20T04:05:30Z | 2024-05-20T04:05:30Z | Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose
Prediction | In recent years, machine learning (ML) methods have emerged as promising alternatives for molecular docking, offering the potential for high accuracy without incurring prohibitive computational costs. However, recent studies have indicated that these ML models may overfit to quantitative metrics while neglecting the physical constraints inherent in the problem. In this work, we present Uni-Mol Docking V2, which demonstrates a remarkable improvement in performance, accurately predicting the binding poses of 77+% of ligands in the PoseBusters benchmark with an RMSD value of less than 2.0 {AA}, and 75+% passing all quality checks. This represents a significant increase from the 62% achieved by the previous Uni-Mol Docking model. Notably, our Uni-Mol Docking approach generates chemically accurate predictions, circumventing issues such as chirality inversions and steric clashes that have plagued previous ML models. Furthermore, we observe enhanced performance in terms of high-quality predictions (RMSD values of less than 1.0 {AA} and 1.5 {AA}) and physical soundness when Uni-Mol Docking is combined with more physics-based methods like Uni-Dock. Our results represent a significant advancement in the application of artificial intelligence for scientific research, adopting a holistic approach to ligand docking that is well-suited for industrial applications in virtual screening and drug design. The code, data and service for Uni-Mol Docking are publicly available for use and further development in https://github.com/dptech-corp/Uni-Mol. | [
"['Eric Alcaide' 'Zhifeng Gao' 'Guolin Ke' 'Yaqi Li' 'Linfeng Zhang'\n 'Hang Zheng' 'Gengmo Zhou']"
] |
null | null | 2405.11775 | null | null | http://arxiv.org/pdf/2405.11775v1 | 2024-05-20T04:31:04Z | 2024-05-20T04:31:04Z | Exploring Ordinality in Text Classification: A Comparative Study of
Explicit and Implicit Techniques | Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that textbf{explicitly} account for the ordinal nature of labels. However, with the advent of Pretrained Language Models (PLMs), it became possible to tackle ordinality through the textbf{implicit} semantics of the labels as well. This paper provides a comprehensive theoretical and empirical examination of both these approaches. Furthermore, we also offer strategic recommendations regarding the most effective approach to adopt based on specific settings. | [
"['Siva Rajesh Kasa' 'Aniket Goel' 'Karan Gupta' 'Sumegh Roychowdhury'\n 'Anish Bhanushali' 'Nikhil Pattisapu' 'Prasanna Srinivasa Murthy']"
] |
null | null | 2405.11778 | null | null | http://arxiv.org/pdf/2405.11778v1 | 2024-05-20T04:36:02Z | 2024-05-20T04:36:02Z | Efficient Multi-agent Reinforcement Learning by Planning | Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable breakthroughs in solving large-scale decision-making tasks. Nonetheless, most existing MARL algorithms are model-free, limiting sample efficiency and hindering their applicability in more challenging scenarios. In contrast, model-based reinforcement learning (MBRL), particularly algorithms integrating planning, such as MuZero, has demonstrated superhuman performance with limited data in many tasks. Hence, we aim to boost the sample efficiency of MARL by adopting model-based approaches. However, incorporating planning and search methods into multi-agent systems poses significant challenges. The expansive action space of multi-agent systems often necessitates leveraging the nearly-independent property of agents to accelerate learning. To tackle this issue, we propose the MAZero algorithm, which combines a centralized model with Monte Carlo Tree Search (MCTS) for policy search. We design a novel network structure to facilitate distributed execution and parameter sharing. To enhance search efficiency in deterministic environments with sizable action spaces, we introduce two novel techniques: Optimistic Search Lambda (OS($lambda$)) and Advantage-Weighted Policy Optimization (AWPO). Extensive experiments on the SMAC benchmark demonstrate that MAZero outperforms model-free approaches in terms of sample efficiency and provides comparable or better performance than existing model-based methods in terms of both sample and computational efficiency. Our code is available at https://github.com/liuqh16/MAZero. | [
"['Qihan Liu' 'Jianing Ye' 'Xiaoteng Ma' 'Jun Yang' 'Bin Liang'\n 'Chongjie Zhang']"
] |
null | null | 2405.11780 | null | null | http://arxiv.org/pdf/2405.11780v1 | 2024-05-20T04:46:14Z | 2024-05-20T04:46:14Z | General bounds on the quality of Bayesian coresets | Bayesian coresets speed up posterior inference in the large-scale data regime by approximating the full-data log-likelihood function with a surrogate log-likelihood based on a small, weighted subset of the data. But while Bayesian coresets and methods for construction are applicable in a wide range of models, existing theoretical analysis of the posterior inferential error incurred by coreset approximations only apply in restrictive settings -- i.e., exponential family models, or models with strong log-concavity and smoothness assumptions. This work presents general upper and lower bounds on the Kullback-Leibler (KL) divergence of coreset approximations that reflect the full range of applicability of Bayesian coresets. The lower bounds require only mild model assumptions typical of Bayesian asymptotic analyses, while the upper bounds require the log-likelihood functions to satisfy a generalized subexponentiality criterion that is weaker than conditions used in earlier work. The lower bounds are applied to obtain fundamental limitations on the quality of coreset approximations, and to provide a theoretical explanation for the previously-observed poor empirical performance of importance sampling-based construction methods. The upper bounds are used to analyze the performance of recent subsample-optimize methods. The flexibility of the theory is demonstrated in validation experiments involving multimodal, unidentifiable, heavy-tailed Bayesian posterior distributions. | [
"['Trevor Campbell']"
] |
null | null | 2405.11783 | null | null | http://arxiv.org/pdf/2405.11783v1 | 2024-05-20T05:02:12Z | 2024-05-20T05:02:12Z | Inverse Design of Metal-Organic Frameworks Using Quantum Natural
Language Processing | In this study, we explore the potential of using quantum natural language processing (QNLP) to inverse design metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 150 hypothetical MOF structures consisting of 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore volume and $H_{2}$ uptake values. We then compare various QNLP models (i.e. the bag-of-words, DisCoCat (Distributional Compositional Categorical), and sequence-based models) to identify the most effective approach to process the MOF dataset. Using a classical simulator provided by the IBM Qiskit, the bag-of-words model is identified to be the optimum model, achieving validation accuracies of 85.7% and 86.7% for binary classification tasks on pore volume and $H_{2}$ uptake, respectively. Further, we developed multi-class classification models tailored to the probabilistic nature of quantum circuits, with average test accuracies of 88.4% and 80.7% across different classes for pore volume and $H_{2}$ uptake datasets. Finally, the performance of generating MOF with target properties showed accuracies of 93.5% for pore volume and 89% for $H_{2}$ uptake, respectively. Although our investigation covers only a fraction of the vast MOF search space, it marks a promising first step towards using quantum computing for materials design, offering a new perspective through which to explore the complex landscape of MOFs. | [
"['Shinyoung Kang' 'Jihan Kim']"
] |
null | null | 2405.11784 | null | null | http://arxiv.org/pdf/2405.11784v1 | 2024-05-20T05:05:14Z | 2024-05-20T05:05:14Z | Reward-Punishment Reinforcement Learning with Maximum Entropy | We introduce the ``soft Deep MaxPain'' (softDMP) algorithm, which integrates the optimization of long-term policy entropy into reward-punishment reinforcement learning objectives. Our motivation is to facilitate a smoother variation of operators utilized in the updating of action values beyond traditional ``max'' and ``min'' operators, where the goal is enhancing sample efficiency and robustness. We also address two unresolved issues from the previous Deep MaxPain method. Firstly, we investigate how the negated (``flipped'') pain-seeking sub-policy, derived from the punishment action value, collaborates with the ``min'' operator to effectively learn the punishment module and how softDMP's smooth learning operator provides insights into the ``flipping'' trick. Secondly, we tackle the challenge of data collection for learning the punishment module to mitigate inconsistencies arising from the involvement of the ``flipped'' sub-policy (pain-avoidance sub-policy) in the unified behavior policy. We empirically explore the first issue in two discrete Markov Decision Process (MDP) environments, elucidating the crucial advancements of the DMP approach and the necessity for soft treatments on the hard operators. For the second issue, we propose a probabilistic classifier based on the ratio of the pain-seeking sub-policy to the sum of the pain-seeking and goal-reaching sub-policies. This classifier assigns roll-outs to separate replay buffers for updating reward and punishment action-value functions, respectively. Our framework demonstrates superior performance in Turtlebot 3's maze navigation tasks under the ROS Gazebo simulation. | [
"['Jiexin Wang' 'Eiji Uchibe']"
] |
null | null | 2405.11785 | null | null | http://arxiv.org/pdf/2405.11785v1 | 2024-05-20T05:08:55Z | 2024-05-20T05:08:55Z | Guided Multi-objective Generative AI to Enhance Structure-based Drug
Design | Generative AI has the potential to revolutionize drug discovery. Yet, despite recent advances in machine learning, existing models cannot generate molecules that satisfy all desired physicochemical properties. Herein, we describe IDOLpro, a novel generative chemistry AI combining deep diffusion with multi-objective optimization for structure-based drug design. The latent variables of the diffusion model are guided by differentiable scoring functions to explore uncharted chemical space and generate novel ligands in silico, optimizing a plurality of target physicochemical properties. We demonstrate its effectiveness by generating ligands with optimized binding affinity and synthetic accessibility on two benchmark sets. IDOLpro produces ligands with binding affinities over 10% higher than the next best state-of-the-art on each test set. On a test set of experimental complexes, IDOLpro is the first to surpass the performance of experimentally observed ligands. IDOLpro can accommodate other scoring functions (e.g. ADME-Tox) to accelerate hit-finding, hit-to-lead, and lead optimization for drug discovery. | [
"['Amit Kadan' 'Kevin Ryczko' 'Adrian Roitberg' 'Takeshi Yamazaki']"
] |
null | null | 2405.11788 | null | null | http://arxiv.org/pdf/2405.11788v1 | 2024-05-20T05:11:02Z | 2024-05-20T05:11:02Z | TinyLLaVA Factory: A Modularized Codebase for Small-scale Large
Multimodal Models | We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. Following the design philosophy of the factory pattern in software engineering, TinyLLaVA Factory modularizes the entire system into interchangeable components, with each component integrating a suite of cutting-edge models and methods, meanwhile leaving room for extensions to more features. In addition to allowing users to customize their own LMMs, TinyLLaVA Factory provides popular training recipes to let users pretrain and finetune their models with less coding effort. Empirical experiments validate the effectiveness of our codebase. The goal of TinyLLaVA Factory is to assist researchers and practitioners in exploring the wide landscape of designing and training small-scale LMMs with affordable computational resources. | [
"['Junlong Jia' 'Ying Hu' 'Xi Weng' 'Yiming Shi' 'Miao Li' 'Xingjian Zhang'\n 'Baichuan Zhou' 'Ziyu Liu' 'Jie Luo' 'Lei Huang' 'Ji Wu']"
] |
null | null | 2405.11795 | null | null | http://arxiv.org/pdf/2405.11795v1 | 2024-05-20T05:29:45Z | 2024-05-20T05:29:45Z | Application of time-series quantum generative model to financial data | Despite proposing a quantum generative model for time series that successfully learns correlated series with multiple Brownian motions, the model has not been adapted and evaluated for financial problems. In this study, a time-series generative model was applied as a quantum generative model to actual financial data. Future data for two correlated time series were generated and compared with classical methods such as long short-term memory and vector autoregression. Furthermore, numerical experiments were performed to complete missing values. Based on the results, we evaluated the practical applications of the time-series quantum generation model. It was observed that fewer parameter values were required compared with the classical method. In addition, the quantum time-series generation model was feasible for both stationary and nonstationary data. These results suggest that several parameters can be applied to various types of time-series data. | [
"['Shun Okumura' 'Masayuki Ohzeki' 'Masaya Abe']"
] |
null | null | 2405.11801 | null | null | http://arxiv.org/pdf/2405.11801v1 | 2024-05-20T05:46:41Z | 2024-05-20T05:46:41Z | LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph
Clustering | Graph clustering is a fundamental problem in machine learning. Deep learning methods achieve the state-of-the-art results in recent years, but they still cannot work without predefined cluster numbers. Such limitation motivates us to pose a more challenging problem of graph clustering with unknown cluster number. We propose to address this problem from a fresh perspective of graph information theory (i.e., structural information). In the literature, structural information has not yet been introduced to deep clustering, and its classic definition falls short of discrete formulation and modeling node features. In this work, we first formulate a differentiable structural information (DSI) in the continuous realm, accompanied by several theoretical results. By minimizing DSI, we construct the optimal partitioning tree where densely connected nodes in the graph tend to have the same assignment, revealing the cluster structure. DSI is also theoretically presented as a new graph clustering objective, not requiring the predefined cluster number. Furthermore, we design a neural LSEnet in the Lorentz model of hyperbolic space, where we integrate node features to structural information via manifold-valued graph convolution. Extensive empirical results on real graphs show the superiority of our approach. | [
"['Li Sun' 'Zhenhao Huang' 'Hao Peng' 'Yujie Wang' 'Chunyang Liu'\n 'Philip S. Yu']"
] |
null | null | 2405.11802 | null | null | http://arxiv.org/pdf/2405.11802v1 | 2024-05-20T05:48:20Z | 2024-05-20T05:48:20Z | Counterfactual Explanation-Based Badminton Motion Guidance Generation
Using Wearable Sensors | This study proposes a framework for enhancing the stroke quality of badminton players by generating personalized motion guides, utilizing a multimodal wearable dataset. These guides are based on counterfactual algorithms and aim to reduce the performance gap between novice and expert players. Our approach provides joint-level guidance through visualizable data to assist players in improving their movements without requiring expert knowledge. The method was evaluated against a traditional algorithm using metrics to assess validity, proximity, and plausibility, including arithmetic measures and motion-specific evaluation metrics. Our evaluation demonstrates that the proposed framework can generate motions that maintain the essence of original movements while enhancing stroke quality, providing closer guidance than direct expert motion replication. The results highlight the potential of our approach for creating personalized sports motion guides by generating counterfactual motion guidance for arbitrary input motion samples of badminton strokes. | [
"['Minwoo Seong' 'Gwangbin Kim' 'Yumin Kang' 'Junhyuk Jang'\n 'Joseph DelPreto' 'SeungJun Kim']"
] |
null | null | 2405.11811 | null | null | http://arxiv.org/pdf/2405.11811v1 | 2024-05-20T06:12:33Z | 2024-05-20T06:12:33Z | FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable
Federated Learning | Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients while preserving data privacy. However, the quest to balance acceleration and stability becomes a significant challenge in FL, especially on the client-side. In this paper, we introduce FedCAda, an innovative federated client adaptive algorithm designed to tackle this challenge. FedCAda leverages the Adam algorithm to adjust the correction process of the first moment estimate $m$ and the second moment estimate $v$ on the client-side and aggregate adaptive algorithm parameters on the server-side, aiming to accelerate convergence speed and communication efficiency while ensuring stability and performance. Additionally, we investigate several algorithms incorporating different adjustment functions. This comparative analysis revealed that due to the limited information contained within client models from other clients during the initial stages of federated learning, more substantial constraints need to be imposed on the parameters of the adaptive algorithm. As federated learning progresses and clients gather more global information, FedCAda gradually diminishes the impact on adaptive parameters. These findings provide insights for enhancing the robustness and efficiency of algorithmic improvements. Through extensive experiments on computer vision (CV) and natural language processing (NLP) datasets, we demonstrate that FedCAda outperforms the state-of-the-art methods in terms of adaptability, convergence, stability, and overall performance. This work contributes to adaptive algorithms for federated learning, encouraging further exploration. | [
"['Liuzhi Zhou' 'Yu He' 'Kun Zhai' 'Xiang Liu' 'Sen Liu' 'Xingjun Ma'\n 'Guangnan Ye' 'Yu-Gang Jiang' 'Hongfeng Chai']"
] |
null | null | 2405.11819 | null | null | http://arxiv.org/pdf/2405.11819v1 | 2024-05-20T06:28:43Z | 2024-05-20T06:28:43Z | Beyond MLE: Investigating SEARNN for Low-Resourced Neural Machine
Translation | Structured prediction tasks, like machine translation, involve learning functions that map structured inputs to structured outputs. Recurrent Neural Networks (RNNs) have historically been a popular choice for such tasks, including in natural language processing (NLP) applications. However, training RNNs using Maximum Likelihood Estimation (MLE) has its limitations, including exposure bias and a mismatch between training and testing metrics. SEARNN, based on the learning to search (L2S) framework, has been proposed as an alternative to MLE for RNN training. This project explored the potential of SEARNN to improve machine translation for low-resourced African languages -- a challenging task characterized by limited training data availability and the morphological complexity of the languages. Through experiments conducted on translation for English to Igbo, French to ewe, and French to ghomala directions, this project evaluated the efficacy of SEARNN over MLE in addressing the unique challenges posed by these languages. With an average BLEU score improvement of $5.4$% over the MLE objective, we proved that SEARNN is indeed a viable algorithm to effectively train RNNs on machine translation for low-resourced languages. | [
"['Chris Emezue']"
] |
null | null | 2405.11821 | null | null | http://arxiv.org/pdf/2405.11821v1 | 2024-05-20T06:32:57Z | 2024-05-20T06:32:57Z | A Three-Phase Analysis of Synergistic Effects During Co-pyrolysis of
Algae and Wood for Biochar Yield Using Machine Learning | Pyrolysis techniques have served to be a groundbreaking technique for effectively utilising natural and man-made biomass products like plastics, wood, crop residue, fruit peels etc. Recent advancements have shown a greater yield of essential products like biochar, bio-oil and other non-condensable gases by blending different biomasses in a certain ratio. This synergy effect of combining two pyrolytic raw materials i.e co-pyrolysis of algae and wood biomass has been systematically studied and grouped into 3 phases in this research paper-kinetic analysis of co-pyrolysis, correlation among proximate and ultimate analysis with bio-char yield and lastly grouping of different weight ratios based on biochar yield up to a certain percentage. Different ML and DL algorithms have been utilized for regression and classification techniques to give a comprehensive overview of the effect of the synergy of two different biomass materials on biochar yield. For the first phase, the best prediction of biochar yield was obtained by using a decision tree regressor with a perfect MSE score of 0.00, followed by a gradient-boosting regressor. The second phase was analyzed using both ML and DL techniques. Within ML, SVR proved to be the most convenient model with an accuracy score of 0.972 with DNN employed for deep learning technique. Finally, for the third phase, binary classification was applied to biochar yield with and without heating rate for biochar yield percentage above and below 40%. The best technique for ML was Support Vector followed by Random forest while ANN was the most suitable Deep Learning Technique. | [
"['Subhadeep Chakrabarti' 'Saish Shinde']"
] |
null | null | 2405.11828 | null | null | http://arxiv.org/pdf/2405.11828v1 | 2024-05-20T06:53:55Z | 2024-05-20T06:53:55Z | Federated Learning with Incomplete Sensing Modalities | Many mobile sensing applications utilize data from various modalities, including motion and physiological sensors in mobile and wearable devices. Federated Learning (FL) is particularly suitable for these applications thanks to its privacy-preserving feature. However, challenges such as limited battery life, poor network conditions, and sensor malfunctions can restrict the use of all available modalities for local model training. Additionally, existing multimodal FL systems also struggle with scalability and efficiency as the number of modality sources increases. To address these issues, we introduce FLISM, a framework designed to enable multimodal FL with incomplete modalities. FLISM leverages simulation technique to learn robust representations that can handle missing modalities and transfers model knowledge across clients with varying set of modalities. The evaluation results using three real-world datasets and simulations demonstrate FLISM's effective balance between model performance and system efficiency. It shows an average improvement of .067 in F1-score, while also reducing communication (2.69x faster) and computational (2.28x more efficient) overheads compared to existing methods addressing incomplete modalities. Moreover, in simulated scenarios involving tasks with a larger number of modalities, FLISM achieves a significant speedup of 3.23x~85.10x in communication and 3.73x~32.29x in computational efficiency. | [
"['Adiba Orzikulova' 'Jaehyun Kwak' 'Jaemin Shin' 'Sung-Ju Lee']"
] |
null | null | 2405.11829 | null | null | http://arxiv.org/pdf/2405.11829v1 | 2024-05-20T06:56:43Z | 2024-05-20T06:56:43Z | Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory
Overfitting Challenge in Continual Learning | Continual learning focuses on learning non-stationary data distribution without forgetting previous knowledge. Rehearsal-based approaches are commonly used to combat catastrophic forgetting. However, these approaches suffer from a problem called "rehearsal memory overfitting, " where the model becomes too specialized on limited memory samples and loses its ability to generalize effectively. As a result, the effectiveness of the rehearsal memory progressively decays, ultimately resulting in catastrophic forgetting of the learned tasks. We introduce the Adversarially Diversified Rehearsal Memory (ADRM) to address the memory overfitting challenge. This novel method is designed to enrich memory sample diversity and bolster resistance against natural and adversarial noise disruptions. ADRM employs the FGSM attacks to introduce adversarially modified memory samples, achieving two primary objectives: enhancing memory diversity and fostering a robust response to continual feature drifts in memory samples. Our contributions are as follows: Firstly, ADRM addresses overfitting in rehearsal memory by employing FGSM to diversify and increase the complexity of the memory buffer. Secondly, we demonstrate that ADRM mitigates memory overfitting and significantly improves the robustness of CL models, which is crucial for safety-critical applications. Finally, our detailed analysis of features and visualization demonstrates that ADRM mitigates feature drifts in CL memory samples, significantly reducing catastrophic forgetting and resulting in a more resilient CL model. Additionally, our in-depth t-SNE visualizations of feature distribution and the quantification of the feature similarity further enrich our understanding of feature representation in existing CL approaches. Our code is publically available at https://github.com/hikmatkhan/ADRM. | [
"['Hikmat Khan' 'Ghulam Rasool' 'Nidhal Carla Bouaynaya']"
] |
null | null | 2405.11831 | null | null | http://arxiv.org/pdf/2405.11831v1 | 2024-05-20T06:58:47Z | 2024-05-20T06:58:47Z | SSAMBA: Self-Supervised Audio Representation Learning with Mamba State
Space Model | Transformers have revolutionized deep learning across various tasks, including audio representation learning, due to their powerful modeling capabilities. However, they often suffer from quadratic complexity in both GPU memory usage and computational inference time, affecting their efficiency. Recently, state space models (SSMs) like Mamba have emerged as a promising alternative, offering a more efficient approach by avoiding these complexities. Given these advantages, we explore the potential of SSM-based models in audio tasks. In this paper, we introduce Self-Supervised Audio Mamba (SSAMBA), the first self-supervised, attention-free, and SSM-based model for audio representation learning. SSAMBA leverages the bidirectional Mamba to capture complex audio patterns effectively. We incorporate a self-supervised pretraining framework that optimizes both discriminative and generative objectives, enabling the model to learn robust audio representations from large-scale, unlabeled datasets. We evaluated SSAMBA on various tasks such as audio classification, keyword spotting, and speaker identification. Our results demonstrate that SSAMBA outperforms the Self-Supervised Audio Spectrogram Transformer (SSAST) in most tasks. Notably, SSAMBA is approximately 92.7% faster in batch inference speed and 95.4% more memory-efficient than SSAST for the tiny model size with an input token size of 22k. These efficiency gains, combined with superior performance, underscore the effectiveness of SSAMBA's architectural innovation, making it a compelling choice for a wide range of audio processing applications. | [
