categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.10270 | null | null | http://arxiv.org/pdf/2406.10270v1 | 2024-06-11T17:08:16Z | 2024-06-11T17:08:16Z | A Conceptual Framework For Trie-Augmented Neural Networks (TANNS) | Trie-Augmented Neural Networks (TANNs) combine trie structures with neural networks, forming a hierarchical design that enhances decision-making transparency and efficiency in machine learning. This paper investigates the use of TANNs for text and document classification, applying Recurrent Neural Networks (RNNs) and Feed forward Neural Networks (FNNs). We evaluated TANNs on the 20 NewsGroup and SMS Spam Collection datasets, comparing their performance with traditional RNN and FFN Networks with and without dropout regularization. The results show that TANNs achieve similar or slightly better performance in text classification. The primary advantage of TANNs is their structured decision-making process, which improves interpretability. We discuss implementation challenges and practical limitations. Future work will aim to refine the TANNs architecture for more complex classification tasks. | [
"['Temitayo Adefemi']"
] |
null | null | 2406.10272 | null | null | http://arxiv.org/pdf/2406.10272v2 | 2024-06-18T10:41:48Z | 2024-06-11T19:04:29Z | Connected Speech-Based Cognitive Assessment in Chinese and English | We present a novel benchmark dataset and prediction tasks for investigating approaches to assess cognitive function through analysis of connected speech. The dataset consists of speech samples and clinical information for speakers of Mandarin Chinese and English with different levels of cognitive impairment as well as individuals with normal cognition. These data have been carefully matched by age and sex by propensity score analysis to ensure balance and representativity in model training. The prediction tasks encompass mild cognitive impairment diagnosis and cognitive test score prediction. This framework was designed to encourage the development of approaches to speech-based cognitive assessment which generalise across languages. We illustrate it by presenting baseline prediction models that employ language-agnostic and comparable features for diagnosis and cognitive test score prediction. The models achieved unweighted average recall was 59.2% in diagnosis, and root mean squared error of 2.89 in score prediction. | [
"['Saturnino Luz' 'Sofia De La Fuente Garcia' 'Fasih Haider' 'Davida Fromm'\n 'Brian MacWhinney' 'Alyssa Lanzi' 'Ya-Ning Chang' 'Chia-Ju Chou'\n 'Yi-Chien Liu']"
] |
null | null | 2406.10279 | null | null | http://arxiv.org/pdf/2406.10279v1 | 2024-06-12T03:29:06Z | 2024-06-12T03:29:06Z | We Have a Package for You! A Comprehensive Analysis of Package
Hallucinations by Code Generating LLMs | The reliance of popular programming languages such as Python and JavaScript on centralized package repositories and open-source software, combined with the emergence of code-generating Large Language Models (LLMs), has created a new type of threat to the software supply chain: package hallucinations. These hallucinations, which arise from fact-conflicting errors when generating code using LLMs, represent a novel form of package confusion attack that poses a critical threat to the integrity of the software supply chain. This paper conducts a rigorous and comprehensive evaluation of package hallucinations across different programming languages, settings, and parameters, exploring how different configurations of LLMs affect the likelihood of generating erroneous package recommendations and identifying the root causes of this phenomena. Using 16 different popular code generation models, across two programming languages and two unique prompt datasets, we collect 576,000 code samples which we analyze for package hallucinations. Our findings reveal that 19.7% of generated packages across all the tested LLMs are hallucinated, including a staggering 205,474 unique examples of hallucinated package names, further underscoring the severity and pervasiveness of this threat. We also implemented and evaluated mitigation strategies based on Retrieval Augmented Generation (RAG), self-detected feedback, and supervised fine-tuning. These techniques demonstrably reduced package hallucinations, with hallucination rates for one model dropping below 3%. While the mitigation efforts were effective in reducing hallucination rates, our study reveals that package hallucinations are a systemic and persistent phenomenon that pose a significant challenge for code generating LLMs. | [
"['Joseph Spracklen' 'Raveen Wijewickrama' 'A H M Nazmus Sakib'\n 'Anindya Maiti' 'Murtuza Jadliwala']"
] |
null | null | 2406.10280 | null | null | http://arxiv.org/pdf/2406.10280v1 | 2024-06-12T05:09:58Z | 2024-06-12T05:09:58Z | Transferable Embedding Inversion Attack: Uncovering Privacy Risks in
Text Embeddings without Model Queries | This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous research requiring direct model access, we explore a more realistic threat model by developing a transfer attack method. This approach uses a surrogate model to mimic the victim model's behavior, allowing the attacker to infer sensitive information from text embeddings without direct access. Our experiments across various embedding models and a clinical dataset demonstrate that our transfer attack significantly outperforms traditional methods, revealing the potential privacy vulnerabilities in embedding technologies and emphasizing the need for enhanced security measures. | [
"['Yu-Hsiang Huang' 'Yuche Tsai' 'Hsiang Hsiao' 'Hong-Yi Lin' 'Shou-De Lin']"
] |
null | null | 2406.10281 | null | null | http://arxiv.org/pdf/2406.10281v1 | 2024-06-12T05:13:09Z | 2024-06-12T05:13:09Z | Watermarking Language Models with Error Correcting Codes | Recent progress in large language models enables the creation of realistic machine-generated content. Watermarking is a promising approach to distinguish machine-generated text from human text, embedding statistical signals in the output that are ideally undetectable to humans. We propose a watermarking framework that encodes such signals through an error correcting code. Our method, termed robust binary code (RBC) watermark, introduces no distortion compared to the original probability distribution, and no noticeable degradation in quality. We evaluate our watermark on base and instruction fine-tuned models and find our watermark is robust to edits, deletions, and translations. We provide an information-theoretic perspective on watermarking, a powerful statistical test for detection and for generating p-values, and theoretical guarantees. Our empirical findings suggest our watermark is fast, powerful, and robust, comparing favorably to the state-of-the-art. | [
"['Patrick Chao' 'Edgar Dobriban' 'Hamed Hassani']"
] |
null | null | 2406.10286 | null | null | http://arxiv.org/pdf/2406.10286v1 | 2024-06-12T11:16:30Z | 2024-06-12T11:16:30Z | Malicious URL Detection using optimized Hist Gradient Boosting
Classifier based on grid search method | Trusting the accuracy of data inputted on online platforms can be difficult due to the possibility of malicious websites gathering information for unlawful reasons. Analyzing each website individually becomes challenging with the presence of such malicious sites, making it hard to efficiently list all Uniform Resource Locators (URLs) on a blacklist. This ongoing challenge emphasizes the crucial need for strong security measures to safeguard against potential threats and unauthorized data collection. To detect the risk posed by malicious websites, it is proposed to utilize Machine Learning (ML)-based techniques. To this, we used several ML techniques such as Hist Gradient Boosting Classifier (HGBC), K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM) for detection of the benign and malicious website dataset. The dataset used contains 1781 records of malicious and benign website data with 13 features. First, we investigated missing value imputation on the dataset. Then, we normalized this data by scaling to a range of zero and one. Next, we utilized the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data since the data set was unbalanced. After that, we applied ML algorithms to the balanced training set. Meanwhile, all algorithms were optimized based on grid search. Finally, the models were evaluated based on accuracy, precision, recall, F1 score, and the Area Under the Curve (AUC) metrics. The results demonstrated that the HGBC classifier has the best performance in terms of the mentioned metrics compared to the other classifiers. | [
"['Mohammad Maftoun' 'Nima Shadkam' 'Seyedeh Somayeh Salehi Komamardakhi'\n 'Zulkefli Mansor' 'Javad Hassannataj Joloudari']"
] |
null | null | 2406.10288 | null | null | http://arxiv.org/pdf/2406.10288v2 | 2024-07-01T10:17:58Z | 2024-06-12T18:33:11Z | Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large
Language Models | Fine-tuning large language models on small, high-quality datasets can enhance their performance on specific downstream tasks. Recent research shows that fine-tuning on benign, instruction-following data can inadvertently undo the safety alignment process and increase a model's propensity to comply with harmful queries. Although critical, understanding and mitigating safety risks in well-defined tasks remains distinct from the instruction-following context due to structural differences in the data. Our work addresses the gap in our understanding of these risks across diverse types of data in closed models - where providers control how user data is utilized in the fine-tuning process. We demonstrate how malicious actors can subtly manipulate the structure of almost any task-specific dataset to foster significantly more dangerous model behaviors, while maintaining an appearance of innocuity and reasonable downstream task performance. To address this issue, we propose a novel mitigation strategy that mixes in safety data which mimics the task format and prompting style of the user data, showing this is more effective than existing baselines at re-establishing safety alignment while maintaining similar task performance. | [
"['Francisco Eiras' 'Aleksandar Petrov' 'Phillip H. S. Torr'\n 'M. Pawan Kumar' 'Adel Bibi']"
] |
null | null | 2406.10290 | null | null | http://arxiv.org/pdf/2406.10290v1 | 2024-06-12T22:58:12Z | 2024-06-12T22:58:12Z | MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases | The deployment of Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices has gained significant attention due to the benefits of enhanced privacy, stability, and personalization. However, the hardware constraints of mobile devices necessitate the use of models with fewer parameters and model compression techniques like quantization. Currently, there is limited understanding of quantization's impact on various task performances, including LLM tasks, LMM tasks, and, critically, trust and safety. There is a lack of adequate tools for systematically testing these models on mobile devices. To address these gaps, we introduce MobileAIBench, a comprehensive benchmarking framework for evaluating mobile-optimized LLMs and LMMs. MobileAIBench assesses models across different sizes, quantization levels, and tasks, measuring latency and resource consumption on real devices. Our two-part open-source framework includes a library for running evaluations on desktops and an iOS app for on-device latency and hardware utilization measurements. Our thorough analysis aims to accelerate mobile AI research and deployment by providing insights into the performance and feasibility of deploying LLMs and LMMs on mobile platforms. | [
"['Rithesh Murthy' 'Liangwei Yang' 'Juntao Tan' 'Tulika Manoj Awalgaonkar'\n 'Yilun Zhou' 'Shelby Heinecke' 'Sachin Desai' 'Jason Wu' 'Ran Xu'\n 'Sarah Tan' 'Jianguo Zhang' 'Zhiwei Liu' 'Shirley Kokane' 'Zuxin Liu'\n 'Ming Zhu' 'Huan Wang' 'Caiming Xiong' 'Silvio Savarese']"
] |
null | null | 2406.10292 | null | null | http://arxiv.org/pdf/2406.10292v1 | 2024-06-13T04:23:35Z | 2024-06-13T04:23:35Z | Automatically Labeling $200B Life-Saving Datasets: A Large Clinical
Trial Outcome Benchmark | The global cost of drug discovery and development exceeds $200 billion annually. The main results of drug discovery and development are the outcomes of clinical trials, which directly influence the regulatory approval of new drug candidates and ultimately affect patient outcomes. Despite their significance, large-scale, high-quality clinical trial outcome data are not readily available to the public. Suppose a large clinical trial outcome dataset is provided; machine learning researchers can potentially develop accurate prediction models using past trials and outcome labels, which could help prioritize and optimize therapeutic programs, ultimately benefiting patients. This paper introduces Clinical Trial Outcome (CTO) dataset, the largest trial outcome dataset with around 479K clinical trials, aggregating outcomes from multiple sources of weakly supervised labels, minimizing the noise from individual sources, and eliminating the need for human annotation. These sources include large language model (LLM) decisions on trial-related documents, news headline sentiments, stock prices of trial sponsors, trial linkages across phases, and other signals such as patient dropout rates and adverse events. CTO's labels show unprecedented agreement with supervised clinical trial outcome labels from test split of the supervised TOP dataset, with a 91 F1. | [
"['Chufan Gao' 'Jathurshan Pradeepkumar' 'Trisha Das' 'Shivashankar Thati'\n 'Jimeng Sun']"
] |
null | null | 2406.10294 | null | null | http://arxiv.org/pdf/2406.10294v1 | 2024-06-13T06:42:32Z | 2024-06-13T06:42:32Z | RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance | Recent advancements in Artificial Intelligence (AI), particularly the widespread adoption of Large Language Models (LLMs), have significantly enhanced text analysis capabilities. This technological evolution offers considerable promise for automating the review of scientific papers, a task traditionally managed through peer review by fellow researchers. Despite its critical role in maintaining research quality, the conventional peer-review process is often slow and subject to biases, potentially impeding the swift propagation of scientific knowledge. In this paper, we propose RelevAI-Reviewer, an automatic system that conceptualizes the task of survey paper review as a classification problem, aimed at assessing the relevance of a paper in relation to a specified prompt, analogous to a "call for papers". To address this, we introduce a novel dataset comprised of 25,164 instances. Each instance contains one prompt and four candidate papers, each varying in relevance to the prompt. The objective is to develop a machine learning (ML) model capable of determining the relevance of each paper and identifying the most pertinent one. We explore various baseline approaches, including traditional ML classifiers like Support Vector Machine (SVM) and advanced language models such as BERT. Preliminary findings indicate that the BERT-based end-to-end classifier surpasses other conventional ML methods in performance. We present this problem as a public challenge to foster engagement and interest in this area of research. | [
"['Paulo Henrique Couto' 'Quang Phuoc Ho' 'Nageeta Kumari'\n 'Benedictus Kent Rachmat' 'Thanh Gia Hieu Khuong' 'Ihsan Ullah'\n 'Lisheng Sun-Hosoya']"
] |
null | null | 2406.10300 | null | null | http://arxiv.org/pdf/2406.10300v1 | 2024-06-13T21:32:56Z | 2024-06-13T21:32:56Z | Large Language Models as Software Components: A Taxonomy for
LLM-Integrated Applications | Large Language Models (LLMs) have become widely adopted recently. Research explores their use both as autonomous agents and as tools for software engineering. LLM-integrated applications, on the other hand, are software systems that leverage an LLM to perform tasks that would otherwise be impossible or require significant coding effort. While LLM-integrated application engineering is emerging as new discipline, its terminology, concepts and methods need to be established. This study provides a taxonomy for LLM-integrated applications, offering a framework for analyzing and describing these systems. It also demonstrates various ways to utilize LLMs in applications, as well as options for implementing such integrations. Following established methods, we analyze a sample of recent LLM-integrated applications to identify relevant dimensions. We evaluate the taxonomy by applying it to additional cases. This review shows that applications integrate LLMs in numerous ways for various purposes. Frequently, they comprise multiple LLM integrations, which we term ``LLM components''. To gain a clear understanding of an application's architecture, we examine each LLM component separately. We identify thirteen dimensions along which to characterize an LLM component, including the LLM skills leveraged, the format of the output, and more. LLM-integrated applications are described as combinations of their LLM components. We suggest a concise representation using feature vectors for visualization. The taxonomy is effective for describing LLM-integrated applications. It can contribute to theory building in the nascent field of LLM-integrated application engineering and aid in developing such systems. Researchers and practitioners explore numerous creative ways to leverage LLMs in applications. Though challenges persist, integrating LLMs may revolutionize the way software systems are built. | [
"['Irene Weber']"
] |
null | null | 2406.10305 | null | null | http://arxiv.org/pdf/2406.10305v1 | 2024-06-14T03:39:01Z | 2024-06-14T03:39:01Z | Unlock the Correlation between Supervised Fine-Tuning and Reinforcement
Learning in Training Code Large Language Models | Automatic code generation has been a longstanding research topic. With the advancement of general-purpose large language models (LLMs), the ability to code stands out as one important measure to the model's reasoning performance. Usually, a two-stage training paradigm is implemented to obtain a Code LLM, namely the pretraining and the fine-tuning. Within the fine-tuning, supervised fine-tuning (SFT), and reinforcement learning (RL) are often used to improve the model's zero-shot ability. A large number of work has been conducted to improve the model's performance on code-related benchmarks with either modifications to the algorithm or refinement of the dataset. However, we still lack a deep insight into the correlation between SFT and RL. For instance, what kind of dataset should be used to ensure generalization, or what if we abandon the SFT phase in fine-tuning. In this work, we make an attempt to understand the correlation between SFT and RL. To facilitate our research, we manually craft 100 basis python functions, called atomic functions, and then a synthesizing pipeline is deployed to create a large number of synthetic functions on top of the atomic ones. In this manner, we ensure that the train and test sets remain distinct, preventing data contamination. Through comprehensive ablation study, we find: (1) Both atomic and synthetic functions are indispensable for SFT's generalization, and only a handful of synthetic functions are adequate; (2) Through RL, the SFT's generalization to target domain can be greatly enhanced, even with the same training prompts; (3) Training RL from scratch can alleviate the over-fitting issue introduced in the SFT phase. | [
"['Jie Chen' 'Xintian Han' 'Yu Ma' 'Xun Zhou' 'Liang Xiang']"
] |
null | null | 2406.10306 | null | null | http://arxiv.org/pdf/2406.10306v1 | 2024-06-14T04:07:37Z | 2024-06-14T04:07:37Z | A Simple, Solid, and Reproducible Baseline for Bridge Bidding AI | Contract bridge, a cooperative game characterized by imperfect information and multi-agent dynamics, poses significant challenges and serves as a critical benchmark in artificial intelligence (AI) research. Success in this domain requires agents to effectively cooperate with their partners. This study demonstrates that an appropriate combination of existing methods can perform surprisingly well in bridge bidding against WBridge5, a leading benchmark in the bridge bidding system and a multiple-time World Computer-Bridge Championship winner. Our approach is notably simple, yet it outperforms the current state-of-the-art methodologies in this field. Furthermore, we have made our code and models publicly available as open-source software. This initiative provides a strong starting foundation for future bridge AI research, facilitating the development and verification of new strategies and advancements in the field. | [
"['Haruka Kita' 'Sotetsu Koyamada' 'Yotaro Yamaguchi' 'Shin Ishii']"
] |
null | null | 2406.10314 | null | null | http://arxiv.org/pdf/2406.10314v1 | 2024-06-14T14:38:15Z | 2024-06-14T14:38:15Z | Development and Validation of a Machine Learning Algorithm for Clinical
Wellness Visit Classification in Cats and Dogs | Early disease detection in veterinary care relies on identifying subclinical abnormalities in asymptomatic animals during wellness visits. This study introduces an algorithm designed to distinguish between wellness and other veterinary visits.The purpose of this study is to validate the use of a visit classification algorithm compared to manual classification of veterinary visits by three board-certified veterinarians. Using a dataset of 11,105 clinical visits from 2012 to 2017 involving 655 animals (85.3% canines and 14.7% felines) across 544 U.S. veterinary establishments, the model was trained using a Gradient Boosting Machine model. Three validators were tasked with classifying 400 visits, including both wellness and other types of visits, selected randomly from the same database used for initial algorithm training, aiming to maintain consistency and relevance between the training and application phases; visit classifications were subsequently categorized into "wellness" or "other" based on majority consensus among validators to assess the algorithm's performance in identifying wellness visits. The algorithm demonstrated a specificity of 0.94 (95% CI: 0.91 to 0.96), implying its accuracy in distinguishing non-wellness visits. The algorithm had a sensitivity of 0.86 (95% CI: 0.80 to 0.92), indicating its ability to correctly identify wellness visits as compared to the annotations provided by veterinary experts. The balanced accuracy, calculated as 0.90 (95% CI: 0.87 to 0.93), further confirms the algorithm's overall effectiveness. The algorithm exhibits strong specificity and sensitivity, ensuring accurate identification of a high proportion of wellness visits. Overall, this algorithm holds promise for advancing research on preventive care's role in subclinical disease identification, but prospective studies are needed for validation. | [
"['Donald Szlosek' 'Michael Coyne' 'Julia Riggot' 'Kevin Knight'\n 'DJ McCrann' 'Dave Kincaid']"
] |
null | null | 2406.10322 | null | null | http://arxiv.org/pdf/2406.10322v1 | 2024-06-14T17:41:55Z | 2024-06-14T17:41:55Z | LieRE: Generalizing Rotary Position Encodings | While Rotary Position Embeddings (RoPE) for natural language performs well and has become widely adopted, its adoption for other modalities has been slower. Here, we introduce Lie group Relative position Encodings (LieRE) that goes beyond RoPE in supporting higher dimensional inputs. We evaluate the performance of LieRE on 2D and 3D image classification tasks and observe that LieRE leads to marked improvements in performance (up to 6%), training efficiency (3.5x reduction), data efficiency (30%) compared to the baselines of RoFormer, DeiT III, RoPE-Mixed and Vision-Llama | [
"['Sophie Ostmeier' 'Brian Axelrod' 'Michael E. Moseley' 'Akshay Chaudhari'\n 'Curtis Langlotz']"
] |
null | null | 2406.10324 | null | null | http://arxiv.org/pdf/2406.10324v1 | 2024-06-14T17:51:18Z | 2024-06-14T17:51:18Z | L4GM: Large 4D Gaussian Reconstruction Model | We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second. Key to our success is a novel dataset of multiview videos containing curated, rendered animated objects from Objaverse. This dataset depicts 44K diverse objects with 110K animations rendered in 48 viewpoints, resulting in 12M videos with a total of 300M frames. We keep our L4GM simple for scalability and build directly on top of LGM, a pretrained 3D Large Reconstruction Model that outputs 3D Gaussian ellipsoids from multiview image input. L4GM outputs a per-frame 3D Gaussian Splatting representation from video frames sampled at a low fps and then upsamples the representation to a higher fps to achieve temporal smoothness. We add temporal self-attention layers to the base LGM to help it learn consistency across time, and utilize a per-timestep multiview rendering loss to train the model. The representation is upsampled to a higher framerate by training an interpolation model which produces intermediate 3D Gaussian representations. We showcase that L4GM that is only trained on synthetic data generalizes extremely well on in-the-wild videos, producing high quality animated 3D assets. | [
"['Jiawei Ren' 'Kevin Xie' 'Ashkan Mirzaei' 'Hanxue Liang' 'Xiaohui Zeng'\n 'Karsten Kreis' 'Ziwei Liu' 'Antonio Torralba' 'Sanja Fidler'\n 'Seung Wook Kim' 'Huan Ling']"
] |
null | null | 2406.10325 | null | null | http://arxiv.org/pdf/2406.10325v1 | 2024-06-14T17:56:53Z | 2024-06-14T17:56:53Z | Enhancing Multilingual Voice Toxicity Detection with Speech-Text
Alignment | Toxicity classification for voice heavily relies on the semantic content of speech. We propose a novel framework that utilizes cross-modal learning to integrate the semantic embedding of text into a multilabel speech toxicity classifier during training. This enables us to incorporate textual information during training while still requiring only audio during inference. We evaluate this classifier on large-scale datasets with real-world characteristics to validate the effectiveness of this framework. Through ablation studies, we demonstrate that general-purpose semantic text embeddings are rich and aligned with speech for toxicity classification purposes. Conducting experiments across multiple languages at scale, we show improvements in voice toxicity classification across five languages and different toxicity categories. | [
"['Joseph Liu' 'Mahesh Kumar Nandwana' 'Janne Pylkkönen'\n 'Hannes Heikinheimo' 'Morgan McGuire']"
] |
null | null | 2406.10327 | null | null | http://arxiv.org/pdf/2406.10327v1 | 2024-06-14T17:59:25Z | 2024-06-14T17:59:25Z | Analysing Multi-Task Regression via Random Matrix Theory with
Application to Time Series Forecasting | In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensional, non-Gaussian data distributions. We formulate a multi-task optimization problem as a regularization technique to enable single-task models to leverage multi-task learning information. We derive a closed-form solution for multi-task optimization in the context of linear models. Our analysis provides valuable insights by linking the multi-task learning performance to various model statistics such as raw data covariances, signal-generating hyperplanes, noise levels, as well as the size and number of datasets. We finally propose a consistent estimation of training and testing errors, thereby offering a robust foundation for hyperparameter optimization in multi-task regression scenarios. Experimental validations on both synthetic and real-world datasets in regression and multivariate time series forecasting demonstrate improvements on univariate models, incorporating our method into the training loss and thus leveraging multivariate information. | [
"['Romain Ilbert' 'Malik Tiomoko' 'Cosme Louart' 'Ambroise Odonnat'\n 'Vasilii Feofanov' 'Themis Palpanas' 'Ievgen Redko']"
] |
null | null | 2406.10328 | null | null | http://arxiv.org/pdf/2406.10328v1 | 2024-06-14T17:59:53Z | 2024-06-14T17:59:53Z | From Pixels to Prose: A Large Dataset of Dense Image Captions | Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To bridge this gap, we introduce PixelProse, a comprehensive dataset of over 16M (million) synthetically generated captions, leveraging cutting-edge vision-language models for detailed and accurate descriptions. To ensure data integrity, we rigorously analyze our dataset for problematic content, including child sexual abuse material (CSAM), personally identifiable information (PII), and toxicity. We also provide valuable metadata such as watermark presence and aesthetic scores, aiding in further dataset filtering. We hope PixelProse will be a valuable resource for future vision-language research. PixelProse is available at https://huggingface.co/datasets/tomg-group-umd/pixelprose | [
"['Vasu Singla' 'Kaiyu Yue' 'Sukriti Paul' 'Reza Shirkavand'\n 'Mayuka Jayawardhana' 'Alireza Ganjdanesh' 'Heng Huang' 'Abhinav Bhatele'\n 'Gowthami Somepalli' 'Tom Goldstein']"
] |
null | null | 2406.10354 | null | null | http://arxiv.org/pdf/2406.10354v1 | 2024-06-14T18:04:06Z | 2024-06-14T18:04:06Z | SigDiffusions: Score-Based Diffusion Models for Long Time Series via
Log-Signature Embeddings | Score-based diffusion models have recently emerged as state-of-the-art generative models for a variety of data modalities. Nonetheless, it remains unclear how to adapt these models to generate long multivariate time series. Viewing a time series as the discretization of an underlying continuous process, we introduce SigDiffusion, a novel diffusion model operating on log-signature embeddings of the data. The forward and backward processes gradually perturb and denoise log-signatures preserving their algebraic structure. To recover a signal from its log-signature, we provide new closed-form inversion formulae expressing the coefficients obtained by expanding the signal in a given basis (e.g. Fourier or orthogonal polynomials) as explicit polynomial functions of the log-signature. Finally, we show that combining SigDiffusion with these inversion formulae results in highly realistic time series generation, competitive with the current state-of-the-art on various datasets of synthetic and real-world examples. | [