"['Siavash Shams' 'Sukru Samet Dindar' 'Xilin Jiang' 'Nima Mesgarani']"
] |
null | null | 2405.11848 | null | null | http://arxiv.org/pdf/2405.11848v1 | 2024-05-20T07:47:06Z | 2024-05-20T07:47:06Z | Alternators For Sequence Modeling | This paper introduces alternators, a novel family of non-Markovian dynamical models for sequences. An alternator features two neural networks: the observation trajectory network (OTN) and the feature trajectory network (FTN). The OTN and the FTN work in conjunction, alternating between outputting samples in the observation space and some feature space, respectively, over a cycle. The parameters of the OTN and the FTN are not time-dependent and are learned via a minimum cross-entropy criterion over the trajectories. Alternators are versatile. They can be used as dynamical latent-variable generative models or as sequence-to-sequence predictors. When alternators are used as generative models, the FTN produces interpretable low-dimensional latent variables that capture the dynamics governing the observations. When alternators are used as sequence-to-sequence predictors, the FTN learns to predict the observed features. In both cases, the OTN learns to produce sequences that match the data. Alternators can uncover the latent dynamics underlying complex sequential data, accurately forecast and impute missing data, and sample new trajectories. We showcase the capabilities of alternators in three applications. We first used alternators to model the Lorenz equations, often used to describe chaotic behavior. We then applied alternators to Neuroscience, to map brain activity to physical activity. Finally, we applied alternators to Climate Science, focusing on sea-surface temperature forecasting. In all our experiments, we found alternators are stable to train, fast to sample from, yield high-quality generated samples and latent variables, and outperform strong baselines such as neural ODEs and diffusion models in the domains we studied. | [
"['Mohammad Reza Rezaei' 'Adji Bousso Dieng']"
] |
null | null | 2405.11867 | null | null | http://arxiv.org/pdf/2405.11867v1 | 2024-05-20T08:19:08Z | 2024-05-20T08:19:08Z | Depth Prompting for Sensor-Agnostic Depth Estimation | Dense depth maps have been used as a key element of visual perception tasks. There have been tremendous efforts to enhance the depth quality, ranging from optimization-based to learning-based methods. Despite the remarkable progress for a long time, their applicability in the real world is limited due to systematic measurement biases such as density, sensing pattern, and scan range. It is well-known that the biases make it difficult for these methods to achieve their generalization. We observe that learning a joint representation for input modalities (e.g., images and depth), which most recent methods adopt, is sensitive to the biases. In this work, we disentangle those modalities to mitigate the biases with prompt engineering. For this, we design a novel depth prompt module to allow the desirable feature representation according to new depth distributions from either sensor types or scene configurations. Our depth prompt can be embedded into foundation models for monocular depth estimation. Through this embedding process, our method helps the pretrained model to be free from restraint of depth scan range and to provide absolute scale depth maps. We demonstrate the effectiveness of our method through extensive evaluations. Source code is publicly available at https://github.com/JinhwiPark/DepthPrompting . | [
"['Jin-Hwi Park' 'Chanhwi Jeong' 'Junoh Lee' 'Hae-Gon Jeon']"
] |
null | null | 2405.11868 | null | null | http://arxiv.org/pdf/2405.11868v1 | 2024-05-20T08:19:10Z | 2024-05-20T08:19:10Z | Towards Graph Contrastive Learning: A Survey and Beyond | In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive nature. To address this challenge, self-supervised learning (SSL) on graphs has gained increasing attention and has made significant progress. SSL enables machine learning models to produce informative representations from unlabeled graph data, reducing the reliance on expensive labeled data. While SSL on graphs has witnessed widespread adoption, one critical component, Graph Contrastive Learning (GCL), has not been thoroughly investigated in the existing literature. Thus, this survey aims to fill this gap by offering a dedicated survey on GCL. We provide a comprehensive overview of the fundamental principles of GCL, including data augmentation strategies, contrastive modes, and contrastive optimization objectives. Furthermore, we explore the extensions of GCL to other aspects of data-efficient graph learning, such as weakly supervised learning, transfer learning, and related scenarios. We also discuss practical applications spanning domains such as drug discovery, genomics analysis, recommender systems, and finally outline the challenges and potential future directions in this field. | [
"['Wei Ju' 'Yifan Wang' 'Yifang Qin' 'Zhengyang Mao' 'Zhiping Xiao'\n 'Junyu Luo' 'Junwei Yang' 'Yiyang Gu' 'Dongjie Wang' 'Qingqing Long'\n 'Siyu Yi' 'Xiao Luo' 'Ming Zhang']"
] |
null | null | 2405.11877 | null | null | http://arxiv.org/pdf/2405.11877v3 | 2024-05-22T19:14:51Z | 2024-05-20T08:41:15Z | A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI:
The First Romanian Natural Language Inference Corpus | Natural language inference (NLI), the task of recognizing the entailment relationship in sentence pairs, is an actively studied topic serving as a proxy for natural language understanding. Despite the relevance of the task in building conversational agents and improving text classification, machine translation and other NLP tasks, to the best of our knowledge, there is no publicly available NLI corpus for the Romanian language. To this end, we introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs, which are obtained via distant supervision, and 6K validation and test sentence pairs, which are manually annotated with the correct labels. We conduct experiments with multiple machine learning methods based on distant learning, ranging from shallow models based on word embeddings to transformer-based neural networks, to establish a set of competitive baselines. Furthermore, we improve on the best model by employing a new curriculum learning strategy based on data cartography. Our dataset and code to reproduce the baselines are available at https://github.com/Eduard6421/RONLI. | [
"['Eduard Poesina' 'Cornelia Caragea' 'Radu Tudor Ionescu']"
] |
null | null | 2405.11880 | null | null | http://arxiv.org/pdf/2405.11880v1 | 2024-05-20T08:51:03Z | 2024-05-20T08:51:03Z | Quantifying In-Context Reasoning Effects and Memorization Effects in
LLMs | In this study, we propose an axiomatic system to define and quantify the precise memorization and in-context reasoning effects used by the large language model (LLM) for language generation. These effects are formulated as non-linear interactions between tokens/words encoded by the LLM. Specifically, the axiomatic system enables us to categorize the memorization effects into foundational memorization effects and chaotic memorization effects, and further classify in-context reasoning effects into enhanced inference patterns, eliminated inference patterns, and reversed inference patterns. Besides, the decomposed effects satisfy the sparsity property and the universal matching property, which mathematically guarantee that the LLM's confidence score can be faithfully decomposed into the memorization effects and in-context reasoning effects. Experiments show that the clear disentanglement of memorization effects and in-context reasoning effects enables a straightforward examination of detailed inference patterns encoded by LLMs. | [
"['Siyu Lou' 'Yuntian Chen' 'Xiaodan Liang' 'Liang Lin' 'Quanshi Zhang']"
] |
null | null | 2405.11881 | null | null | http://arxiv.org/pdf/2405.11881v1 | 2024-05-20T08:54:03Z | 2024-05-20T08:54:03Z | Out-of-Distribution Detection with a Single Unconditional Diffusion
Model | Out-of-distribution (OOD) detection is a critical task in machine learning that seeks to identify abnormal samples. Traditionally, unsupervised methods utilize a deep generative model for OOD detection. However, such approaches necessitate a different model when evaluating abnormality against a new distribution. With the emergence of foundational generative models, this paper explores whether a single generalist model can also perform OOD detection across diverse tasks. To that end, we introduce our method, Diffusion Paths, (DiffPath) in this work. DiffPath proposes to utilize a single diffusion model originally trained to perform unconditional generation for OOD detection. Specifically, we introduce a novel technique of measuring the rate-of-change and curvature of the diffusion paths connecting samples to the standard normal. Extensive experiments show that with a single model, DiffPath outperforms prior work on a variety of OOD tasks involving different distributions. Our code is publicly available at https://github.com/clear-nus/diffpath. | [
"['Alvin Heng' 'Alexandre H. Thiery' 'Harold Soh']"
] |
null | null | 2405.11884 | null | null | http://arxiv.org/pdf/2405.11884v2 | 2024-05-21T07:46:03Z | 2024-05-20T08:57:39Z | Vertical Federated Learning Hybrid Local Pre-training | Vertical Federated Learning (VFL), which has a broad range of real-world applications, has received much attention in both academia and industry. Enterprises aspire to exploit more valuable features of the same users from diverse departments to boost their model prediction skills. VFL addresses this demand and concurrently secures individual parties from exposing their raw data. However, conventional VFL encounters a bottleneck as it only leverages aligned samples, whose size shrinks with more parties involved, resulting in data scarcity and the waste of unaligned data. To address this problem, we propose a novel VFL Hybrid Local Pre-training (VFLHLP) approach. VFLHLP first pre-trains local networks on the local data of participating parties. Then it utilizes these pre-trained networks to adjust the sub-model for the labeled party or enhance representation learning for other parties during downstream federated learning on aligned data, boosting the performance of federated models. The experimental results on real-world advertising datasets, demonstrate that our approach achieves the best performance over baseline methods by large margins. The ablation study further illustrates the contribution of each technique in VFLHLP to its overall performance. | [
"['Wenguo Li' 'Xinling Guo' 'Xu Jiao' 'Tiancheng Huang' 'Xiaoran Yan'\n 'Yao Yang']"
] |
null | null | 2405.11895 | null | null | http://arxiv.org/pdf/2405.11895v1 | 2024-05-20T09:28:23Z | 2024-05-20T09:28:23Z | Sparse Attention-driven Quality Prediction for Production Process
Optimization in Digital Twins | In the process industry, optimizing production lines for long-term efficiency requires real-time monitoring and analysis of operation states to fine-tune production line parameters. However, the complexity in operational logic and the intricate coupling of production process parameters make it difficult to develop an accurate mathematical model for the entire process, thus hindering the deployment of efficient optimization mechanisms. In view of these difficulties, we propose to deploy a digital twin of the production line by digitally abstracting its physical layout and operational logic. By iteratively mapping the real-world data reflecting equipment operation status and product quality inspection in the digital twin, we adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks. This model enables the data-driven state evolution of the digital twin. The digital twin takes a role of aggregating the information of actual operating conditions and the results of quality-sensitive analysis, which facilitates the optimization of process production quality with virtual-reality evolution under multi-dimensional constraints. Leveraging the digital twin model as an information-flow carrier, we extract temporal features from key process indicators and establish a production process quality prediction model based on the proposed composite neural network. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed digital twin-based production process optimization method fosters seamless integration between virtual and real production lines. This integration achieves an average operating status prediction accuracy of over 98% and near-optimal production process control. | [
"['Yanlei Yin' 'Lihua Wang' 'Wenbo Wang' 'Dinh Thai Hoang']"
] |
null | null | 2405.11907 | null | null | http://arxiv.org/pdf/2405.11907v2 | 2024-05-21T08:27:26Z | 2024-05-20T09:42:44Z | Ensemble and Mixture-of-Experts DeepONets For Operator Learning | We present a novel deep operator network (DeepONet) architecture for operator learning, the ensemble DeepONet, that allows for enriching the trunk network of a single DeepONet with multiple distinct trunk networks. This trunk enrichment allows for greater expressivity and generalization capabilities over a range of operator learning problems. We also present a spatial mixture-of-experts (MoE) DeepONet trunk network architecture that utilizes a partition-of-unity (PoU) approximation to promote spatial locality and model sparsity in the operator learning problem. We first prove that both the ensemble and PoU-MoE DeepONets are universal approximators. We then demonstrate that ensemble DeepONets containing a trunk ensemble of a standard trunk, the PoU-MoE trunk, and/or a proper orthogonal decomposition (POD) trunk can achieve 2-4x lower relative $ell_2$ errors than standard DeepONets and POD-DeepONets on both standard and challenging new operator learning problems involving partial differential equations (PDEs) in two and three dimensions. Our new PoU-MoE formulation provides a natural way to incorporate spatial locality and model sparsity into any neural network architecture, while our new ensemble DeepONet provides a powerful and general framework for incorporating basis enrichment in scientific machine learning architectures for operator learning. | [