"['Barbora Barancikova' 'Zhuoyue Huang' 'Cristopher Salvi']"
] |
null | null | 2406.10366 | null | null | http://arxiv.org/pdf/2406.10366v1 | 2024-06-14T18:47:37Z | 2024-06-14T18:47:37Z | Improving the Validity and Practical Usefulness of AI/ML Evaluations
Using an Estimands Framework | Commonly, AI or machine learning (ML) models are evaluated on benchmark datasets. This practice supports innovative methodological research, but benchmark performance can be poorly correlated with performance in real-world applications -- a construct validity issue. To improve the validity and practical usefulness of evaluations, we propose using an estimands framework adapted from international clinical trials guidelines. This framework provides a systematic structure for inference and reporting in evaluations, emphasizing the importance of a well-defined estimation target. We illustrate our proposal on examples of commonly used evaluation methodologies - involving cross-validation, clustering evaluation, and LLM benchmarking - that can lead to incorrect rankings of competing models (rank reversals) with high probability, even when performance differences are large. We demonstrate how the estimands framework can help uncover underlying issues, their causes, and potential solutions. Ultimately, we believe this framework can improve the validity of evaluations through better-aligned inference, and help decision-makers and model users interpret reported results more effectively. | [
"['Olivier Binette' 'Jerome P. Reiter']"
] |
null | null | 2406.10367 | null | null | http://arxiv.org/pdf/2406.10367v1 | 2024-06-14T18:50:47Z | 2024-06-14T18:50:47Z | Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs | Heterogeneous graphs have attracted a lot of research interests recently due to the success for representing complex real-world systems. However, existing methods have two pain points in embedding them into low-dimensional spaces: the mixing of structural and semantic information, and the distributional mismatch between data and embedding spaces. These two challenges require representation methods to consider the global and partial data distributions while unmixing the information. Therefore, in this paper, we propose $text{Dis-H}^2text{GCN}$, a Disentangled Hyperbolic Heterogeneous Graph Convolutional Network. On the one hand, we leverage the mutual information minimization and discrimination maximization constraints to disentangle the semantic features from comprehensively learned representations by independent message propagation for each edge type, away from the pure structural features. On the other hand, the entire model is constructed upon the hyperbolic geometry to narrow the gap between data distributions and representing spaces. We evaluate our proposed $text{Dis-H}^2text{GCN}$ on five real-world heterogeneous graph datasets across two downstream tasks: node classification and link prediction. The results demonstrate its superiority over state-of-the-art methods, showcasing the effectiveness of our method in disentangling and representing heterogeneous graph data in hyperbolic spaces. | [
"['Qijie Bai' 'Changli Nie' 'Haiwei Zhang' 'Zhicheng Dou' 'Xiaojie Yuan']"
] |
null | null | 2406.10368 | null | null | http://arxiv.org/pdf/2406.10368v1 | 2024-06-14T18:52:34Z | 2024-06-14T18:52:34Z | A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts | The advent of powerful neural classifiers has increased interest in problems that require both learning and reasoning. These problems are critical for understanding important properties of models, such as trustworthiness, generalization, interpretability, and compliance to safety and structural constraints. However, recent research observed that tasks requiring both learning and reasoning on background knowledge often suffer from reasoning shortcuts (RSs): predictors can solve the downstream reasoning task without associating the correct concepts to the high-dimensional data. To address this issue, we introduce rsbench, a comprehensive benchmark suite designed to systematically evaluate the impact of RSs on models by providing easy access to highly customizable tasks affected by RSs. Furthermore, rsbench implements common metrics for evaluating concept quality and introduces novel formal verification procedures for assessing the presence of RSs in learning tasks. Using rsbench, we highlight that obtaining high quality concepts in both purely neural and neuro-symbolic models is a far-from-solved problem. rsbench is available at: https://unitn-sml.github.io/rsbench. | [
"['Samuele Bortolotti' 'Emanuele Marconato' 'Tommaso Carraro'\n 'Paolo Morettin' 'Emile van Krieken' 'Antonio Vergari' 'Stefano Teso'\n 'Andrea Passerini']"
] |
null | null | 2406.10391 | null | null | http://arxiv.org/pdf/2406.10391v1 | 2024-06-14T19:39:19Z | 2024-06-14T19:39:19Z | BEACON: Benchmark for Comprehensive RNA Tasks and Language Models | RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms. Despite the emergence of numerous deep learning approaches for RNA, particularly universal RNA language models, there remains a significant lack of standardized benchmarks to assess the effectiveness of these methods. In this study, we introduce the first comprehensive RNA benchmark BEACON (textbf{BE}nchmtextbf{A}rk for textbf{CO}mprehensive Rtextbf{N}A Task and Language Models). First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications, enabling a comprehensive assessment of the performance of methods on various RNA understanding tasks. Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models. Third, we investigate the vital RNA language model components from the tokenizer and positional encoding aspects. Notably, our findings emphasize the superiority of single nucleotide tokenization and the effectiveness of Attention with Linear Biases (ALiBi) over traditional positional encoding methods. Based on these insights, a simple yet strong baseline called BEACON-B is proposed, which can achieve outstanding performance with limited data and computational resources. The datasets and source code of our benchmark are available at https://github.com/terry-r123/RNABenchmark. | [
"['Yuchen Ren' 'Zhiyuan Chen' 'Lifeng Qiao' 'Hongtai Jing' 'Yuchen Cai'\n 'Sheng Xu' 'Peng Ye' 'Xinzhu Ma' 'Siqi Sun' 'Hongliang Yan' 'Dong Yuan'\n 'Wanli Ouyang' 'Xihui Liu']"
] |
null | null | 2406.10407 | null | null | http://arxiv.org/pdf/2406.10407v1 | 2024-06-14T20:31:22Z | 2024-06-14T20:31:22Z | Suboptimality bounds for trace-bounded SDPs enable a faster and scalable
low-rank SDP solver SDPLR+ | Semidefinite programs (SDPs) and their solvers are powerful tools with many applications in machine learning and data science. Designing scalable SDP solvers is challenging because by standard the positive semidefinite decision variable is an $n times n$ dense matrix, even though the input is often an $n times n$ sparse matrix. However, the information in the solution may not correspond to a full-rank dense matrix as shown by Bavinok and Pataki. Two decades ago, Burer and Monterio developed an SDP solver $texttt{SDPLR}$ that optimizes over a low-rank factorization instead of the full matrix. This greatly decreases the storage cost and works well for many problems. The original solver $texttt{SDPLR}$ tracks only the primal infeasibility of the solution, limiting the technique's flexibility to produce moderate accuracy solutions. We use a suboptimality bound for trace-bounded SDP problems that enables us to track the progress better and perform early termination. We then develop $texttt{SDPLR+}$, which starts the optimization with an extremely low-rank factorization and dynamically updates the rank based on the primal infeasibility and suboptimality. This further speeds up the computation and saves the storage cost. Numerical experiments on Max Cut, Minimum Bisection, Cut Norm, and Lov'{a}sz Theta problems with many recent memory-efficient scalable SDP solvers demonstrate its scalability up to problems with million-by-million decision variables and it is often the fastest solver to a moderate accuracy of $10^{-2}$. | [
"['Yufan Huang' 'David F. Gleich']"
] |
null | null | 2406.10416 | null | null | http://arxiv.org/pdf/2406.10416v4 | 2024-07-13T19:29:16Z | 2024-06-14T21:28:37Z | Byzantine-Robust Decentralized Federated Learning | Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system follows the server-assisted architecture (server-assisted FL), where the training process is coordinated by a central server. However, the server-assisted FL framework suffers from poor scalability due to a communication bottleneck at the server, and trust dependency issues. To address challenges, decentralized federated learning (DFL) architecture has been proposed to allow clients to train models collaboratively in a serverless and peer-to-peer manner. However, due to its fully decentralized nature, DFL is highly vulnerable to poisoning attacks, where malicious clients could manipulate the system by sending carefully-crafted local models to their neighboring clients. To date, only a limited number of Byzantine-robust DFL methods have been proposed, most of which are either communication-inefficient or remain vulnerable to advanced poisoning attacks. In this paper, we propose a new algorithm called BALANCE (Byzantine-robust averaging through local similarity in decentralization) to defend against poisoning attacks in DFL. In BALANCE, each client leverages its own local model as a similarity reference to determine if the received model is malicious or benign. We establish the theoretical convergence guarantee for BALANCE under poisoning attacks in both strongly convex and non-convex settings. Furthermore, the convergence rate of BALANCE under poisoning attacks matches those of the state-of-the-art counterparts in Byzantine-free settings. Extensive experiments also demonstrate that BALANCE outperforms existing DFL methods and effectively defends against poisoning attacks. | [
"['Minghong Fang' 'Zifan Zhang' 'Hairi' 'Prashant Khanduri' 'Jia Liu'\n 'Songtao Lu' 'Yuchen Liu' 'Neil Gong']"
] |
null | null | 2406.10417 | null | null | http://arxiv.org/pdf/2406.10417v1 | 2024-06-14T21:29:15Z | 2024-06-14T21:29:15Z | Enhanced Intrusion Detection System for Multiclass Classification in UAV
Networks | Unmanned Aerial Vehicles (UAVs) have become increasingly popular in various applications, especially with the emergence of 6G systems and networks. However, their widespread adoption has also led to concerns regarding security vulnerabilities, making the development of reliable intrusion detection systems (IDS) essential for ensuring UAVs safety and mission success. This paper presents a new IDS for UAV networks. A binary-tuple representation was used for encoding class labels, along with a deep learning-based approach employed for classification. The proposed system enhances the intrusion detection by capturing complex class relationships and temporal network patterns. Moreover, a cross-correlation study between common features of different UAVs was conducted to discard correlated features that might mislead the classification of the proposed IDS. The full study was carried out using the UAV-IDS-2020 dataset, and we assessed the performance of the proposed IDS using different evaluation metrics. The experimental results highlighted the effectiveness of the proposed multiclass classifier model with an accuracy of 95%. | [
"['Safaa Menssouri' 'Mamady Delamou' 'Khalil Ibrahimi' 'El Mehdi Amhoud']"
] |
null | null | 2406.10419 | null | null | http://arxiv.org/abs/2406.10419v1 | 2024-06-14T21:36:00Z | 2024-06-14T21:36:00Z | Learning Flexible Time-windowed Granger Causality Integrating
Heterogeneous Interventional Time Series Data | Granger causality, commonly used for inferring causal structures from time series data, has been adopted in widespread applications across various fields due to its intuitive explainability and high compatibility with emerging deep neural network prediction models. To alleviate challenges in better deciphering causal structures unambiguously from time series, the use of interventional data has become a practical approach. However, existing methods have yet to be explored in the context of imperfect interventions with unknown targets, which are more common and often more beneficial in a wide range of real-world applications. Additionally, the identifiability issues of Granger causality with unknown interventional targets in complex network models remain unsolved. Our work presents a theoretically-grounded method that infers Granger causal structure and identifies unknown targets by leveraging heterogeneous interventional time series data. We further illustrate that learning Granger causal structure and recovering interventional targets can mutually promote each other. Comparative experiments demonstrate that our method outperforms several robust baseline methods in learning Granger causal structure from interventional time series data. | [
"['Ziyi Zhang' 'Shaogang Ren' 'Xiaoning Qian' 'Nick Duffield']"
] |
null | null | 2406.10425 | null | null | http://arxiv.org/abs/2406.10425v2 | 2024-06-22T22:26:01Z | 2024-06-14T22:05:21Z | Multi-source Unsupervised Domain Adaptation on Graphs with
Transferability Modeling | In this paper, we tackle a new problem of textit{multi-source unsupervised domain adaptation (MSUDA) for graphs}, where models trained on annotated source domains need to be transferred to the unsupervised target graph for node classification. Due to the discrepancy in distribution across domains, the key challenge is how to select good source instances and how to adapt the model. Diverse graph structures further complicate this problem, rendering previous MSUDA approaches less effective. In this work, we present the framework Selective Multi-source Adaptation for Graph ({method}), with a graph-modeling-based domain selector, a sub-graph node selector, and a bi-level alignment objective for the adaptation. Concretely, to facilitate the identification of informative source data, the similarity across graphs is disentangled and measured with the transferability of a graph-modeling task set, and we use it as evidence for source domain selection. A node selector is further incorporated to capture the variation in transferability of nodes within the same source domain. To learn invariant features for adaptation, we align the target domain to selected source data both at the embedding space by minimizing the optimal transport distance and at the classification level by distilling the label function. Modules are explicitly learned to select informative source data and conduct the alignment in virtual training splits with a meta-learning strategy. Experimental results on five graph datasets show the effectiveness of the proposed method. | [
"['Tianxiang Zhao' 'Dongsheng Luo' 'Xiang Zhang' 'Suhang Wang']"
] |
null | null | 2406.10426 | null | null | http://arxiv.org/pdf/2406.10426v2 | 2024-06-26T19:26:58Z | 2024-06-14T22:07:11Z | Towards Neural Scaling Laws for Foundation Models on Temporal Graphs | The field of temporal graph learning aims to learn from evolving network data to forecast future interactions. Given a collection of observed temporal graphs, is it possible to predict the evolution of an unseen network from the same domain? To answer this question, we first present the Temporal Graph Scaling (TGS) dataset, a large collection of temporal graphs consisting of eighty-four ERC20 token transaction networks collected from 2017 to 2023. Next, we evaluate the transferability of Temporal Graph Neural Networks (TGNNs) for the temporal graph property prediction task by pre-training on a collection of up to sixty-four token transaction networks and then evaluating the downstream performance on twenty unseen token networks. We find that the neural scaling law observed in NLP and Computer Vision also applies in temporal graph learning, where pre-training on greater number of networks leads to improved downstream performance. To the best of our knowledge, this is the first empirical demonstration of the transferability of temporal graphs learning. On downstream token networks, the largest pre-trained model outperforms single model TGNNs on thirteen unseen test networks. Therefore, we believe that this is a promising first step towards building foundation models for temporal graphs. | [
"['Razieh Shirzadkhani' 'Tran Gia Bao Ngo' 'Kiarash Shamsi'\n 'Shenyang Huang' 'Farimah Poursafaei' 'Poupak Azad' 'Reihaneh Rabbany'\n 'Baris Coskunuzer' 'Guillaume Rabusseau' 'Cuneyt Gurcan Akcora']"
] |
null | null | 2406.10427 | null | null | http://arxiv.org/pdf/2406.10427v1 | 2024-06-14T22:11:02Z | 2024-06-14T22:11:02Z | Adaptive Randomized Smoothing: Certifying Multi-Step Defences against
Adversarial Examples | We propose Adaptive Randomized Smoothing (ARS) to certify the predictions of our test-time adaptive models against adversarial examples. ARS extends the analysis of randomized smoothing using f-Differential Privacy to certify the adaptive composition of multiple steps. For the first time, our theory covers the sound adaptive composition of general and high-dimensional functions of noisy input. We instantiate ARS on deep image classification to certify predictions against adversarial examples of bounded $L_{infty}$ norm. In the $L_{infty}$ threat model, our flexibility enables adaptation through high-dimensional input-dependent masking. We design adaptivity benchmarks, based on CIFAR-10 and CelebA, and show that ARS improves accuracy by $2$ to $5%$ points. On ImageNet, ARS improves accuracy by $1$ to $3%$ points over standard RS without adaptivity. | [
"['Saiyue Lyu' 'Shadab Shaikh' 'Frederick Shpilevskiy' 'Evan Shelhamer'\n 'Mathias Lécuyer']"
] |
null | null | 2406.10433 | null | null | http://arxiv.org/pdf/2406.10433v1 | 2024-06-14T22:42:02Z | 2024-06-14T22:42:02Z | Differentiable Predictive Control for Large-Scale Urban Road Networks | Transportation is a major contributor to CO2 emissions, making it essential to optimize traffic networks to reduce energy-related emissions. This paper presents a novel approach to traffic network control using Differentiable Predictive Control (DPC), a physics-informed machine learning methodology. We base our model on the Macroscopic Fundamental Diagram (MFD) and the Networked Macroscopic Fundamental Diagram (NMFD), offering a simplified representation of citywide traffic networks. Our approach ensures compliance with system constraints by construction. In empirical comparisons with existing state-of-the-art Model Predictive Control (MPC) methods, our approach demonstrates a 4 order of magnitude reduction in computation time and an up to 37% improvement in traffic performance. Furthermore, we assess the robustness of our controller to scenario shifts and find that it adapts well to changes in traffic patterns. This work proposes more efficient traffic control methods, particularly in large-scale urban networks, and aims to mitigate emissions and alleviate congestion in the future. | [
"['Renukanandan Tumu' 'Wenceslao Shaw Cortez' 'Ján Drgoňa'\n 'Draguna L. Vrabie' 'Sonja Glavaski']"
] |
null | null | 2406.10445 | null | null | http://arxiv.org/pdf/2406.10445v2 | 2024-07-06T20:03:16Z | 2024-06-14T23:40:42Z | Optimal Reward Labeling: Bridging Offline Preference and Reward-Based
Reinforcement Learning | Offline reinforcement learning has become one of the most practical RL settings. A recent success story has been RLHF, offline preference-based RL (PBRL) with preference from humans. However, most existing works on offline RL focus on the standard setting with scalar reward feedback. It remains unknown how to universally transfer the existing rich understanding of offline RL from the reward-based to the preference-based setting. In this work, we propose a general framework to bridge this gap. Our key insight is transforming preference feedback to scalar rewards via optimal reward labeling (ORL), and then any reward-based offline RL algorithms can be applied to the dataset with the reward labels. We theoretically show the connection between several recent PBRL techniques and our framework combined with specific offline RL algorithms in terms of how they utilize the preference signals. By combining reward labeling with different algorithms, our framework can lead to new and potentially more efficient offline PBRL algorithms. We empirically test our framework on preference datasets based on the standard D4RL benchmark. When combined with a variety of efficient reward-based offline RL algorithms, the learning result achieved under our framework is comparable to training the same algorithm on the dataset with actual rewards in many cases and better than the recent PBRL baselines in most cases. | [
"['Yinglun Xu' 'David Zhu' 'Rohan Gumaste' 'Gagandeep Singh']"
] |
null | null | 2406.10449 | null | null | http://arxiv.org/pdf/2406.10449v1 | 2024-06-15T00:07:36Z | 2024-06-15T00:07:36Z | Learning Temporal Logic Predicates from Data with Statistical Guarantees | Temporal logic rules are often used in control and robotics to provide structured, human-interpretable descriptions of high-dimensional trajectory data. These rules have numerous applications including safety validation using formal methods, constraining motion planning among autonomous agents, and classifying data. However, existing methods for learning temporal logic predicates from data provide no assurances about the correctness of the resulting predicate. We present a novel method to learn temporal logic predicates from data with finite-sample correctness guarantees. Our approach leverages expression optimization and conformal prediction to learn predicates that correctly describe future trajectories under mild assumptions with a user-defined confidence level. We provide experimental results showing the performance of our approach on a simulated trajectory dataset and perform ablation studies to understand how each component of our algorithm contributes to its performance. | [
"['Emi Soroka' 'Rohan Sinha' 'Sanjay Lall']"
] |
null | null | 2406.10454 | null | null | http://arxiv.org/pdf/2406.10454v1 | 2024-06-15T00:41:34Z | 2024-06-15T00:41:34Z | HumanPlus: Humanoid Shadowing and Imitation from Humans | One of the key arguments for building robots that have similar form factors to human beings is that we can leverage the massive human data for training. Yet, doing so has remained challenging in practice due to the complexities in humanoid perception and control, lingering physical gaps between humanoids and humans in morphologies and actuation, and lack of a data pipeline for humanoids to learn autonomous skills from egocentric vision. In this paper, we introduce a full-stack system for humanoids to learn motion and autonomous skills from human data. We first train a low-level policy in simulation via reinforcement learning using existing 40-hour human motion datasets. This policy transfers to the real world and allows humanoid robots to follow human body and hand motion in real time using only a RGB camera, i.e. shadowing. Through shadowing, human operators can teleoperate humanoids to collect whole-body data for learning different tasks in the real world. Using the data collected, we then perform supervised behavior cloning to train skill policies using egocentric vision, allowing humanoids to complete different tasks autonomously by imitating human skills. We demonstrate the system on our customized 33-DoF 180cm humanoid, autonomously completing tasks such as wearing a shoe to stand up and walk, unloading objects from warehouse racks, folding a sweatshirt, rearranging objects, typing, and greeting another robot with 60-100% success rates using up to 40 demonstrations. Project website: https://humanoid-ai.github.io/ | [
"['Zipeng Fu' 'Qingqing Zhao' 'Qi Wu' 'Gordon Wetzstein' 'Chelsea Finn']"
] |
null | null | 2406.10455 | null | null | http://arxiv.org/pdf/2406.10455v1 | 2024-06-15T00:44:32Z | 2024-06-15T00:44:32Z | Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose
Inference | Cryo-Electron Microscopy (cryo-EM) is an increasingly popular experimental technique for estimating the 3D structure of macromolecular complexes such as proteins based on 2D images. These images are notoriously noisy, and the pose of the structure in each image is unknown textit{a priori}. Ab-initio 3D reconstruction from 2D images entails estimating the pose in addition to the structure. In this work, we propose a new approach to this problem. We first adopt a multi-head architecture as a pose encoder to infer multiple plausible poses per-image in an amortized fashion. This approach mitigates the high uncertainty in pose estimation by encouraging exploration of pose space early in reconstruction. Once uncertainty is reduced, we refine poses in an auto-decoding fashion. In particular, we initialize with the most likely pose and iteratively update it for individual images using stochastic gradient descent (SGD). Through evaluation on synthetic datasets, we demonstrate that our method is able to handle multi-modal pose distributions during the amortized inference stage, while the later, more flexible stage of direct pose optimization yields faster and more accurate convergence of poses compared to baselines. Finally, on experimental data, we show that our approach is faster than state-of-the-art cryoAI and achieves higher-resolution reconstruction. | [
"['Shayan Shekarforoush' 'David B. Lindell' 'Marcus A. Brubaker'\n 'David J. Fleet']"
] |
null | null | 2406.10481 | null | null | http://arxiv.org/pdf/2406.10481v1 | 2024-06-15T03:17:48Z | 2024-06-15T03:17:48Z | DCDILP: a distributed learning method for large-scale causal structure
learning | This paper presents a novel approach to causal discovery through a divide-and-conquer framework. By decomposing the problem into smaller subproblems defined on Markov blankets, the proposed DCDILP method first explores in parallel the local causal graphs of these subproblems. However, this local discovery phase encounters systematic challenges due to the presence of hidden confounders (variables within each Markov blanket may be influenced by external variables). Moreover, aggregating these local causal graphs in a consistent global graph defines a large size combinatorial optimization problem. DCDILP addresses these challenges by: i) restricting the local subgraphs to causal links only related with the central variable of the Markov blanket; ii) formulating the reconciliation of local causal graphs as an integer linear programming method. The merits of the approach, in both terms of causal discovery accuracy and scalability in the size of the problem, are showcased by experiments and comparisons with the state of the art. | [
"['Shuyu Dong' 'Michèle Sebag' 'Kento Uemura' 'Akito Fujii' 'Shuang Chang'\n 'Yusuke Koyanagi' 'Koji Maruhashi']"
] |
null | null | 2406.10485 | null | null | http://arxiv.org/pdf/2406.10485v1 | 2024-06-15T03:30:29Z | 2024-06-15T03:30:29Z | A Label is Worth a Thousand Images in Dataset Distillation | Data $textit{quality}$ is a crucial factor in the performance of machine learning models, a principle that dataset distillation methods exploit by compressing training datasets into much smaller counterparts that maintain similar downstream performance. Understanding how and why data distillation methods work is vital not only for improving these methods but also for revealing fundamental characteristics of "good" training data. However, a major challenge in achieving this goal is the observation that distillation approaches, which rely on sophisticated but mostly disparate methods to generate synthetic data, have little in common with each other. In this work, we highlight a largely overlooked aspect common to most of these methods: the use of soft (probabilistic) labels. Through a series of ablation experiments, we study the role of soft labels in depth. Our results reveal that the main factor explaining the performance of state-of-the-art distillation methods is not the specific techniques used to generate synthetic data but rather the use of soft labels. Furthermore, we demonstrate that not all soft labels are created equal; they must contain $textit{structured information}$ to be beneficial. We also provide empirical scaling laws that characterize the effectiveness of soft labels as a function of images-per-class in the distilled dataset and establish an empirical Pareto frontier for data-efficient learning. Combined, our findings challenge conventional wisdom in dataset distillation, underscore the importance of soft labels in learning, and suggest new directions for improving distillation methods. Code for all experiments is available at https://github.com/sunnytqin/no-distillation. | [
"['Tian Qin' 'Zhiwei Deng' 'David Alvarez-Melis']"
] |
null | null | 2406.10490 | null | null | http://arxiv.org/pdf/2406.10490v1 | 2024-06-15T04:03:12Z | 2024-06-15T04:03:12Z | Active, anytime-valid risk controlling prediction sets | Rigorously establishing the safety of black-box machine learning models concerning critical risk measures is important for providing guarantees about model behavior. Recently, Bates et. al. (JACM '24) introduced the notion of a risk controlling prediction set (RCPS) for producing prediction sets that are statistically guaranteed low risk from machine learning models. Our method extends this notion to the sequential setting, where we provide guarantees even when the data is collected adaptively, and ensures that the risk guarantee is anytime-valid, i.e., simultaneously holds at all time steps. Further, we propose a framework for constructing RCPSes for active labeling, i.e., allowing one to use a labeling policy that chooses whether to query the true label for each received data point and ensures that the expected proportion of data points whose labels are queried are below a predetermined label budget. We also describe how to use predictors (i.e., the machine learning model for which we provide risk control guarantees) to further improve the utility of our RCPSes by estimating the expected risk conditioned on the covariates. We characterize the optimal choices of label policy and predictor under a fixed label budget and show a regret result that relates the estimation error of the optimal labeling policy and predictor to the wealth process that underlies our RCPSes. Lastly, we present practical ways of formulating label policies and empirically show that our label policies use fewer labels to reach higher utility than naive baseline labeling strategies (e.g., labeling all points, randomly labeling points) on both simulations and real data. | [