"['Ramansh Sharma' 'Varun Shankar']"
] |
null | null | 2405.11911 | null | null | http://arxiv.org/pdf/2405.11911v1 | 2024-05-20T09:47:22Z | 2024-05-20T09:47:22Z | PULL: PU-Learning-based Accurate Link Prediction | Given an edge-incomplete graph, how can we accurately find the missing links? The link prediction in edge-incomplete graphs aims to discover the missing relations between entities when their relationships are represented as a graph. Edge-incomplete graphs are prevalent in real-world due to practical limitations, such as not checking all users when adding friends in a social network. Addressing the problem is crucial for various tasks, including recommending friends in social networks and finding references in citation networks. However, previous approaches rely heavily on the given edge-incomplete (observed) graph, making it challenging to consider the missing (unobserved) links during training. In this paper, we propose PULL (PU-Learning-based Link predictor), an accurate link prediction method based on the positive-unlabeled (PU) learning. PULL treats the observed edges in the training graph as positive examples, and the unconnected node pairs as unlabeled ones. PULL effectively prevents the link predictor from overfitting to the observed graph by proposing latent variables for every edge, and leveraging the expected graph structure with respect to the variables. Extensive experiments on five real-world datasets show that PULL consistently outperforms the baselines for predicting links in edge-incomplete graphs. | [
"['Junghun Kim' 'Ka Hyun Park' 'Hoyoung Yoon' 'U Kang']"
] |
null | null | 2405.11916 | null | null | http://arxiv.org/pdf/2405.11916v3 | 2024-05-22T04:04:17Z | 2024-05-20T09:52:31Z | Information Leakage from Embedding in Large Language Models | The widespread adoption of large language models (LLMs) has raised concerns regarding data privacy. This study aims to investigate the potential for privacy invasion through input reconstruction attacks, in which a malicious model provider could potentially recover user inputs from embeddings. We first propose two base methods to reconstruct original texts from a model's hidden states. We find that these two methods are effective in attacking the embeddings from shallow layers, but their effectiveness decreases when attacking embeddings from deeper layers. To address this issue, we then present Embed Parrot, a Transformer-based method, to reconstruct input from embeddings in deep layers. Our analysis reveals that Embed Parrot effectively reconstructs original inputs from the hidden states of ChatGLM-6B and Llama2-7B, showcasing stable performance across various token lengths and data distributions. To mitigate the risk of privacy breaches, we introduce a defense mechanism to deter exploitation of the embedding reconstruction process. Our findings emphasize the importance of safeguarding user privacy in distributed learning systems and contribute valuable insights to enhance the security protocols within such environments. | [
"['Zhipeng Wan' 'Anda Cheng' 'Yinggui Wang' 'Lei Wang']"
] |
null | null | 2405.11919 | null | null | http://arxiv.org/pdf/2405.11919v2 | 2024-05-29T06:43:37Z | 2024-05-20T09:57:29Z | On Efficient and Statistical Quality Estimation for Data Annotation | Annotated datasets are an essential ingredient to train, evaluate, compare and productionalize supervised machine learning models. It is therefore imperative that annotations are of high quality. For their creation, good quality management and thereby reliable quality estimates are needed. Then, if quality is insufficient during the annotation process, rectifying measures can be taken to improve it. Quality estimation is often performed by having experts manually label instances as correct or incorrect. But checking all annotated instances tends to be expensive. Therefore, in practice, usually only subsets are inspected; sizes are chosen mostly without justification or regard to statistical power and more often than not, are relatively small. Basing estimates on small sample sizes, however, can lead to imprecise values for the error rate. Using unnecessarily large sample sizes costs money that could be better spent, for instance on more annotations. Therefore, we first describe in detail how to use confidence intervals for finding the minimal sample size needed to estimate the annotation error rate. Then, we propose applying acceptance sampling as an alternative to error rate estimation We show that acceptance sampling can reduce the required sample sizes up to 50% while providing the same statistical guarantees. | [
"['Jan-Christoph Klie' 'Juan Haladjian' 'Marc Kirchner' 'Rahul Nair']"
] |
null | null | 2405.11922 | null | null | http://arxiv.org/pdf/2405.11922v1 | 2024-05-20T09:58:27Z | 2024-05-20T09:58:27Z | Effective Clustering on Large Attributed Bipartite Graphs | Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, such as customer-product purchase networks and author-paper authorship graphs. Partitioning the target node set in such graphs into k disjoint clusters (referred to as k-ABGC) finds widespread use in various domains, including social network analysis, recommendation systems, information retrieval, and bioinformatics. However, the majority of existing solutions towards k-ABGC either overlook attribute information or fail to capture bipartite graph structures accurately, engendering severely compromised result quality. The severity of these issues is accentuated in real ABGs, which often encompass millions of nodes and a sheer volume of attribute data, rendering effective k-ABGC over such graphs highly challenging. In this paper, we propose TPO, an effective and efficient approach to k-ABGC that achieves superb clustering performance on multiple real datasets. TPO obtains high clustering quality through two major contributions: (i) a novel formulation and transformation of the k-ABGC problem based on multi-scale attribute affinity specialized for capturing attribute affinities between nodes with the consideration of their multi-hop connections in ABGs, and (ii) a highly efficient solver that includes a suite of carefully-crafted optimizations for sidestepping explicit affinity matrix construction and facilitating faster convergence. Extensive experiments, comparing TPO against 19 baselines over 5 real ABGs, showcase the superior clustering quality of TPO measured against ground-truth labels. Moreover, compared to the state of the arts, TPO is often more than 40x faster over both small and large ABGs. | [
"['Renchi Yang' 'Yidu Wu' 'Xiaoyang Lin' 'Qichen Wang' 'Tsz Nam Chan'\n 'Jieming Shi']"
] |
null | null | 2405.11930 | null | null | http://arxiv.org/pdf/2405.11930v2 | 2024-06-03T05:21:54Z | 2024-05-20T10:12:23Z | Data Contamination Calibration for Black-box LLMs | The rapid advancements of Large Language Models (LLMs) tightly associate with the expansion of the training data size. However, the unchecked ultra-large-scale training sets introduce a series of potential risks like data contamination, i.e. the benchmark data is used for training. In this work, we propose a holistic method named Polarized Augment Calibration (PAC) along with a new to-be-released dataset to detect the contaminated data and diminish the contamination effect. PAC extends the popular MIA (Membership Inference Attack) -- from machine learning community -- by forming a more global target at detecting training data to Clarify invisible training data. As a pioneering work, PAC is very much plug-and-play that can be integrated with most (if not all) current white- and black-box LLMs. By extensive experiments, PAC outperforms existing methods by at least 4.5%, towards data contamination detection on more 4 dataset formats, with more than 10 base LLMs. Besides, our application in real-world scenarios highlights the prominent presence of contamination and related issues. | [
"['Wentao Ye' 'Jiaqi Hu' 'Liyao Li' 'Haobo Wang' 'Gang Chen' 'Junbo Zhao']"
] |
null | null | 2405.11932 | null | null | http://arxiv.org/pdf/2405.11932v1 | 2024-05-20T10:16:26Z | 2024-05-20T10:16:26Z | Nonequilbrium physics of generative diffusion models | Generative diffusion models apply the concept of Langevin dynamics in physics to machine leaning, attracting a lot of interest from industrial application, but a complete picture about inherent mechanisms is still lacking. In this paper, we provide a transparent physics analysis of the diffusion models, deriving the fluctuation theorem, entropy production, Franz-Parisi potential to understand the intrinsic phase transitions discovered recently. Our analysis is rooted in non-equlibrium physics and concepts from equilibrium physics, i.e., treating both forward and backward dynamics as a Langevin dynamics, and treating the reverse diffusion generative process as a statistical inference, where the time-dependent state variables serve as quenched disorder studied in spin glass theory. This unified principle is expected to guide machine learning practitioners to design better algorithms and theoretical physicists to link the machine learning to non-equilibrium thermodynamics. | [
"['Zhendong Yu' 'Haiping Huang']"
] |
null | null | 2405.11937 | null | null | http://arxiv.org/pdf/2405.11937v1 | 2024-05-20T10:25:03Z | 2024-05-20T10:25:03Z | Chasing COMET: Leveraging Minimum Bayes Risk Decoding for Self-Improving
Machine Translation | This paper explores Minimum Bayes Risk (MBR) decoding for self-improvement in machine translation (MT), particularly for domain adaptation and low-resource languages. We implement the self-improvement process by fine-tuning the model on its MBR-decoded forward translations. By employing COMET as the MBR utility metric, we aim to achieve the reranking of translations that better aligns with human preferences. The paper explores the iterative application of this approach and the potential need for language-specific MBR utility metrics. The results demonstrate significant enhancements in translation quality for all examined language pairs, including successful application to domain-adapted models and generalisation to low-resource settings. This highlights the potential of COMET-guided MBR for efficient MT self-improvement in various scenarios. | [
"['Kamil Guttmann' 'Mikołaj Pokrywka' 'Adrian Charkiewicz'\n 'Artur Nowakowski']"
] |
null | null | 2405.11950 | null | null | http://arxiv.org/pdf/2405.11950v1 | 2024-05-20T10:54:47Z | 2024-05-20T10:54:47Z | WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models
for Lay Summarization of Scientific Articles | This paper details the efforts of the WisPerMed team in the BioLaySumm2024 Shared Task on automatic lay summarization in the biomedical domain, aimed at making scientific publications accessible to non-specialists. Large language models (LLMs), specifically the BioMistral and Llama3 models, were fine-tuned and employed to create lay summaries from complex scientific texts. The summarization performance was enhanced through various approaches, including instruction tuning, few-shot learning, and prompt variations tailored to incorporate specific context information. The experiments demonstrated that fine-tuning generally led to the best performance across most evaluated metrics. Few-shot learning notably improved the models' ability to generate relevant and factually accurate texts, particularly when using a well-crafted prompt. Additionally, a Dynamic Expert Selection (DES) mechanism to optimize the selection of text outputs based on readability and factuality metrics was developed. Out of 54 participants, the WisPerMed team reached the 4th place, measured by readability, factuality, and relevance. Determined by the overall score, our approach improved upon the baseline by approx. 5.5 percentage points and was only approx 1.5 percentage points behind the first place. | [
"['Tabea M. G. Pakull' 'Hendrik Damm' 'Ahmad Idrissi-Yaghir'\n 'Henning Schäfer' 'Peter A. Horn' 'Christoph M. Friedrich']"
] |
null | null | 2405.11951 | null | null | http://arxiv.org/pdf/2405.11951v1 | 2024-05-20T11:02:53Z | 2024-05-20T11:02:53Z | Distinguished In Uniform: Self Attention Vs. Virtual Nodes | Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to be universal function approximators, with two reservations: 1. The initial node features must be augmented with certain positional encodings. 2. The approximation is non-uniform: Graphs of different sizes may require a different approximating network. We first clarify that this form of universality is not unique to GTs: Using the same positional encodings, also pure MPGNNs and even 2-layer MLPs are non-uniform universal approximators. We then consider uniform expressivity: The target function is to be approximated by a single network for graphs of all sizes. There, we compare GTs to the more efficient MPGNN + Virtual Node architecture. The essential difference between the two model definitions is in their global computation method -- Self-Attention Vs Virtual Node. We prove that none of the models is a uniform-universal approximator, before proving our main result: Neither model's uniform expressivity subsumes the other's. We demonstrate the theory with experiments on synthetic data. We further augment our study with real-world datasets, observing mixed results which indicate no clear ranking in practice as well. | [