"['Ziyu Xu' 'Nikos Karampatziakis' 'Paul Mineiro']"
] |
null | null | 2406.10492 | null | null | http://arxiv.org/pdf/2406.10492v1 | 2024-06-15T04:09:31Z | 2024-06-15T04:09:31Z | Large Language Models as Event Forecasters | Key elements of human events are extracted as quadruples that consist of subject, relation, object, and timestamp. This representation can be extended to a quintuple by adding a fifth element: a textual summary that briefly describes the event. These quadruples or quintuples, when organized within a specific domain, form a temporal knowledge graph (TKG). Current learning frameworks focus on a few TKG-related tasks, such as predicting an object given a subject and a relation or forecasting the occurrences of multiple types of events (i.e., relation) in the next time window. They typically rely on complex structural and sequential models like graph neural networks (GNNs) and recurrent neural networks (RNNs) to update intermediate embeddings. However, these methods often neglect the contextual information inherent in each quintuple, which can be effectively captured through concise textual descriptions. In this paper, we investigate how large language models (LLMs) can streamline the design of TKG learning frameworks while maintaining competitive accuracy in prediction and forecasting tasks. We develop multiple prompt templates to frame the object prediction (OP) task as a standard question-answering (QA) task, suitable for instruction fine-tuning with an encoder-decoder generative LLM. For multi-event forecasting (MEF), we design simple yet effective prompt templates for each TKG quintuple. This novel approach removes the need for GNNs and RNNs, instead utilizing an encoder-only LLM to generate fixed intermediate embeddings, which are subsequently processed by a prediction head with a self-attention mechanism to forecast potential future relations. Extensive experiments on multiple real-world datasets using various evaluation metrics validate the effectiveness and robustness of our approach. | [
"['Libo Zhang' 'Yue Ning']"
] |
null | null | 2406.10498 | null | null | http://arxiv.org/pdf/2406.10498v1 | 2024-06-15T04:36:40Z | 2024-06-15T04:36:40Z | A Unified Graph Selective Prompt Learning for Graph Neural Networks | In recent years, graph prompt learning/tuning has garnered increasing attention in adapting pre-trained models for graph representation learning. As a kind of universal graph prompt learning method, Graph Prompt Feature (GPF) has achieved remarkable success in adapting pre-trained models for Graph Neural Networks (GNNs). By fixing the parameters of a pre-trained GNN model, the aim of GPF is to modify the input graph data by adding some (learnable) prompt vectors into graph node features to better align with the downstream tasks on the smaller dataset. However, existing GPFs generally suffer from two main limitations. First, GPFs generally focus on node prompt learning which ignore the prompting for graph edges. Second, existing GPFs generally conduct the prompt learning on all nodes equally which fails to capture the importances of different nodes and may perform sensitively w.r.t noisy nodes in aligning with the downstream tasks. To address these issues, in this paper, we propose a new unified Graph Selective Prompt Feature learning (GSPF) for GNN fine-tuning. The proposed GSPF integrates the prompt learning on both graph node and edge together, which thus provides a unified prompt model for the graph data. Moreover, it conducts prompt learning selectively on nodes and edges by concentrating on the important nodes and edges for prompting which thus make our model be more reliable and compact. Experimental results on many benchmark datasets demonstrate the effectiveness and advantages of the proposed GSPF method. | [
"['Bo Jiang' 'Hao Wu' 'Ziyan Zhang' 'Beibei Wang' 'Jin Tang']"
] |
null | null | 2406.10500 | null | null | http://arxiv.org/pdf/2406.10500v1 | 2024-06-15T04:47:40Z | 2024-06-15T04:47:40Z | Geodesic Distance Between Graphs: A Spectral Metric for Assessing the
Stability of Graph Neural Networks | This paper presents a spectral framework for assessing the generalization and stability of Graph Neural Networks (GNNs) by introducing a Graph Geodesic Distance (GGD) metric. For two different graphs with the same number of nodes, our framework leverages a spectral graph matching procedure to find node correspondence so that the geodesic distance between them can be subsequently computed by solving a generalized eigenvalue problem associated with their Laplacian matrices. For graphs with different sizes, a resistance-based spectral graph coarsening scheme is introduced to reduce the size of the bigger graph while preserving the original spectral properties. We show that the proposed GGD metric can effectively quantify dissimilarities between two graphs by encapsulating their differences in key structural (spectral) properties, such as effective resistances between nodes, cuts, the mixing time of random walks, etc. Through extensive experiments comparing with the state-of-the-art metrics, such as the latest Tree-Mover's Distance (TMD) metric, the proposed GGD metric shows significantly improved performance for stability evaluation of GNNs especially when only partial node features are available. | [
"['Soumen Sikder Shuvo' 'Ali Aghdaei' 'Zhuo Feng']"
] |
null | null | 2406.10502 | null | null | http://arxiv.org/pdf/2406.10502v1 | 2024-06-15T04:50:20Z | 2024-06-15T04:50:20Z | Candidate Pseudolabel Learning: Enhancing Vision-Language Models by
Prompt Tuning with Unlabeled Data | Fine-tuning vision-language models (VLMs) with abundant unlabeled data recently has attracted increasing attention. Existing methods that resort to the pseudolabeling strategy would suffer from heavily incorrect hard pseudolabels when VLMs exhibit low zero-shot performance in downstream tasks. To alleviate this issue, we propose a Candidate Pseudolabel Learning method, termed CPL, to fine-tune VLMs with suitable candidate pseudolabels of unlabeled data in downstream tasks. The core of our method lies in the generation strategy of candidate pseudolabels, which progressively generates refined candidate pseudolabels by both intra- and inter-instance label selection, based on a confidence score matrix for all unlabeled data. This strategy can result in better performance in true label inclusion and class-balanced instance selection. In this way, we can directly apply existing loss functions to learn with generated candidate psueudolabels. Extensive experiments on nine benchmark datasets with three learning paradigms demonstrate the effectiveness of our method. Our code can be found at https://github.com/vanillaer/CPL-ICML2024. | [
"['Jiahan Zhang' 'Qi Wei' 'Feng Liu' 'Lei Feng']"
] |
null | null | 2406.10504 | null | null | http://arxiv.org/pdf/2406.10504v1 | 2024-06-15T04:54:26Z | 2024-06-15T04:54:26Z | Task Facet Learning: A Structured Approach to Prompt Optimization | Given a task in the form of a basic description and its training examples, prompt optimization is the problem of synthesizing the given information into a text prompt for a large language model (LLM). Humans solve this problem by also considering the different facets that define a task (e.g., counter-examples, explanations, analogies) and including them in the prompt. However, it is unclear whether existing algorithmic approaches, based on iteratively editing a given prompt or automatically selecting a few in-context examples, can cover the multiple facets required to solve a complex task. In this work, we view prompt optimization as that of learning multiple facets of a task from a set of training examples. We identify and exploit structure in the prompt optimization problem -- first, we find that prompts can be broken down into loosely coupled semantic sections that have a relatively independent effect on the prompt's performance; second, we cluster the input space and use clustered batches so that the optimization procedure can learn the different facets of a task across batches. The resulting algorithm, UniPrompt, consists of a generative model to generate initial candidates for each prompt section; and a feedback mechanism that aggregates suggested edits from multiple mini-batches into a conceptual description for the section. Empirical evaluation on multiple datasets and a real-world task shows that prompts generated using UniPrompt obtain higher accuracy than human-tuned prompts and those from state-of-the-art methods. In particular, our algorithm can generate long, complex prompts that existing methods are unable to generate. Code for UniPrompt will be available at url{https://aka.ms/uniprompt}. | [
"['Gurusha Juneja' 'Nagarajan Natarajan' 'Hua Li' 'Jian Jiao' 'Amit Sharma']"
] |
null | null | 2406.10513 | null | null | http://arxiv.org/pdf/2406.10513v1 | 2024-06-15T05:29:07Z | 2024-06-15T05:29:07Z | Lift Your Molecules: Molecular Graph Generation in Latent Euclidean
Space | We introduce a new framework for molecular graph generation with 3D molecular generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps molecular graphs to Euclidean point clouds via synthetic conformer coordinates and learns the inverse map using an E(n)-Equivariant Graph Neural Network (EGNN). The induced point cloud-structured latent space is well-suited to apply existing 3D molecular generative models. This approach simplifies the graph generation problem - without relying on molecular fragments nor autoregressive decoding - into a point cloud generation problem followed by node and edge classification tasks. Further, we propose a novel similarity-constrained optimization scheme for 3D diffusion models based on inpainting and guidance. As a concrete implementation of our framework, we develop EDM-SyCo based on the E(3) Equivariant Diffusion Model (EDM). EDM-SyCo achieves state-of-the-art performance in distribution learning of molecular graphs, outperforming the best non-autoregressive methods by more than 30% on ZINC250K and 16% on the large-scale GuacaMol dataset while improving conditional generation by up to 3.9 times. | [
"['Mohamed Amine Ketata' 'Nicholas Gao' 'Johanna Sommer' 'Tom Wollschläger'\n 'Stephan Günnemann']"
] |
null | null | 2406.10514 | null | null | http://arxiv.org/pdf/2406.10514v1 | 2024-06-15T05:37:04Z | 2024-06-15T05:37:04Z | GTR-Voice: Articulatory Phonetics Informed Controllable Expressive
Speech Synthesis | Expressive speech synthesis aims to generate speech that captures a wide range of para-linguistic features, including emotion and articulation, though current research primarily emphasizes emotional aspects over the nuanced articulatory features mastered by professional voice actors. Inspired by this, we explore expressive speech synthesis through the lens of articulatory phonetics. Specifically, we define a framework with three dimensions: Glottalization, Tenseness, and Resonance (GTR), to guide the synthesis at the voice production level. With this framework, we record a high-quality speech dataset named GTR-Voice, featuring 20 Chinese sentences articulated by a professional voice actor across 125 distinct GTR combinations. We verify the framework and GTR annotations through automatic classification and listening tests, and demonstrate precise controllability along the GTR dimensions on two fine-tuned expressive TTS models. We open-source the dataset and TTS models. | [
"['Zehua Kcriss Li' 'Meiying Melissa Chen' 'Yi Zhong' 'Pinxin Liu'\n 'Zhiyao Duan']"
] |
null | null | 2406.10517 | null | null | http://arxiv.org/pdf/2406.10517v1 | 2024-06-15T06:04:46Z | 2024-06-15T06:04:46Z | ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in
Advertising | Advertising platforms have evolved in estimating Lifetime Value (LTV) to better align with advertisers' true performance metric. However, the sparsity of real-world LTV data presents a significant challenge to LTV predictive model(i.e., pLTV), severely limiting the their capabilities. Therefore, we propose to utilize external data, in addition to the internal data of advertising platform, to expand the size of purchase samples and enhance the LTV prediction model of the advertising platform. To tackle the issue of data distribution shift between internal and external platforms, we introduce an Adaptive Difference Siamese Network (ADSNet), which employs cross-domain transfer learning to prevent negative transfer. Specifically, ADSNet is designed to learn information that is beneficial to the target domain. We introduce a gain evaluation strategy to calculate information gain, aiding the model in learning helpful information for the target domain and providing the ability to reject noisy samples, thus avoiding negative transfer. Additionally, we also design a Domain Adaptation Module as a bridge to connect different domains, reduce the distribution distance between them, and enhance the consistency of representation space distribution. We conduct extensive offline experiments and online A/B tests on a real advertising platform. Our proposed ADSNet method outperforms other methods, improving GINI by 2$%$. The ablation study highlights the importance of the gain evaluation strategy in negative gain sample rejection and improving model performance. Additionally, ADSNet significantly improves long-tail prediction. The online A/B tests confirm ADSNet's efficacy, increasing online LTV by 3.47$%$ and GMV by 3.89$%$. | [
"['Ruize Wang' 'Hui Xu' 'Ying Cheng' 'Qi He' 'Xing Zhou' 'Rui Feng'\n 'Wei Xu' 'Lei Huang' 'Jie Jiang']"
] |
null | null | 2406.10521 | null | null | http://arxiv.org/pdf/2406.10521v2 | 2024-06-29T13:48:12Z | 2024-06-15T06:26:17Z | MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial
Network for Synthesizing Tabular Data | In the era of big data, access to abundant data is crucial for driving research forward. However, such data is often inaccessible due to privacy concerns or high costs, particularly in healthcare domain. Generating synthetic (tabular) data can address this, but existing models typically require substantial amounts of data to train effectively, contradicting our objective to solve data scarcity. To address this challenge, we propose a novel framework to generate synthetic tabular data, powered by large language models (LLMs) that emulates the architecture of a Generative Adversarial Network (GAN). By incorporating data generation process as contextual information and utilizing LLM as the optimizer, our approach significantly enhance the quality of synthetic data generation in common scenarios with small sample sizes. Our experimental results on public and private datasets demonstrate that our model outperforms several state-of-art models regarding generating higher quality synthetic data for downstream tasks while keeping privacy of the real data. | [
"['Yaobin Ling' 'Xiaoqian Jiang' 'Yejin Kim']"
] |
null | null | 2406.10522 | null | null | http://arxiv.org/pdf/2406.10522v1 | 2024-06-15T06:26:25Z | 2024-06-15T06:26:25Z | Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for
Cartoon Captioning | We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing rating data for The New Yorker's weekly cartoon caption contest over the past eight years. This unique dataset supports the development and evaluation of multimodal large language models and preference-based fine-tuning algorithms for humorous caption generation. We propose novel benchmarks for judging the quality of model-generated captions, utilizing both GPT4 and human judgments to establish ranking-based evaluation strategies. Our experimental results highlight the limitations of current fine-tuning methods, such as RLHF and DPO, when applied to creative tasks. Furthermore, we demonstrate that even state-of-the-art models like GPT4 and Claude currently underperform top human contestants in generating humorous captions. As we conclude this extensive data collection effort, we release the entire preference dataset to the research community, fostering further advancements in AI humor generation and evaluation. | [
"['Jifan Zhang' 'Lalit Jain' 'Yang Guo' 'Jiayi Chen' 'Kuan Lok Zhou'\n 'Siddharth Suresh' 'Andrew Wagenmaker' 'Scott Sievert' 'Timothy Rogers'\n 'Kevin Jamieson' 'Robert Mankoff' 'Robert Nowak']"
] |
null | null | 2406.10528 | null | null | http://arxiv.org/pdf/2406.10528v1 | 2024-06-15T06:40:48Z | 2024-06-15T06:40:48Z | Memory Faults in Activation-sparse Quantized Deep Neural Networks:
Analysis and Mitigation using Sharpness-aware Training | Improving the hardware efficiency of deep neural network (DNN) accelerators with techniques such as quantization and sparsity enhancement have shown an immense promise. However, their inference accuracy in non-ideal real-world settings (such as in the presence of hardware faults) is yet to be systematically analyzed. In this work, we investigate the impact of memory faults on activation-sparse quantized DNNs (AS QDNNs). We show that a high level of activation sparsity comes at the cost of larger vulnerability to faults, with AS QDNNs exhibiting up to 11.13% lower accuracy than the standard QDNNs. We establish that the degraded accuracy correlates with a sharper minima in the loss landscape for AS QDNNs, which makes them more sensitive to perturbations in the weight values due to faults. Based on this observation, we employ sharpness-aware quantization (SAQ) training to mitigate the impact of memory faults. The AS and standard QDNNs trained with SAQ have up to 19.50% and 15.82% higher inference accuracy, respectively compared to their conventionally trained equivalents. Moreover, we show that SAQ-trained AS QDNNs show higher accuracy in faulty settings than standard QDNNs trained conventionally. Thus, sharpness-aware training can be instrumental in achieving sparsity-related latency benefits without compromising on fault tolerance. | [
"['Akul Malhotra' 'Sumeet Kumar Gupta']"
] |
null | null | 2406.10529 | null | null | http://arxiv.org/pdf/2406.10529v1 | 2024-06-15T06:43:45Z | 2024-06-15T06:43:45Z | A Theory of Interpretable Approximations | Can a deep neural network be approximated by a small decision tree based on simple features? This question and its variants are behind the growing demand for machine learning models that are *interpretable* by humans. In this work we study such questions by introducing *interpretable approximations*, a notion that captures the idea of approximating a target concept $c$ by a small aggregation of concepts from some base class $mathcal{H}$. In particular, we consider the approximation of a binary concept $c$ by decision trees based on a simple class $mathcal{H}$ (e.g., of bounded VC dimension), and use the tree depth as a measure of complexity. Our primary contribution is the following remarkable trichotomy. For any given pair of $mathcal{H}$ and $c$, exactly one of these cases holds: (i) $c$ cannot be approximated by $mathcal{H}$ with arbitrary accuracy; (ii) $c$ can be approximated by $mathcal{H}$ with arbitrary accuracy, but there exists no universal rate that bounds the complexity of the approximations as a function of the accuracy; or (iii) there exists a constant $kappa$ that depends only on $mathcal{H}$ and $c$ such that, for *any* data distribution and *any* desired accuracy level, $c$ can be approximated by $mathcal{H}$ with a complexity not exceeding $kappa$. This taxonomy stands in stark contrast to the landscape of supervised classification, which offers a complex array of distribution-free and universally learnable scenarios. We show that, in the case of interpretable approximations, even a slightly nontrivial a-priori guarantee on the complexity of approximations implies approximations with constant (distribution-free and accuracy-free) complexity. We extend our trichotomy to classes $mathcal{H}$ of unbounded VC dimension and give characterizations of interpretability based on the algebra generated by $mathcal{H}$. | [
"['Marco Bressan' 'Nicolò Cesa-Bianchi' 'Emmanuel Esposito'\n 'Yishay Mansour' 'Shay Moran' 'Maximilian Thiessen']"
] |
null | null | 2406.10534 | null | null | http://arxiv.org/pdf/2406.10534v1 | 2024-06-15T07:30:40Z | 2024-06-15T07:30:40Z | A Finite Difference Informed Graph Network for Solving Steady-State
Incompressible Flows on Block-Structured Grids | Recently, advancements in deep learning have enabled physics-informed neural networks (PINNs) to solve partial differential equations (PDEs). Numerical differentiation (ND) using the finite difference (FD) method is efficient in physics-constrained designs, even in parameterized settings, often employing body-fitted block-structured grids for complex flow cases. However, convolution operators in CNNs for finite differences are typically limited to single-block grids. To address this, we use graphs and graph networks (GNs) to learn flow representations across multi-block structured grids. We propose a graph convolution-based finite difference method (GC-FDM) to train GNs in a physics-constrained manner, enabling differentiable finite difference operations on graph unstructured outputs. Our goal is to solve parametric steady incompressible Navier-Stokes equations for flows around a backward-facing step, a circular cylinder, and double cylinders, using multi-block structured grids. Comparing our method to a CFD solver under various boundary conditions, we demonstrate improved training efficiency and accuracy, achieving a minimum relative error of $10^{-3}$ in velocity field prediction and a 20% reduction in training cost compared to PINNs. | [
"['Yiye Zou' 'Tianyu Li' 'Shufan Zou' 'Jingyu Wang' 'Laiping Zhang'\n 'Xiaogang Deng']"
] |
null | null | 2406.10537 | null | null | http://arxiv.org/pdf/2406.10537v1 | 2024-06-15T07:40:36Z | 2024-06-15T07:40:36Z | Scalable Differentiable Causal Discovery in the Presence of Latent
Confounders with Skeleton Posterior (Extended Version) | Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn maximal ancestral graphs (MAGs). To date, existing differentiable MAG learning algorithms have been limited to small datasets and failed to scale to larger ones (e.g., with more than 50 variables). The key insight in this paper is that the causal skeleton, which is the undirected version of the causal graph, has potential for improving accuracy and reducing the search space of the optimization procedure, thereby enhancing the performance of differentiable causal discovery. Therefore, we seek to address a two-fold challenge to harness the potential of the causal skeleton for differentiable causal discovery in the presence of latent confounders: (1) scalable and accurate estimation of skeleton and (2) universal integration of skeleton estimation with differentiable causal discovery. To this end, we propose SPOT (Skeleton Posterior-guided OpTimization), a two-phase framework that harnesses skeleton posterior for differentiable causal discovery in the presence of latent confounders. On the contrary to a ``point-estimation'', SPOT seeks to estimate the posterior distribution of skeletons given the dataset. It first formulates the posterior inference as an instance of amortized inference problem and concretizes it with a supervised causal learning (SCL)-enabled solution to estimate the skeleton posterior. To incorporate the skeleton posterior with differentiable causal discovery, SPOT then features a skeleton posterior-guided stochastic optimization procedure to guide the optimization of MAGs. [abridged due to length limit] | [
"['Pingchuan Ma' 'Rui Ding' 'Qiang Fu' 'Jiaru Zhang' 'Shuai Wang' 'Shi Han'\n 'Dongmei Zhang']"
] |
null | null | 2406.10538 | null | null | http://arxiv.org/pdf/2406.10538v2 | 2024-06-21T17:36:12Z | 2024-06-15T07:41:16Z | Large Reasoning Models for 3D Floorplanning in EDA: Learning from
Imperfections | In this paper, we introduce Dreamweaver, which belongs to a new class of auto-regressive decision-making models known as large reasoning models (LRMs). Dreamweaver is designed to improve 3D floorplanning in electronic design automation (EDA) via an architecture that melds advancements in sequence-to-sequence reinforcement learning algorithms. A significant advantage of our approach is its ability to effectively reason over large discrete action spaces, which is essential for handling the numerous potential positions for various functional blocks in floorplanning. Additionally, Dreamweaver demonstrates strong performance even when trained on entirely random trajectories, showcasing its capacity to leverage sub-optimal or non-expert trajectories to enhance its results. This innovative approach contributes to streamlining the integrated circuit (IC) design flow and reducing the high computational costs typically associated with floorplanning. We evaluate its performance against a current state-of-the-art method, highlighting notable improvements. | [
"['Fin Amin' 'Nirjhor Rouf' 'Tse-Han Pan' 'Md Kamal Ibn Shafi'\n 'Paul D. Franzon']"
] |
null | null | 2406.10552 | null | null | http://arxiv.org/pdf/2406.10552v4 | 2024-07-06T09:19:08Z | 2024-06-15T08:13:47Z | Large Language Model Enhanced Clustering for News Event Detection | The news landscape is continuously evolving, with an ever-increasing volume of information from around the world. Automated event detection within this vast data repository is essential for monitoring, identifying, and categorizing significant news occurrences across diverse platforms. This paper presents an event detection framework that leverages Large Language Models (LLMs) combined with clustering analysis to detect news events from the Global Database of Events, Language, and Tone (GDELT). The framework enhances event clustering through both pre-event detection tasks (keyword extraction and text embedding) and post-event detection tasks (event summarization and topic labelling). We also evaluate the impact of various textual embeddings on the quality of clustering outcomes, ensuring robust news categorization. Additionally, we introduce a novel Cluster Stability Assessment Index (CSAI) to assess the validity and robustness of clustering results. CSAI utilizes multiple feature vectors to provide a new way of measuring clustering quality. Our experiments indicate that the use of LLM embedding in the event detection framework has significantly improved the results, demonstrating greater robustness in terms of CSAI scores. Moreover, post-event detection tasks generate meaningful insights, facilitating effective interpretation of event clustering results. Overall, our experimental results indicate that the proposed framework offers valuable insights and could enhance the accuracy in news analysis and reporting. | [
"['Adane Nega Tarekegn']"
] |
null | null | 2406.10559 | null | null | http://arxiv.org/pdf/2406.10559v1 | 2024-06-15T08:37:51Z | 2024-06-15T08:37:51Z | Grad-Instructor: Universal Backpropagation with Explainable Evaluation
Neural Networks for Meta-learning and AutoML | This paper presents a novel method for autonomously enhancing deep neural network training. My approach employs an Evaluation Neural Network (ENN) trained via deep reinforcement learning to predict the performance of the target network. The ENN then works as an additional evaluation function during backpropagation. Computational experiments with Multi-Layer Perceptrons (MLPs) demonstrate the method's effectiveness. By processing input data at 0.15^2 times its original resolution, the ENNs facilitated efficient inference. Results indicate that MLPs trained with the proposed method achieved a mean test accuracy of 93.02%, which is 2.8% higher than those trained solely with conventional backpropagation or with L1 regularization. The proposed method's test accuracy is comparable to networks initialized with He initialization while reducing the difference between test and training errors. These improvements are achieved without increasing the number of epochs, thus avoiding the risk of overfitting. Additionally, the proposed method dynamically adjusts gradient magnitudes according to the training stage. The optimal ENN for enhancing MLPs can be predicted, reducing the time spent exploring optimal training methodologies. The explainability of ENNs is also analyzed using Grad-CAM, demonstrating their ability to visualize evaluation bases and supporting the Strong Lottery Ticket hypothesis. | [
"['Ryohei Ino']"
] |
null | null | 2406.10563 | null | null | http://arxiv.org/pdf/2406.10563v2 | 2024-07-04T14:10:00Z | 2024-06-15T08:43:40Z | Privacy-Preserving Heterogeneous Federated Learning for Sensitive
Healthcare Data | In the realm of healthcare where decentralized facilities are prevalent, machine learning faces two major challenges concerning the protection of data and models. The data-level challenge concerns the data privacy leakage when centralizing data with sensitive personal information. While the model-level challenge arises from the heterogeneity of local models, which need to be collaboratively trained while ensuring their confidentiality to address intellectual property concerns. To tackle these challenges, we propose a new framework termed Abstention-Aware Federated Voting (AAFV) that can collaboratively and confidentially train heterogeneous local models while simultaneously protecting the data privacy. This is achieved by integrating a novel abstention-aware voting mechanism and a differential privacy mechanism onto local models' predictions. In particular, the proposed abstention-aware voting mechanism exploits a threshold-based abstention method to select high-confidence votes from heterogeneous local models, which not only enhances the learning utility but also protects model confidentiality. Furthermore, we implement AAFV on two practical prediction tasks of diabetes and in-hospital patient mortality. The experiments demonstrate the effectiveness and confidentiality of AAFV in testing accuracy and privacy protection. | [