"['Eran Rosenbluth' 'Jan Tönshoff' 'Martin Ritzert' 'Berke Kisin'\n 'Martin Grohe']"
] |
null | null | 2405.11955 | null | null | http://arxiv.org/pdf/2405.11955v1 | 2024-05-20T11:21:23Z | 2024-05-20T11:21:23Z | Shallow Recurrent Decoder for Reduced Order Modeling of Plasma Dynamics | Reduced order models are becoming increasingly important for rendering complex and multiscale spatio-temporal dynamics computationally tractable. The computational efficiency of such surrogate models is especially important for design, exhaustive exploration and physical understanding. Plasma simulations, in particular those applied to the study of ${bf E}times {bf B}$ plasma discharges and technologies, such as Hall thrusters, require substantial computational resources in order to resolve the multidimentional dynamics that span across wide spatial and temporal scales. Although high-fidelity computational tools are available to simulate such systems over limited conditions and in highly simplified geometries, simulations of full-size systems and/or extensive parametric studies over many geometric configurations and under different physical conditions are computationally intractable with conventional numerical tools. Thus, scientific studies and industrially oriented modeling of plasma systems, including the important ${bf E}times {bf B}$ technologies, stand to significantly benefit from reduced order modeling algorithms. We develop a model reduction scheme based upon a {em Shallow REcurrent Decoder} (SHRED) architecture. The scheme uses a neural network for encoding limited sensor measurements in time (sequence-to-sequence encoding) to full state-space reconstructions via a decoder network. Based upon the theory of separation of variables, the SHRED architecture is capable of (i) reconstructing full spatio-temporal fields with as little as three point sensors, even the fields that are not measured with sensor feeds but that are in dynamic coupling with the measured field, and (ii) forecasting the future state of the system using neural network roll-outs from the trained time encoding model. | [
"['J. Nathan Kutz' 'Maryam Reza' 'Farbod Faraji' 'Aaron Knoll']"
] |
null | null | 2405.11958 | null | null | http://arxiv.org/pdf/2405.11958v1 | 2024-05-20T11:28:32Z | 2024-05-20T11:28:32Z | Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey
Analysis | This study presents insights gathered from surveys and discussions with specialists in three domains, aiming to find essential elements for a universal explanation framework that could be applied to these and other similar use cases. The insights are incorporated into a software tool that utilizes GP algorithms, known for their interpretability. The applications analyzed include a medical scenario (involving predictive ML), a retail use case (involving prescriptive ML), and an energy use case (also involving predictive ML). We interviewed professionals from each sector, transcribing their conversations for further analysis. Additionally, experts and non-experts in these fields filled out questionnaires designed to probe various dimensions of explanatory methods. The findings indicate a universal preference for sacrificing a degree of accuracy in favor of greater explainability. Additionally, we highlight the significance of feature importance and counterfactual explanations as critical components of such a framework. Our questionnaires are publicly available to facilitate the dissemination of knowledge in the field of XAI. | [
"['Eduard Barbu' 'Marharytha Domnich' 'Raul Vicente' 'Nikos Sakkas'\n 'André Morim']"
] |
null | null | 2405.11968 | null | null | http://arxiv.org/pdf/2405.11968v2 | 2024-06-05T18:17:51Z | 2024-05-20T11:47:31Z | Conditional Shift-Robust Conformal Prediction for Graph Neural Network | Graph Neural Networks (GNNs) have emerged as potent tools for predicting outcomes in graph-structured data. Despite their efficacy, a significant drawback of GNNs lies in their limited ability to provide robust uncertainty estimates, posing challenges to their reliability in contexts where errors carry significant consequences. Moreover, GNNs typically excel in in-distribution settings, assuming that training and test data follow identical distributions a condition often unmet in real world graph data scenarios. In this article, we leverage conformal prediction, a widely recognized statistical technique for quantifying uncertainty by transforming predictive model outputs into prediction sets, to address uncertainty quantification in GNN predictions amidst conditional shiftfootnote{Representing the change in conditional probability distribution (P(label|input)) from source domain to target domain.} in graph-based semi-supervised learning (SSL). Additionally, we propose a novel loss function aimed at refining model predictions by minimizing conditional shift in latent stages. Termed Conditional Shift Robust (CondSR) conformal prediction for GNNs, our approach CondSR is model-agnostic and adaptable to various classification models. We validate the effectiveness of our method on standard graph benchmark datasets, integrating it with state-of-the-art GNNs in node classification tasks. Comprehensive evaluations demonstrate that our approach consistently achieves any predefined target marginal coverage, enhances the accuracy of state of the art GNN models by up to 12% under conditional shift, and reduces the prediction set size by up to 48%. The code implementation is publicly available for further exploration and experimentation. | [
"['S. Akansha']"
] |
null | null | 2405.11982 | null | null | http://arxiv.org/pdf/2405.11982v1 | 2024-05-20T12:31:11Z | 2024-05-20T12:31:11Z | Robust Deep Reinforcement Learning with Adaptive Adversarial
Perturbations in Action Space | Deep reinforcement learning (DRL) algorithms can suffer from modeling errors between the simulation and the real world. Many studies use adversarial learning to generate perturbation during training process to model the discrepancy and improve the robustness of DRL. However, most of these approaches use a fixed parameter to control the intensity of the adversarial perturbation, which can lead to a trade-off between average performance and robustness. In fact, finding the optimal parameter of the perturbation is challenging, as excessive perturbations may destabilize training and compromise agent performance, while insufficient perturbations may not impart enough information to enhance robustness. To keep the training stable while improving robustness, we propose a simple but effective method, namely, Adaptive Adversarial Perturbation (A2P), which can dynamically select appropriate adversarial perturbations for each sample. Specifically, we propose an adaptive adversarial coefficient framework to adjust the effect of the adversarial perturbation during training. By designing a metric for the current intensity of the perturbation, our method can calculate the suitable perturbation levels based on the current relative performance. The appealing feature of our method is that it is simple to deploy in real-world applications and does not require accessing the simulator in advance. The experiments in MuJoCo show that our method can improve the training stability and learn a robust policy when migrated to different test environments. The code is available at https://github.com/Lqm00/A2P-SAC. | [
"['Qianmei Liu' 'Yufei Kuang' 'Jie Wang']"
] |
null | null | 2405.12001 | null | null | http://arxiv.org/pdf/2405.12001v1 | 2024-05-20T13:14:26Z | 2024-05-20T13:14:26Z | Scrutinize What We Ignore: Reining Task Representation Shift In
Context-Based Offline Meta Reinforcement Learning | Offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and meta-learning techniques. Previous context-based approaches predominantly rely on the intuition that maximizing the mutual information between the task and the task representation ($I(Z;M)$) can lead to performance improvements. Despite achieving attractive results, the theoretical justification of performance improvement for such intuition has been lacking. Motivated by the return discrepancy scheme in the model-based RL field, we find that maximizing $I(Z;M)$ can be interpreted as consistently raising the lower bound of the expected return for a given policy conditioning on the optimal task representation. However, this optimization process ignores the task representation shift between two consecutive updates, which may lead to performance improvement collapse. To address this problem, we turn to use the framework of performance difference bound to consider the impacts of task representation shift explicitly. We demonstrate that by reining the task representation shift, it is possible to achieve monotonic performance improvements, thereby showcasing the advantage against previous approaches. To make it practical, we design an easy yet highly effective algorithm RETRO (underline{RE}ining underline{T}ask underline{R}epresentation shift in context-based underline{O}ffline meta reinforcement learning) with only adding one line of code compared to the backbone. Empirical results validate its state-of-the-art (SOTA) asymptotic performance, training stability and training-time consumption on MuJoCo and MetaWorld benchmarks. | [
"['Hai Zhang' 'Boyuan Zheng' 'Anqi Guo' 'Tianying Ji' 'Pheng-Ann Heng'\n 'Junqiao Zhao' 'Lanqing Li']"
] |
null | null | 2405.12016 | null | null | http://arxiv.org/pdf/2405.12016v3 | 2024-07-07T14:48:38Z | 2024-05-20T13:39:58Z | Strategy-Proof Auctions through Conformal Prediction | Auctions are key for maximizing sellers' revenue and ensuring truthful bidding among buyers. Recently, an approach known as differentiable economics based on deep learning shows promise in learning optimal auction mechanisms for multiple items and participants. However, this approach has no guarantee of strategy-proofness at test time. Strategy-proofness is crucial as it ensures that buyers are incentivized to bid their true valuations, leading to optimal and fair auction outcomes without the risk of manipulation. Building on conformal prediction, we introduce a novel approach to achieve strategy-proofness with rigorous statistical guarantees. The key novelties of our method are: (i) the formulation of a regret prediction model, used to quantify at test time violations of strategy-proofness; and (ii) an auction acceptance rule that leverages the predicted regret to ensure that for a new auction, the data-driven mechanism meets the strategy-proofness requirement with high probability (e.g., 99%). Numerical experiments demonstrate the necessity for rigorous guarantees, the validity of our theoretical results, and the applicability of our proposed method. | [
"['Roy Maor Lotan' 'Inbal Talgam-Cohen' 'Yaniv Romano']"
] |
null | null | 2405.12038 | null | null | http://arxiv.org/pdf/2405.12038v2 | 2024-06-04T02:30:49Z | 2024-05-20T14:05:35Z | Adaptive Convolutional Forecasting Network Based on Time Series
Feature-Driven | Time series data in real-world scenarios contain a substantial amount of nonlinear information, which significantly interferes with the training process of models, leading to decreased prediction performance. Therefore, during the time series forecasting process, extracting the local and global time series patterns and understanding the potential nonlinear features among different time observations are highly significant. To address this challenge, we introduce multi-resolution convolution and deformable convolution operations. By enlarging the receptive field using convolution kernels with different dilation factors to capture temporal correlation information at different resolutions, and adaptively adjusting the sampling positions through additional offset vectors, we enhance the network's ability to capture potential nonlinear features among time observations. Building upon this, we propose ACNet, an adaptive convolutional network designed to effectively model the local and global temporal dependencies and the nonlinear features between observations in multivariate time series. Specifically, by extracting and fusing time series features at different resolutions, we capture both local contextual information and global patterns in the time series. The designed nonlinear feature adaptive extraction module captures the nonlinear features among different time observations in the time series. We evaluated the performance of ACNet across twelve real-world datasets. The results indicate that ACNet consistently achieves state-of-the-art performance in both short-term and long-term forecasting tasks with favorable runtime efficiency. | [
"['Dandan Zhang' 'Zhiqiang Zhang' 'Nanguang Chen' 'Yun Wang']"
] |
null | null | 2405.12046 | null | null | http://arxiv.org/pdf/2405.12046v1 | 2024-05-20T14:13:22Z | 2024-05-20T14:13:22Z | Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov
Optimization Approach | Federated learning (FL) has received significant attention in recent years for its advantages in efficient training of machine learning models across distributed clients without disclosing user-sensitive data. Specifically, in federated edge learning (FEEL) systems, the time-varying nature of wireless channels introduces inevitable system dynamics in the communication process, thereby affecting training latency and energy consumption. In this work, we further consider a streaming data scenario where new training data samples are randomly generated over time at edge devices. Our goal is to develop a dynamic scheduling and resource allocation algorithm to address the inherent randomness in data arrivals and resource availability under long-term energy constraints. To achieve this, we formulate a stochastic network optimization problem and use the Lyapunov drift-plus-penalty framework to obtain a dynamic resource management design. Our proposed algorithm makes adaptive decisions on device scheduling, computational capacity adjustment, and allocation of bandwidth and transmit power in every round. We provide convergence analysis for the considered setting with heterogeneous data and time-varying objective functions, which supports the rationale behind our proposed scheduling design. The effectiveness of our scheme is verified through simulation results, demonstrating improved learning performance and energy efficiency as compared to baseline schemes. | [
"['Chung-Hsuan Hu' 'Zheng Chen' 'Erik G. Larsson']"
] |
null | null | 2405.12052 | null | null | http://arxiv.org/pdf/2405.12052v1 | 2024-05-20T14:18:36Z | 2024-05-20T14:18:36Z | Parallelization of the K-Means Algorithm with Applications to Big Data
Clustering | The K-Means clustering using LLoyd's algorithm is an iterative approach to partition the given dataset into K different clusters. The algorithm assigns each point to the cluster based on the following objective function [ min Sigma_{i=1}^{n}||x_i-mu_{x_i}||^2] The serial algorithm involves iterative steps where we compute the distance of each datapoint from the centroids and assign the datapoint to the nearest centroid. This approach is essentially known as the expectation-maximization step. Clustering involves extensive computations to calculate distances at each iteration, which increases as the number of data points increases. This provides scope for parallelism. However, we must ensure that in a parallel process, each thread has access to the updated centroid value and no racing condition exists on any centroid values. We will compare two different approaches in this project. The first approach is an OpenMP flat synchronous method where all processes are run in parallel, and we use synchronization to ensure safe updates of clusters. The second approach we adopt is a GPU based parallelization approach using OpenACC wherein we will try to make use of GPU architecture to parallelize chunks of the algorithm to observe decreased computation time. We will analyze metrics such as speed up, efficiency,time taken with varying data points, and number of processes to compare the two approaches and understand the relative performance improvement we can get. | [
"['Ashish Srivastava' 'Mohammed Nawfal']"
] |
null | null | 2405.12085 | null | null | http://arxiv.org/pdf/2405.12085v1 | 2024-05-20T14:55:20Z | 2024-05-20T14:55:20Z | Noise-tolerant learnability of shallow quantum circuits from statistics
and the cost of quantum pseudorandomness | This work studies the learnability of unknown quantum circuits in the near term. We prove the natural robustness of quantum statistical queries for learning quantum processes and provide an efficient way to benchmark various classes of noise from statistics, which gives us a powerful framework for developing noise-tolerant algorithms. We adapt a learning algorithm for constant-depth quantum circuits to the quantum statistical query setting with a small overhead in the query complexity. We prove average-case lower bounds for learning random quantum circuits of logarithmic and higher depths within diamond distance with statistical queries. Additionally, we show the hardness of the quantum threshold search problem from quantum statistical queries and discuss its implications for the learnability of shallow quantum circuits. Finally, we prove that pseudorandom unitaries (PRUs) cannot be constructed using circuits of constant depth by constructing an efficient distinguisher and proving a new variation of the quantum no-free lunch theorem. | [
"['Chirag Wadhwa' 'Mina Doosti']"
] |
null | null | 2405.12087 | null | null | http://arxiv.org/pdf/2405.12087v1 | 2024-05-20T14:57:16Z | 2024-05-20T14:57:16Z | Channel Balance Interpolation in the Lightning Network via Machine
Learning | The Bitcoin Lightning Network is a Layer 2 payment protocol that addresses Bitcoin's scalability by facilitating quick and cost effective transactions through payment channels. This research explores the feasibility of using machine learning models to interpolate channel balances within the network, which can be used for optimizing the network's pathfinding algorithms. While there has been much exploration in balance probing and multipath payment protocols, predicting channel balances using solely node and channel features remains an uncharted area. This paper evaluates the performance of several machine learning models against two heuristic baselines and investigates the predictive capabilities of various features. Our model performs favorably in experimental evaluation, outperforming by 10% against an equal split baseline where both edges are assigned half of the channel capacity. | [
"['Vincent' 'Emanuele Rossi' 'Vikash Singh']"
] |
null | null | 2405.12094 | null | null | http://arxiv.org/pdf/2405.12094v1 | 2024-05-20T15:05:47Z | 2024-05-20T15:05:47Z | Is Mamba Compatible with Trajectory Optimization in Offline
Reinforcement Learning? | Transformer-based trajectory optimization methods have demonstrated exceptional performance in offline Reinforcement Learning (offline RL), yet it poses challenges due to substantial parameter size and limited scalability, which is particularly critical in sequential decision-making scenarios where resources are constrained such as in robots and drones with limited computational power. Mamba, a promising new linear-time sequence model, offers performance on par with transformers while delivering substantially fewer parameters on long sequences. As it remains unclear whether Mamba is compatible with trajectory optimization, this work aims to conduct comprehensive experiments to explore the potential of Decision Mamba in offline RL (dubbed DeMa) from the aspect of data structures and network architectures with the following insights: (1) Long sequences impose a significant computational burden without contributing to performance improvements due to the fact that DeMa's focus on sequences diminishes approximately exponentially. Consequently, we introduce a Transformer-like DeMa as opposed to an RNN-like DeMa. (2) For the components of DeMa, we identify that the hidden attention mechanism is key to its success, which can also work well with other residual structures and does not require position embedding. Extensive evaluations from eight Atari games demonstrate that our specially designed DeMa is compatible with trajectory optimization and surpasses previous state-of-the-art methods, outdoing Decision Transformer (DT) by 80% with 30% fewer parameters, and exceeds DT in MuJoCo with only a quarter of the parameters. | [
"['Yang Dai' 'Oubo Ma' 'Longfei Zhang' 'Xingxing Liang' 'Shengchao Hu'\n 'Mengzhu Wang' 'Shouling Ji' 'Jincai Huang' 'Li Shen']"
] |
null | null | 2405.12096 | null | null | http://arxiv.org/pdf/2405.12096v1 | 2024-05-20T15:06:36Z | 2024-05-20T15:06:36Z | PATE: Proximity-Aware Time series anomaly Evaluation | Evaluating anomaly detection algorithms in time series data is critical as inaccuracies can lead to flawed decision-making in various domains where real-time analytics and data-driven strategies are essential. Traditional performance metrics assume iid data and fail to capture the complex temporal dynamics and specific characteristics of time series anomalies, such as early and delayed detections. We introduce Proximity-Aware Time series anomaly Evaluation (PATE), a novel evaluation metric that incorporates the temporal relationship between prediction and anomaly intervals. PATE uses proximity-based weighting considering buffer zones around anomaly intervals, enabling a more detailed and informed assessment of a detection. Using these weights, PATE computes a weighted version of the area under the Precision and Recall curve. Our experiments with synthetic and real-world datasets show the superiority of PATE in providing more sensible and accurate evaluations than other evaluation metrics. We also tested several state-of-the-art anomaly detectors across various benchmark datasets using the PATE evaluation scheme. The results show that a common metric like Point-Adjusted F1 Score fails to characterize the detection performances well, and that PATE is able to provide a more fair model comparison. By introducing PATE, we redefine the understanding of model efficacy that steers future studies toward developing more effective and accurate detection models. | [
"['Ramin Ghorbani' 'Marcel J. T. Reinders' 'David M. J. Tax']"
] |
null | null | 2405.12122 | null | null | http://arxiv.org/pdf/2405.12122v1 | 2024-05-20T15:39:40Z | 2024-05-20T15:39:40Z | An Active Learning Framework with a Class Balancing Strategy for Time
Series Classification | Training machine learning models for classification tasks often requires labeling numerous samples, which is costly and time-consuming, especially in time series analysis. This research investigates Active Learning (AL) strategies to reduce the amount of labeled data needed for effective time series classification. Traditional AL techniques cannot control the selection of instances per class for labeling, leading to potential bias in classification performance and instance selection, particularly in imbalanced time series datasets. To address this, we propose a novel class-balancing instance selection algorithm integrated with standard AL strategies. Our approach aims to select more instances from classes with fewer labeled examples, thereby addressing imbalance in time series datasets. We demonstrate the effectiveness of our AL framework in selecting informative data samples for two distinct domains of tactile texture recognition and industrial fault detection. In robotics, our method achieves high-performance texture categorization while significantly reducing labeled training data requirements to 70%. We also evaluate the impact of different sliding window time intervals on robotic texture classification using AL strategies. In synthetic fiber manufacturing, we adapt AL techniques to address the challenge of fault classification, aiming to minimize data annotation cost and time for industries. We also address real-life class imbalances in the multiclass industrial anomalous dataset using our class-balancing instance algorithm integrated with AL strategies. Overall, this thesis highlights the potential of our AL framework across these two distinct domains. | [
"['Shemonto Das']"
] |
null | null | 2405.12126 | null | null | http://arxiv.org/pdf/2405.12126v1 | 2024-05-20T15:44:07Z | 2024-05-20T15:44:07Z | Alzheimer's Magnetic Resonance Imaging Classification Using Deep and
Meta-Learning Models | Deep learning, a cutting-edge machine learning approach, outperforms traditional machine learning in identifying intricate structures in complex high-dimensional data, particularly in the domain of healthcare. This study focuses on classifying Magnetic Resonance Imaging (MRI) data for Alzheimer's disease (AD) by leveraging deep learning techniques characterized by state-of-the-art CNNs. Brain imaging techniques such as MRI have enabled the measurement of pathophysiological brain changes related to Alzheimer's disease. Alzheimer's disease is the leading cause of dementia in the elderly, and it is an irreversible brain illness that causes gradual cognitive function disorder. In this paper, we train some benchmark deep models individually for the approach of the solution and later use an ensembling approach to combine the effect of multiple CNNs towards the observation of higher recall and accuracy. Here, the model's effectiveness is evaluated using various methods, including stacking, majority voting, and the combination of models with high recall values. The majority voting performs better than the alternative modelling approach as the majority voting approach typically reduces the variance in the predictions. We report a test accuracy of 90% with a precision score of 0.90 and a recall score of 0.89 in our proposed approach. In future, this study can be extended to incorporate other types of medical data, including signals, images, and other data. The same or alternative datasets can be used with additional classifiers, neural networks, and AI techniques to enhance Alzheimer's detection. | [
"['Nida Nasir' 'Muneeb Ahmed' 'Neda Afreen' 'Mustafa Sameer']"
] |
null | null | 2405.12130 | null | null | http://arxiv.org/pdf/2405.12130v1 | 2024-05-20T15:48:32Z | 2024-05-20T15:48:32Z | MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning | Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models. In this paper, we analyze the impact of low-rank updating, as implemented in LoRA. Our findings suggest that the low-rank updating mechanism may limit the ability of LLMs to effectively learn and memorize new knowledge. Inspired by this observation, we propose a new method called MoRA, which employs a square matrix to achieve high-rank updating while maintaining the same number of trainable parameters. To achieve it, we introduce the corresponding non-parameter operators to reduce the input dimension and increase the output dimension for the square matrix. Furthermore, these operators ensure that the weight can be merged back into LLMs, which makes our method can be deployed like LoRA. We perform a comprehensive evaluation of our method across five tasks: instruction tuning, mathematical reasoning, continual pretraining, memory and pretraining. Our method outperforms LoRA on memory-intensive tasks and achieves comparable performance on other tasks. | [