"['Yukai Xu' 'Jingfeng Zhang' 'Yujie Gu']"
] |
null | null | 2406.10569 | null | null | http://arxiv.org/pdf/2406.10569v1 | 2024-06-15T09:08:58Z | 2024-06-15T09:08:58Z | MDA: An Interpretable Multi-Modal Fusion with Missing Modalities and
Intrinsic Noise | Multi-modal fusion is crucial in medical data research, enabling a comprehensive understanding of diseases and improving diagnostic performance by combining diverse modalities. However, multi-modal fusion faces challenges, including capturing interactions between modalities, addressing missing modalities, handling erroneous modal information, and ensuring interpretability. Many existing researchers tend to design different solutions for these problems, often overlooking the commonalities among them. This paper proposes a novel multi-modal fusion framework that achieves adaptive adjustment over the weights of each modality by introducing the Modal-Domain Attention (MDA). It aims to facilitate the fusion of multi-modal information while allowing for the inclusion of missing modalities or intrinsic noise, thereby enhancing the representation of multi-modal data. We provide visualizations of accuracy changes and MDA weights by observing the process of modal fusion, offering a comprehensive analysis of its interpretability. Extensive experiments on various gastrointestinal disease benchmarks, the proposed MDA maintains high accuracy even in the presence of missing modalities and intrinsic noise. One thing worth mentioning is that the visualization of MDA is highly consistent with the conclusions of existing clinical studies on the dependence of different diseases on various modalities. Code and dataset will be made available. | [
"['Lin Fan' 'Yafei Ou' 'Cenyang Zheng' 'Pengyu Dai' 'Tamotsu Kamishima'\n 'Masayuki Ikebe' 'Kenji Suzuki' 'Xun Gong']"
] |
null | null | 2406.10573 | null | null | http://arxiv.org/pdf/2406.10573v1 | 2024-06-15T09:23:46Z | 2024-06-15T09:23:46Z | Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and
Future Directions | Graph Neural Networks (GNNs) have significantly advanced various downstream graph-relevant tasks, encompassing recommender systems, molecular structure prediction, social media analysis, etc. Despite the boosts of GNN, recent research has empirically demonstrated its potential vulnerability to backdoor attacks, wherein adversaries employ triggers to poison input samples, inducing GNN to adversary-premeditated malicious outputs. This is typically due to the controlled training process, or the deployment of untrusted models, such as delegating model training to third-party service, leveraging external training sets, and employing pre-trained models from online sources. Although there's an ongoing increase in research on GNN backdoors, comprehensive investigation into this field is lacking. To bridge this gap, we propose the first survey dedicated to GNN backdoors. We begin by outlining the fundamental definition of GNN, followed by the detailed summarization and categorization of current GNN backdoor attacks and defenses based on their technical characteristics and application scenarios. Subsequently, the analysis of the applicability and use cases of GNN backdoors is undertaken. Finally, the exploration of potential research directions of GNN backdoors is presented. This survey aims to explore the principles of graph backdoors, provide insights to defenders, and promote future security research. | [
"['Xiao Yang' 'Gaolei Li' 'Jianhua Li']"
] |
null | null | 2406.10576 | null | null | http://arxiv.org/pdf/2406.10576v1 | 2024-06-15T09:31:03Z | 2024-06-15T09:31:03Z | Optimization-based Structural Pruning for Large Language Models without
Back-Propagation | Compared to the moderate size of neural network models, structural weight pruning on the Large-Language Models (LLMs) imposes a novel challenge on the efficiency of the pruning algorithms, due to the heavy computation/memory demands of the LLMs. Recent efficient LLM pruning methods typically operate at the post-training phase without the expensive weight finetuning, however, their pruning criteria often rely on heuristically designed metrics, potentially leading to suboptimal performance. We instead propose a novel optimization-based structural pruning that learns the pruning masks in a probabilistic space directly by optimizing the loss of the pruned model. To preserve the efficiency, our method 1) works at post-training phase} and 2) eliminates the back-propagation through the LLM per se during the optimization (i.e., only requires the forward pass of the LLM). We achieve this by learning an underlying Bernoulli distribution to sample binary pruning masks, where we decouple the Bernoulli parameters from the LLM loss, thus facilitating an efficient optimization via a policy gradient estimator without back-propagation. As a result, our method is able to 1) operate at structural granularities of channels, heads, and layers, 2) support global and heterogeneous pruning (i.e., our method automatically determines different redundancy for different layers), and 3) optionally use a metric-based method as initialization (of our Bernoulli distributions). Extensive experiments on LLaMA, LLaMA-2, and Vicuna using the C4 and WikiText2 datasets demonstrate that our method operates for 2.7 hours with around 35GB memory for the 13B models on a single A100 GPU, and our pruned models outperform the state-of-the-arts w.r.t. perplexity. Codes will be released. | [
"['Yuan Gao' 'Zujing Liu' 'Weizhong Zhang' 'Bo Du' 'Gui-Song Xia']"
] |
null | null | 2406.10579 | null | null | http://arxiv.org/pdf/2406.10579v1 | 2024-06-15T09:38:41Z | 2024-06-15T09:38:41Z | Robust Image Classification in the Presence of Out-of-Distribution and
Adversarial Samples Using Attractors in Neural Networks | The proper handling of out-of-distribution (OOD) samples in deep classifiers is a critical concern for ensuring the suitability of deep neural networks in safety-critical systems. Existing approaches developed for robust OOD detection in the presence of adversarial attacks lose their performance by increasing the perturbation levels. This study proposes a method for robust classification in the presence of OOD samples and adversarial attacks with high perturbation levels. The proposed approach utilizes a fully connected neural network that is trained to use training samples as its attractors, enhancing its robustness. This network has the ability to classify inputs and identify OOD samples as well. To evaluate this method, the network is trained on the MNIST dataset, and its performance is tested on adversarial examples. The results indicate that the network maintains its performance even when classifying adversarial examples, achieving 87.13% accuracy when dealing with highly perturbed MNIST test data. Furthermore, by using fashion-MNIST and CIFAR-10-bw as OOD samples, the network can distinguish these samples from MNIST samples with an accuracy of 98.84% and 99.28%, respectively. In the presence of severe adversarial attacks, these measures decrease slightly to 98.48% and 98.88%, indicating the robustness of the proposed method. | [
"['Nasrin Alipour' 'Seyyed Ali SeyyedSalehi']"
] |
null | null | 2406.10605 | null | null | http://arxiv.org/pdf/2406.10605v1 | 2024-06-15T11:50:36Z | 2024-06-15T11:50:36Z | Last-iterate Convergence Separation between Extra-gradient and Optimism
in Constrained Periodic Games | Last-iterate behaviors of learning algorithms in repeated two-player zero-sum games have been extensively studied due to their wide applications in machine learning and related tasks. Typical algorithms that exhibit the last-iterate convergence property include optimistic and extra-gradient methods. However, most existing results establish these properties under the assumption that the game is time-independent. Recently, (Feng et al, 2023) studied the last-iterate behaviors of optimistic and extra-gradient methods in games with a time-varying payoff matrix, and proved that in an unconstrained periodic game, extra-gradient method converges to the equilibrium while optimistic method diverges. This finding challenges the conventional wisdom that these two methods are expected to behave similarly as they do in time-independent games. However, compared to unconstrained games, games with constrains are more common both in practical and theoretical studies. In this paper, we investigate the last-iterate behaviors of optimistic and extra-gradient methods in the constrained periodic games, demonstrating that similar separation results for last-iterate convergence also hold in this setting. | [
"['Yi Feng' 'Ping Li' 'Ioannis Panageas' 'Xiao Wang']"
] |
null | null | 2406.10615 | null | null | http://arxiv.org/pdf/2406.10615v1 | 2024-06-15T12:27:35Z | 2024-06-15T12:27:35Z | Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation | Given the high cost of collecting robotic data in the real world, sample efficiency is a consistently compelling pursuit in robotics. In this paper, we introduce SGRv2, an imitation learning framework that enhances sample efficiency through improved visual and action representations. Central to the design of SGRv2 is the incorporation of a critical inductive bias-action locality, which posits that robot's actions are predominantly influenced by the target object and its interactions with the local environment. Extensive experiments in both simulated and real-world settings demonstrate that action locality is essential for boosting sample efficiency. SGRv2 excels in RLBench tasks with keyframe control using merely 5 demonstrations and surpasses the RVT baseline in 23 of 26 tasks. Furthermore, when evaluated on ManiSkill2 and MimicGen using dense control, SGRv2's success rate is 2.54 times that of SGR. In real-world environments, with only eight demonstrations, SGRv2 can perform a variety of tasks at a markedly higher success rate compared to baseline models. Project website: http://sgrv2-robot.github.io | [
"['Tong Zhang' 'Yingdong Hu' 'Jiacheng You' 'Yang Gao']"
] |
null | null | 2406.10616 | null | null | http://arxiv.org/abs/2406.10616v1 | 2024-06-15T12:34:40Z | 2024-06-15T12:34:40Z | HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated
Graph Learning | Federated Graph Learning (FGL) has emerged as a promising way to learn high-quality representations from distributed graph data with privacy preservation. Despite considerable efforts have been made for FGL under either cross-device or cross-silo paradigm, how to effectively capture graph knowledge in a more complicated cross-silo cross-device environment remains an under-explored problem. However, this task is challenging because of the inherent hierarchy and heterogeneity of decentralized clients, diversified privacy constraints in different clients, and the cross-client graph integrity requirement. To this end, in this paper, we propose a Hierarchical Federated Graph Learning (HiFGL) framework for cross-silo cross-device FGL. Specifically, we devise a unified hierarchical architecture to safeguard federated GNN training on heterogeneous clients while ensuring graph integrity. Moreover, we propose a Secret Message Passing (SecMP) scheme to shield unauthorized access to subgraph-level and node-level sensitive information simultaneously. Theoretical analysis proves that HiFGL achieves multi-level privacy preservation with complexity guarantees. Extensive experiments on real-world datasets validate the superiority of the proposed framework against several baselines. Furthermore, HiFGL's versatile nature allows for its application in either solely cross-silo or cross-device settings, further broadening its utility in real-world FGL applications. | [
"['Zhuoning Guo' 'Duanyi Yao' 'Qiang Yang' 'Hao Liu']"
] |
null | null | 2406.10631 | null | null | http://arxiv.org/pdf/2406.10631v1 | 2024-06-15T13:26:17Z | 2024-06-15T13:26:17Z | Fast Last-Iterate Convergence of Learning in Games Requires Forgetful
Algorithms | Self-play via online learning is one of the premier ways to solve large-scale two-player zero-sum games, both in theory and practice. Particularly popular algorithms include optimistic multiplicative weights update (OMWU) and optimistic gradient-descent-ascent (OGDA). While both algorithms enjoy $O(1/T)$ ergodic convergence to Nash equilibrium in two-player zero-sum games, OMWU offers several advantages including logarithmic dependence on the size of the payoff matrix and $widetilde{O}(1/T)$ convergence to coarse correlated equilibria even in general-sum games. However, in terms of last-iterate convergence in two-player zero-sum games, an increasingly popular topic in this area, OGDA guarantees that the duality gap shrinks at a rate of $O(1/sqrt{T})$, while the best existing last-iterate convergence for OMWU depends on some game-dependent constant that could be arbitrarily large. This begs the question: is this potentially slow last-iterate convergence an inherent disadvantage of OMWU, or is the current analysis too loose? Somewhat surprisingly, we show that the former is true. More generally, we prove that a broad class of algorithms that do not forget the past quickly all suffer the same issue: for any arbitrarily small $delta>0$, there exists a $2times 2$ matrix game such that the algorithm admits a constant duality gap even after $1/delta$ rounds. This class of algorithms includes OMWU and other standard optimistic follow-the-regularized-leader algorithms. | [
"['Yang Cai' 'Gabriele Farina' 'Julien Grand-Clément' 'Christian Kroer'\n 'Chung-Wei Lee' 'Haipeng Luo' 'Weiqiang Zheng']"
] |
null | null | 2406.10650 | null | null | http://arxiv.org/pdf/2406.10650v1 | 2024-06-15T14:39:37Z | 2024-06-15T14:39:37Z | The Implicit Bias of Adam on Separable Data | Adam has become one of the most favored optimizers in deep learning problems. Despite its success in practice, numerous mysteries persist regarding its theoretical understanding. In this paper, we study the implicit bias of Adam in linear logistic regression. Specifically, we show that when the training data are linearly separable, Adam converges towards a linear classifier that achieves the maximum $ell_infty$-margin. Notably, for a general class of diminishing learning rates, this convergence occurs within polynomial time. Our result shed light on the difference between Adam and (stochastic) gradient descent from a theoretical perspective. | [
"['Chenyang Zhang' 'Difan Zou' 'Yuan Cao']"
] |
null | null | 2406.10661 | null | null | http://arxiv.org/pdf/2406.10661v1 | 2024-06-15T14:58:17Z | 2024-06-15T14:58:17Z | A GPU-accelerated Large-scale Simulator for Transportation System