"['Ting Jiang' 'Shaohan Huang' 'Shengyue Luo' 'Zihan Zhang' 'Haizhen Huang'\n 'Furu Wei' 'Weiwei Deng' 'Feng Sun' 'Qi Zhang' 'Deqing Wang'\n 'Fuzhen Zhuang']"
] |
null | null | 2405.12150 | null | null | http://arxiv.org/pdf/2405.12150v1 | 2024-05-20T16:23:40Z | 2024-05-20T16:23:40Z | Bangladeshi Native Vehicle Detection in Wild | The success of autonomous navigation relies on robust and precise vehicle recognition, hindered by the scarcity of region-specific vehicle detection datasets, impeding the development of context-aware systems. To advance terrestrial object detection research, this paper proposes a native vehicle detection dataset for the most commonly appeared vehicle classes in Bangladesh. 17 distinct vehicle classes have been taken into account, with fully annotated 81542 instances of 17326 images. Each image width is set to at least 1280px. The dataset's average vehicle bounding box-to-image ratio is 4.7036. This Bangladesh Native Vehicle Dataset (BNVD) has accounted for several geographical, illumination, variety of vehicle sizes, and orientations to be more robust on surprised scenarios. In the context of examining the BNVD dataset, this work provides a thorough assessment with four successive You Only Look Once (YOLO) models, namely YOLO v5, v6, v7, and v8. These dataset's effectiveness is methodically evaluated and contrasted with other vehicle datasets already in use. The BNVD dataset exhibits mean average precision(mAP) at 50% intersection over union (IoU) is 0.848 corresponding precision and recall values of 0.841 and 0.774. The research findings indicate a mAP of 0.643 at an IoU range of 0.5 to 0.95. The experiments show that the BNVD dataset serves as a reliable representation of vehicle distribution and presents considerable complexities. | [
"['Bipin Saha' 'Md. Johirul Islam' 'Shaikh Khaled Mostaque'\n 'Aditya Bhowmik' 'Tapodhir Karmakar Taton' 'Md. Nakib Hayat Chowdhury'\n 'Mamun Bin Ibne Reaz']"
] |
null | null | 2405.12179 | null | null | http://arxiv.org/pdf/2405.12179v3 | 2024-05-31T18:29:13Z | 2024-05-20T17:06:24Z | TENNs-PLEIADES: Building Temporal Kernels with Orthogonal Polynomials | We introduce a neural network named PLEIADES (PoLynomial Expansion In Adaptive Distributed Event-based Systems), belonging to the TENNs (Temporal Neural Networks) architecture. We focus on interfacing these networks with event-based data to perform online spatiotemporal classification and detection with low latency. By virtue of using structured temporal kernels and event-based data, we have the freedom to vary the sample rate of the data along with the discretization step-size of the network without additional finetuning. We experimented with three event-based benchmarks and obtained state-of-the-art results on all three by large margins with significantly smaller memory and compute costs. We achieved: 1) 99.59% accuracy with 192K parameters on the DVS128 hand gesture recognition dataset and 100% with a small additional output filter; 2) 99.58% test accuracy with 277K parameters on the AIS 2024 eye tracking challenge; and 3) 0.556 mAP with 576k parameters on the PROPHESEE 1 Megapixel Automotive Detection Dataset. | [
"['Yan Ru Pei' 'Olivier Coenen']"
] |
null | null | 2405.12183 | null | null | http://arxiv.org/pdf/2405.12183v1 | 2024-05-20T17:09:58Z | 2024-05-20T17:09:58Z | Multi-order Graph Clustering with Adaptive Node-level Weight Learning | Current graph clustering methods emphasize individual node and edge con nections, while ignoring higher-order organization at the level of motif. Re cently, higher-order graph clustering approaches have been designed by motif based hypergraphs. However, these approaches often suffer from hypergraph fragmentation issue seriously, which degrades the clustering performance greatly. Moreover, real-world graphs usually contain diverse motifs, with nodes participating in multiple motifs. A key challenge is how to achieve precise clustering results by integrating information from multiple motifs at the node level. In this paper, we propose a multi-order graph clustering model (MOGC) to integrate multiple higher-order structures and edge connections at node level. MOGC employs an adaptive weight learning mechanism to au tomatically adjust the contributions of different motifs for each node. This not only tackles hypergraph fragmentation issue but enhances clustering accuracy. MOGC is efficiently solved by an alternating minimization algo rithm. Experiments on seven real-world datasets illustrate the effectiveness of MOGC. | [
"['Ye Liu' 'Xuelei Lin' 'Yejia Chen' 'Reynold Cheng']"
] |
null | null | 2405.12186 | null | null | http://arxiv.org/pdf/2405.12186v2 | 2024-05-21T04:26:45Z | 2024-05-20T17:17:44Z | Training Data Attribution via Approximate Unrolled Differentiation | Many training data attribution (TDA) methods aim to estimate how a model's behavior would change if one or more data points were removed from the training set. Methods based on implicit differentiation, such as influence functions, can be made computationally efficient, but fail to account for underspecification, the implicit bias of the optimization algorithm, or multi-stage training pipelines. By contrast, methods based on unrolling address these issues but face scalability challenges. In this work, we connect the implicit-differentiation-based and unrolling-based approaches and combine their benefits by introducing Source, an approximate unrolling-based TDA method that is computed using an influence-function-like formula. While being computationally efficient compared to unrolling-based approaches, Source is suitable in cases where implicit-differentiation-based approaches struggle, such as in non-converged models and multi-stage training pipelines. Empirically, Source outperforms existing TDA techniques in counterfactual prediction, especially in settings where implicit-differentiation-based approaches fall short. | [
"['Juhan Bae' 'Wu Lin' 'Jonathan Lorraine' 'Roger Grosse']"
] |
null | null | 2405.12203 | null | null | http://arxiv.org/pdf/2405.12203v2 | 2024-05-24T13:45:20Z | 2024-05-20T17:41:19Z | Accelerating Relative Entropy Coding with Space Partitioning | Relative entropy coding (REC) algorithms encode a random sample following a target distribution $Q$, using a coding distribution $P$ shared between the sender and receiver. Sadly, general REC algorithms suffer from prohibitive encoding times, at least on the order of $2^{D_{text{KL}}[Q||P]}$, and faster algorithms are limited to very specific settings. This work addresses this issue by introducing a REC scheme utilizing space partitioning to reduce runtime in practical scenarios. We provide theoretical analyses of our method and demonstrate its effectiveness with both toy examples and practical applications. Notably, our method successfully handles REC tasks with $D_{text{KL}}[Q||P]$ about three times greater than what previous methods can manage, and reduces the bitrate by approximately 5-15% in VAE-based lossless compression on MNIST and INR-based lossy compression on CIFAR-10, compared to previous methods, significantly improving the practicality of REC for neural compression. | [
"['Jiajun He' 'Gergely Flamich' 'José Miguel Hernández-Lobato']"
] |
null | null | 2405.12205 | null | null | http://arxiv.org/pdf/2405.12205v1 | 2024-05-20T17:45:26Z | 2024-05-20T17:45:26Z | Metacognitive Capabilities of LLMs: An Exploration in Mathematical
Problem Solving | Metacognitive knowledge refers to humans' intuitive knowledge of their own thinking and reasoning processes. Today's best LLMs clearly possess some reasoning processes. The paper gives evidence that they also have metacognitive knowledge, including ability to name skills and procedures to apply given a task. We explore this primarily in context of math reasoning, developing a prompt-guided interaction procedure to get a powerful LLM to assign sensible skill labels to math questions, followed by having it perform semantic clustering to obtain coarser families of skill labels. These coarse skill labels look interpretable to humans. To validate that these skill labels are meaningful and relevant to the LLM's reasoning processes we perform the following experiments. (a) We ask GPT-4 to assign skill labels to training questions in math datasets GSM8K and MATH. (b) When using an LLM to solve the test questions, we present it with the full list of skill labels and ask it to identify the skill needed. Then it is presented with randomly selected exemplar solved questions associated with that skill label. This improves accuracy on GSM8k and MATH for several strong LLMs, including code-assisted models. The methodology presented is domain-agnostic, even though this article applies it to math problems. | [
"['Aniket Didolkar' 'Anirudh Goyal' 'Nan Rosemary Ke' 'Siyuan Guo'\n 'Michal Valko' 'Timothy Lillicrap' 'Danilo Rezende' 'Yoshua Bengio'\n 'Michael Mozer' 'Sanjeev Arora']"
] |
null | null | 2405.12206 | null | null | http://arxiv.org/abs/2405.12206v1 | 2024-05-20T17:45:36Z | 2024-05-20T17:45:36Z | Modeling citation worthiness by using attention-based bidirectional long
short-term memory networks and interpretable models | Scientist learn early on how to cite scientific sources to support their claims. Sometimes, however, scientists have challenges determining where a citation should be situated -- or, even worse, fail to cite a source altogether. Automatically detecting sentences that need a citation (i.e., citation worthiness) could solve both of these issues, leading to more robust and well-constructed scientific arguments. Previous researchers have applied machine learning to this task but have used small datasets and models that do not take advantage of recent algorithmic developments such as attention mechanisms in deep learning. We hypothesize that we can develop significantly accurate deep learning architectures that learn from large supervised datasets constructed from open access publications. In this work, we propose a Bidirectional Long Short-Term Memory (BiLSTM) network with attention mechanism and contextual information to detect sentences that need citations. We also produce a new, large dataset (PMOA-CITE) based on PubMed Open Access Subset, which is orders of magnitude larger than previous datasets. Our experiments show that our architecture achieves state of the art performance on the standard ACL-ARC dataset ($F_{1}=0.507$) and exhibits high performance ($F_{1}=0.856$) on the new PMOA-CITE. Moreover, we show that it can transfer learning across these datasets. We further use interpretable models to illuminate how specific language is used to promote and inhibit citations. We discover that sections and surrounding sentences are crucial for our improved predictions. We further examined purported mispredictions of the model, and uncovered systematic human mistakes in citation behavior and source data. This opens the door for our model to check documents during pre-submission and pre-archival procedures. We make this new dataset, the code, and a web-based tool available to the community. | [
"['Tong Zeng' 'Daniel E. Acuna']"
] |
null | null | 2405.12207 | null | null | http://arxiv.org/pdf/2405.12207v1 | 2024-05-20T17:47:18Z | 2024-05-20T17:47:18Z | Optimistic Query Routing in Clustering-based Approximate Maximum Inner
Product Search | Clustering-based nearest neighbor search is a simple yet effective method in which data points are partitioned into geometric shards to form an index, and only a few shards are searched during query processing to find an approximate set of top-$k$ vectors. Even though the search efficacy is heavily influenced by the algorithm that identifies the set of shards to probe, it has received little attention in the literature. This work attempts to bridge that gap by studying the problem of routing in clustering-based maximum inner product search (MIPS). We begin by unpacking existing routing protocols and notice the surprising contribution of optimism. We then take a page from the sequential decision making literature and formalize that insight following the principle of ``optimism in the face of uncertainty.'' In particular, we present a new framework that incorporates the moments of the distribution of inner products within each shard to optimistically estimate the maximum inner product. We then present a simple instance of our algorithm that uses only the first two moments to reach the same accuracy as state-of-the-art routers such as scann by probing up to $50%$ fewer points on a suite of benchmark MIPS datasets. Our algorithm is also space-efficient: we design a sketch of the second moment whose size is independent of the number of points and in practice requires storing only $O(1)$ additional vectors per shard. | [
"['Sebastian Bruch' 'Aditya Krishnan' 'Franco Maria Nardini']"
] |
null | null | 2405.12213 | null | null | http://arxiv.org/pdf/2405.12213v2 | 2024-05-26T19:55:26Z | 2024-05-20T17:57:01Z | Octo: An Open-Source Generalist Robot Policy | Large policies pretrained on diverse robot datasets have the potential to transform robotic learning: instead of training new policies from scratch, such generalist robot policies may be finetuned with only a little in-domain data, yet generalize broadly. However, to be widely applicable across a range of robotic learning scenarios, environments, and tasks, such policies need to handle diverse sensors and action spaces, accommodate a variety of commonly used robotic platforms, and finetune readily and efficiently to new domains. In this work, we aim to lay the groundwork for developing open-source, widely applicable, generalist policies for robotic manipulation. As a first step, we introduce Octo, a large transformer-based policy trained on 800k trajectories from the Open X-Embodiment dataset, the largest robot manipulation dataset to date. It can be instructed via language commands or goal images and can be effectively finetuned to robot setups with new sensory inputs and action spaces within a few hours on standard consumer GPUs. In experiments across 9 robotic platforms, we demonstrate that Octo serves as a versatile policy initialization that can be effectively finetuned to new observation and action spaces. We also perform detailed ablations of design decisions for the Octo model, from architecture to training data, to guide future research on building generalist robot models. | [
"['Octo Model Team' 'Dibya Ghosh' 'Homer Walke' 'Karl Pertsch'\n 'Kevin Black' 'Oier Mees' 'Sudeep Dasari' 'Joey Hejna' 'Tobias Kreiman'\n 'Charles Xu' 'Jianlan Luo' 'You Liang Tan' 'Lawrence Yunliang Chen'\n 'Pannag Sanketi' 'Quan Vuong' 'Ted Xiao' 'Dorsa Sadigh' 'Chelsea Finn'\n 'Sergey Levine']"
] |
null | null | 2405.12217 | null | null | http://arxiv.org/pdf/2405.12217v1 | 2024-05-20T17:59:21Z | 2024-05-20T17:59:21Z | Adapting Large Multimodal Models to Distribution Shifts: The Role of
In-Context Learning | Recent studies indicate that large multimodal models (LMMs) are highly robust against natural distribution shifts, often surpassing previous baselines. Despite this, domain-specific adaptation is still necessary, particularly in specialized areas like healthcare. Due to the impracticality of fine-tuning LMMs given their vast parameter space, this work investigates in-context learning (ICL) as an effective alternative for enhancing LMMs' adaptability. We find that the success of ICL heavily relies on the choice of demonstration, mirroring challenges seen in large language models but introducing unique complexities for LMMs facing distribution shifts. Our study addresses this by evaluating an unsupervised ICL method, TopKNearestPR, which selects in-context examples through a nearest example search based on feature similarity. We uncover that its effectiveness is limited by the deficiencies of pre-trained vision encoders under distribution shift scenarios. To address these challenges, we propose InvariantSelectPR, a novel method leveraging Class-conditioned Contrastive Invariance (CCI) for more robust demonstration selection. Specifically, CCI enhances pre-trained vision encoders by improving their discriminative capabilities across different classes and ensuring invariance to domain-specific variations. This enhancement allows the encoders to effectively identify and retrieve the most informative examples, which are then used to guide LMMs in adapting to new query samples under varying distributions. Our experiments show that InvariantSelectPR substantially improves the adaptability of LMMs, achieving significant performance gains on benchmark datasets, with a 34.2%$uparrow$ accuracy increase in 7-shot on Camelyon17 and 16.9%$uparrow$ increase in 7-shot on HAM10000 compared to the baseline zero-shot performance. | [
"['Guanglin Zhou' 'Zhongyi Han' 'Shiming Chen' 'Biwei Huang' 'Liming Zhu'\n 'Salman Khan' 'Xin Gao' 'Lina Yao']"
] |
null | null | 2405.12221 | null | null | http://arxiv.org/pdf/2405.12221v1 | 2024-05-20T17:59:59Z | 2024-05-20T17:59:59Z | Images that Sound: Composing Images and Sounds on a Single Canvas | Spectrograms are 2D representations of sound that look very different from the images found in our visual world. And natural images, when played as spectrograms, make unnatural sounds. In this paper, we show that it is possible to synthesize spectrograms that simultaneously look like natural images and sound like natural audio. We call these spectrograms images that sound. Our approach is simple and zero-shot, and it leverages pre-trained text-to-image and text-to-spectrogram diffusion models that operate in a shared latent space. During the reverse process, we denoise noisy latents with both the audio and image diffusion models in parallel, resulting in a sample that is likely under both models. Through quantitative evaluations and perceptual studies, we find that our method successfully generates spectrograms that align with a desired audio prompt while also taking the visual appearance of a desired image prompt. Please see our project page for video results: https://ificl.github.io/images-that-sound/ | [
"['Ziyang Chen' 'Daniel Geng' 'Andrew Owens']"
] |
null | null | 2405.12225 | null | null | http://arxiv.org/pdf/2405.12225v1 | 2024-04-22T13:21:57Z | 2024-04-22T13:21:57Z | Unraveling the Autism spectrum heterogeneity: Insights from ABIDE I
Database using data/model-driven permutation testing approaches | Autism Spectrum Condition (ASC) is a neurodevelopmental condition characterized by impairments in communication, social interaction and restricted or repetitive behaviors. Extensive research has been conducted to identify distinctions between individuals with ASC and neurotypical individuals. However, limited attention has been given to comprehensively evaluating how variations in image acquisition protocols across different centers influence these observed differences. This analysis focuses on structural magnetic resonance imaging (sMRI) data from the Autism Brain Imaging Data Exchange I (ABIDE I) database, evaluating subjects' condition and individual centers to identify disparities between ASC and control groups. Statistical analysis, employing permutation tests, utilizes two distinct statistical mapping methods: Statistical Agnostic Mapping (SAM) and Statistical Parametric Mapping (SPM). Results reveal the absence of statistically significant differences in any brain region, attributed to factors such as limited sample sizes within certain centers, noise effects and the problem of multicentrism in a heterogeneous condition such as autism. This study indicates limitations in using the ABIDE I database to detect structural differences in the brain between neurotypical individuals and those diagnosed with ASC. Furthermore, results from the SAM mapping method show greater consistency with existing literature. | [
"['F. J. Alcaide' 'I. A. Illan' 'J. Ramirez' 'J. M. Gorriz']"
] |
null | null | 2405.12228 | null | null | http://arxiv.org/pdf/2405.12228v1 | 2024-05-08T03:01:05Z | 2024-05-08T03:01:05Z | Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement
Learning | Stochastic optimization algorithms, particularly stochastic policy gradient (SPG), report significant success in reinforcement learning (RL). Nevertheless, up to now, that how to speedily acquire an optimal solution for RL is still a challenge. To tackle this issue, this work develops a fast SPG algorithm from the perspective of utilizing a momentum, coined SPG-NM. Specifically, in SPG-NM, a novel type of the negative momentum (NM) technique is applied into the classical SPG algorithm. Different from the existing NM techniques, we have adopted a few hyper-parameters in our SPG-NM algorithm. Moreover, the computational complexity is nearly same as the modern SPG-type algorithms, e.g., accelerated policy gradient (APG), which equips SPG with Nesterov's accelerated gradient (NAG). We evaluate the resulting algorithm on two classical tasks, bandit setting and Markov decision process (MDP). Numerical results in different tasks demonstrate faster convergence rate of the resulting algorithm by comparing state-of-the-art algorithms, which confirm the positive impact of NM in accelerating SPG for RL. Also, numerical experiments under different settings confirm the robustness of our SPG-NM algorithm for some certain crucial hyper-parameters, which ride the user feel free in practice. | [
"['Haobin Zhang' 'Zhuang Yang']"
] |
null | null | 2405.12234 | null | null | http://arxiv.org/pdf/2405.12234v2 | 2024-05-27T13:52:39Z | 2024-05-14T02:38:49Z | Joint Prediction Regions for time-series models | Machine Learning algorithms are notorious for providing point predictions but not prediction intervals. There are many applications where one requires confidence in predictions and prediction intervals. Stringing together, these intervals give rise to joint prediction regions with the desired significance level. It is an easy task to compute Joint Prediction regions (JPR) when the data is IID. However, the task becomes overly difficult when JPR is needed for time series because of the dependence between the observations. This project aims to implement Wolf and Wunderli's method for constructing JPRs and compare it with other methods (e.g. NP heuristic, Joint Marginals). The method under study is based on bootstrapping and is applied to different datasets (Min Temp, Sunspots), using different predictors (e.g. ARIMA and LSTM). One challenge of applying the method under study is to derive prediction standard errors for models, it cannot be obtained analytically. A novel method to estimate prediction standard error for different predictors is also devised. Finally, the method is applied to a synthetic dataset to find empirical averages and empirical widths and the results from the Wolf and Wunderli paper are consolidated. The experimental results show a narrowing of width with strong predictors like neural nets, widening of width with increasing forecast horizon H and decreasing significance level alpha, controlling the width with parameter k in K-FWE, and loss of information using Joint Marginals. | [
"['Eshant English']"
] |
null | null | 2405.12235 | null | null | http://arxiv.org/pdf/2405.12235v4 | 2024-06-18T15:25:28Z | 2024-05-14T23:50:01Z | Hypergraph: A Unified and Uniform Definition with Application to
Chemical Hypergraph | The conventional definition of hypergraph has two major issues: (1) there is not a standard definition of directed hypergraph and (2) there is not a formal definition of nested hypergraph. To resolve these issues, we propose a new definition of hypergraph that unifies the concepts of undirected, directed and nested hypergraphs, and that is uniform in using hyperedge as a single construct for representing high-order correlations among things, i.e., nodes and hyperedges. Specifically, we define a hyperedge to be a simple hyperedge, a nesting hyperedge, or a directed hyperedge. With this new definition, a hypergraph is nested if it has nesting hyperedge(s), and is directed if it has directed hyperedge(s). Otherwise, a hypergraph is a simple hypergraph. The uniformity and power of this new definition, with visualization, should facilitate the use of hypergraph for representing (hierarchical) high-order correlations in general and chemical systems in particular. Graph has been widely used as a mathematical structure for machine learning on molecular structures and 3D molecular geometries. However, graph has a major limitation: it can represent only pairwise correlations between nodes. Hypergraph extends graph with high-order correlations among nodes. This extension is significant or essential for machine learning on chemical systems. For molecules, this is significant as it allows the direct, explicit representation of multicenter bonds and molecular substructures. For chemical reactions, this is essential since most chemical reactions involve multiple participants. We propose the use of chemical hypergraph, a multilevel hypergraph with simple, nesting and directed hyperedges, as a single mathematical structure for representing chemical systems. We apply the new definition of hypergraph to chemical hypergraph and, as simplified versions, molecular hypergraph and chemical reaction hypergraph. | [
"['Daniel T. Chang']"
] |
null | null | 2405.12236 | null | null | http://arxiv.org/pdf/2405.12236v1 | 2024-05-15T23:44:06Z | 2024-05-15T23:44:06Z | Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement
Learning | Real-time Internet of Things (IoT) applications require real-time support to handle the ever-growing demand for computing resources to process IoT workloads. Fog Computing provides high availability of such resources in a distributed manner. However, these resources must be efficiently managed to distribute unpredictable traffic demands among heterogeneous Fog resources. This paper proposes a fully distributed load-balancing solution with Multi-Agent Reinforcement Learning (MARL) that intelligently distributes IoT workloads to optimize the waiting time while providing fair resource utilization in the Fog network. These agents use transfer learning for life-long self-adaptation to dynamic changes in the environment. By leveraging distributed decision-making, MARL agents effectively minimize the waiting time compared to a single centralized agent solution and other baselines, enhancing end-to-end execution delay. Besides performance gain, a fully distributed solution allows for a global-scale implementation where agents can work independently in small collaboration regions, leveraging nearby local resources. Furthermore, we analyze the impact of a realistic frequency to observe the state of the environment, unlike the unrealistic common assumption in the literature of having observations readily available in real-time for every required action. The findings highlight the trade-off between realism and performance using an interval-based Gossip-based multi-casting protocol against assuming real-time observation availability for every generated workload. | [
"['Maad Ebrahim' 'Abdelhakim Hafid']"
] |
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