Optimization Benchmarking | With the development of artificial intelligence techniques, transportation system optimization is evolving from traditional methods relying on expert experience to simulation and learning-based decision optimization methods. Learning-based optimization methods require extensive interaction with highly realistic microscopic traffic simulators for optimization. However, existing microscopic traffic simulators are computationally inefficient in large-scale scenarios and therefore significantly reduce the efficiency of the data sampling process of optimization algorithms. In addition, the optimization scenarios supported by existing simulators are limited, mainly focusing on the traffic signal control. To address these challenges and limitations, we propose the first open-source GPU-accelerated large-scale microscopic simulator for transportation system simulation. The simulator is able to iterate at 84.09Hz, which achieves 88.92 times computational acceleration in the large-scale scenario with more than a million vehicles compared to the best baseline. Based on the simulator, we implement a set of microscopic and macroscopic controllable objects and metrics to support most typical transportation system optimization scenarios. These controllable objects and metrics are all provided by Python API for ease of use. We choose five important and representative transportation system optimization scenarios and benchmark classical rule-based algorithms, reinforcement learning, and black-box optimization in four cities. The codes are available at url{https://github.com/tsinghua-fib-lab/moss-benchmark} with the MIT License. | [
"['Jun Zhang' 'Wenxuan Ao' 'Junbo Yan' 'Depeng Jin' 'Yong Li']"
] |
null | null | 2406.10667 | null | null | http://arxiv.org/pdf/2406.10667v1 | 2024-06-15T15:24:15Z | 2024-06-15T15:24:15Z | UniZero: Generalized and Efficient Planning with Scalable Latent World
Models | Learning predictive world models is essential for enhancing the planning capabilities of reinforcement learning agents. Notably, the MuZero-style algorithms, based on the value equivalence principle and Monte Carlo Tree Search (MCTS), have achieved superhuman performance in various domains. However, in environments that require capturing long-term dependencies, MuZero's performance deteriorates rapidly. We identify that this is partially due to the textit{entanglement} of latent representations with historical information, which results in incompatibility with the auxiliary self-supervised state regularization. To overcome this limitation, we present textit{UniZero}, a novel approach that textit{disentangles} latent states from implicit latent history using a transformer-based latent world model. By concurrently predicting latent dynamics and decision-oriented quantities conditioned on the learned latent history, UniZero enables joint optimization of the long-horizon world model and policy, facilitating broader and more efficient planning in latent space. We demonstrate that UniZero, even with single-frame inputs, matches or surpasses the performance of MuZero-style algorithms on the Atari 100k benchmark. Furthermore, it significantly outperforms prior baselines in benchmarks that require long-term memory. Lastly, we validate the effectiveness and scalability of our design choices through extensive ablation studies, visual analyses, and multi-task learning results. The code is available at textcolor{magenta}{https://github.com/opendilab/LightZero}. | [
"['Yuan Pu' 'Yazhe Niu' 'Jiyuan Ren' 'Zhenjie Yang' 'Hongsheng Li' 'Yu Liu']"
] |
null | null | 2406.10670 | null | null | http://arxiv.org/pdf/2406.10670v2 | 2024-06-24T13:52:37Z | 2024-06-15T15:28:02Z | CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language
Model Pre-training | Selecting high-quality data for pre-training is crucial in shaping the downstream task performance of language models. A major challenge lies in identifying this optimal subset, a problem generally considered intractable, thus necessitating scalable and effective heuristics. In this work, we propose a data selection method, CoLoR-Filter (Conditional Loss Reduction Filtering), which leverages an empirical Bayes-inspired approach to derive a simple and computationally efficient selection criterion based on the relative loss values of two auxiliary models. In addition to the modeling rationale, we evaluate CoLoR-Filter empirically on two language modeling tasks: (1) selecting data from C4 for domain adaptation to evaluation on Books and (2) selecting data from C4 for a suite of downstream multiple-choice question answering tasks. We demonstrate favorable scaling both as we subselect more aggressively and using small auxiliary models to select data for large target models. As one headline result, CoLoR-Filter data selected using a pair of 150m parameter auxiliary models can train a 1.2b parameter target model to match a 1.2b parameter model trained on 25b randomly selected tokens with 25x less data for Books and 11x less data for the downstream tasks. Code: https://github.com/davidbrandfonbrener/color-filter-olmo Filtered data: https://huggingface.co/datasets/davidbrandfonbrener/color-filtered-c4 | [
"['David Brandfonbrener' 'Hanlin Zhang' 'Andreas Kirsch'\n 'Jonathan Richard Schwarz' 'Sham Kakade']"
] |
null | null | 2406.10685 | null | null | http://arxiv.org/pdf/2406.10685v1 | 2024-06-15T16:41:04Z | 2024-06-15T16:41:04Z | Scale Equivariant Graph Metanetworks | This paper pertains to an emerging machine learning paradigm: learning higher-order functions, i.e. functions whose inputs are functions themselves, $textit{particularly when these inputs are Neural Networks (NNs)}$. With the growing interest in architectures that process NNs, a recurring design principle has permeated the field: adhering to the permutation symmetries arising from the connectionist structure of NNs. $textit{However, are these the sole symmetries present in NN parameterizations}$? Zooming into most practical activation functions (e.g. sine, ReLU, tanh) answers this question negatively and gives rise to intriguing new symmetries, which we collectively refer to as $textit{scaling symmetries}$, that is, non-zero scalar multiplications and divisions of weights and biases. In this work, we propose $textit{Scale Equivariant Graph MetaNetworks - ScaleGMNs}$, a framework that adapts the Graph Metanetwork (message-passing) paradigm by incorporating scaling symmetries and thus rendering neuron and edge representations equivariant to valid scalings. We introduce novel building blocks, of independent technical interest, that allow for equivariance or invariance with respect to individual scalar multipliers or their product and use them in all components of ScaleGMN. Furthermore, we prove that, under certain expressivity conditions, ScaleGMN can simulate the forward and backward pass of any input feedforward neural network. Experimental results demonstrate that our method advances the state-of-the-art performance for several datasets and activation functions, highlighting the power of scaling symmetries as an inductive bias for NN processing. | [
"['Ioannis Kalogeropoulos' 'Giorgos Bouritsas' 'Yannis Panagakis']"
] |
null | null | 2406.10686 | null | null | http://arxiv.org/pdf/2406.10686v2 | 2024-06-20T20:22:47Z | 2024-06-15T16:45:27Z | Graph Neural Thompson Sampling | We consider an online decision-making problem with a reward function defined over graph-structured data. We formally formulate the problem as an instance of graph action bandit. We then propose texttt{GNN-TS}, a Graph Neural Network (GNN) powered Thompson Sampling (TS) algorithm which employs a GNN approximator for estimating the mean reward function and the graph neural tangent features for uncertainty estimation. We prove that, under certain boundness assumptions on the reward function, GNN-TS achieves a state-of-the-art regret bound which is (1) sub-linear of order $tilde{mathcal{O}}((tilde{d} T)^{1/2})$ in the number of interaction rounds, $T$, and a notion of effective dimension $tilde{d}$, and (2) independent of the number of graph nodes. Empirical results validate that our proposed texttt{GNN-TS} exhibits competitive performance and scales well on graph action bandit problems. | [
"['Shuang Wu' 'Arash A. Amini']"
] |
null | null | 2406.10688 | null | null | http://arxiv.org/pdf/2406.10688v1 | 2024-06-15T16:49:53Z | 2024-06-15T16:49:53Z | Integration of Programmable Diffraction with Digital Neural Networks | Optical imaging and sensing systems based on diffractive elements have seen massive advances over the last several decades. Earlier generations of diffractive optical processors were, in general, designed to deliver information to an independent system that was separately optimized, primarily driven by human vision or perception. With the recent advances in deep learning and digital neural networks, there have been efforts to establish diffractive processors that are jointly optimized with digital neural networks serving as their back-end. These jointly optimized hybrid (optical+digital) processors establish a new "diffractive language" between input electromagnetic waves that carry analog information and neural networks that process the digitized information at the back-end, providing the best of both worlds. Such hybrid designs can process spatially and temporally coherent, partially coherent, or incoherent input waves, providing universal coverage for any spatially varying set of point spread functions that can be optimized for a given task, executed in collaboration with digital neural networks. In this article, we highlight the utility of this exciting collaboration between engineered and programmed diffraction and digital neural networks for a diverse range of applications. We survey some of the major innovations enabled by the push-pull relationship between analog wave processing and digital neural networks, also covering the significant benefits that could be reaped through the synergy between these two complementary paradigms. | [
"['Md Sadman Sakib Rahman' 'Aydogan Ozcan']"
] |
null | null | 2406.10703 | null | null | http://arxiv.org/pdf/2406.10703v2 | 2024-06-19T14:16:03Z | 2024-06-15T18:08:04Z | Calibrating Neural Networks' parameters through Optimal Contraction in a
Prediction Problem | This study introduces a novel approach to ensure the existence and uniqueness of optimal parameters in neural networks. The paper details how a recurrent neural networks (RNN) can be transformed into a contraction in a domain where its parameters are linear. It then demonstrates that a prediction problem modeled through an RNN, with a specific regularization term in the loss function, can have its first-order conditions expressed analytically. This system of equations is reduced to two matrix equations involving Sylvester equations, which can be partially solved. We establish that, if certain conditions are met, optimal parameters exist, are unique, and can be found through a straightforward algorithm to any desired precision. Also, as the number of neurons grows the conditions of convergence become easier to fulfill. Feedforward neural networks (FNNs) are also explored by including linear constraints on parameters. According to our model, incorporating loops (with fixed or variable weights) will produce loss functions that train easier, because it assures the existence of a region where an iterative method converges. | [
"['Valdes Gonzalo']"
] |
null | null | 2406.10707 | null | null | http://arxiv.org/abs/2406.10707v1 | 2024-06-15T18:30:40Z | 2024-06-15T18:30:40Z | DataStates-LLM: Lazy Asynchronous Checkpointing for Large Language
Models | LLMs have seen rapid adoption in all domains. They need to be trained on high-end high-performance computing (HPC) infrastructures and ingest massive amounts of input data. Unsurprisingly, at such a large scale, unexpected events (e.g., failures of components, instability of the software, undesirable learning patterns, etc.), are frequent and typically impact the training in a negative fashion. Thus, LLMs need to be checkpointed frequently so that they can be rolled back to a stable state and subsequently fine-tuned. However, given the large sizes of LLMs, a straightforward checkpointing solution that directly writes the model parameters and optimizer state to persistent storage (e.g., a parallel file system), incurs significant I/O overheads. To address this challenge, in this paper we study how to reduce the I/O overheads for enabling fast and scalable checkpointing for LLMs that can be applied at high frequency (up to the granularity of individual iterations) without significant impact on the training process. Specifically, we introduce a lazy asynchronous multi-level approach that takes advantage of the fact that the tensors making up the model and optimizer state shards remain immutable for extended periods of time, which makes it possible to copy their content in the background with minimal interference during the training process. We evaluate our approach at scales of up to 180 GPUs using different model sizes, parallelism settings, and checkpointing frequencies. The results show up to 48$times$ faster checkpointing and 2.2$times$ faster end-to-end training runtime compared with the state-of-art checkpointing approaches. | [
"['Avinash Maurya' 'Robert Underwood' 'M. Mustafa Rafique'\n 'Franck Cappello' 'Bogdan Nicolae']"
] |
null | null | 2406.10714 | null | null | http://arxiv.org/pdf/2406.10714v1 | 2024-06-15T18:53:45Z | 2024-06-15T18:53:45Z | Planning with Adaptive World Models for Autonomous Driving | Motion planning is crucial for safe navigation in complex urban environments. Historically, motion planners (MPs) have been evaluated with procedurally-generated simulators like CARLA. However, such synthetic benchmarks do not capture real-world multi-agent interactions. nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic, effectively turning the fixed dataset into a reactive simulator. We analyze the characteristics of nuPlan's recorded logs and find that each city has its own unique driving behaviors, suggesting that robust planners must adapt to different environments. We learn to model such unique behaviors with BehaviorNet, a graph convolutional neural network (GCNN) that predicts reactive agent behaviors using features derived from recently-observed agent histories; intuitively, some aggressive agents may tailgate lead vehicles, while others may not. To model such phenomena, BehaviorNet predicts parameters of an agent's motion controller rather than predicting its spacetime trajectory (as most forecasters do). Finally, we present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions. Our extensive experiments demonstrate that AdaptiveDriver achieves state-of-the-art results on the nuPlan closed-loop planning benchmark, reducing test error from 6.4% to 4.6%, even when applied to never-before-seen cities. | [
"['Arun Balajee Vasudevan' 'Neehar Peri' 'Jeff Schneider' 'Deva Ramanan']"
] |
null | null | 2406.10718 | null | null | http://arxiv.org/pdf/2406.10718v1 | 2024-06-15T19:05:49Z | 2024-06-15T19:05:49Z | Stacking for Probabilistic Short-term Load Forecasting | In this study, we delve into the realm of meta-learning to combine point base forecasts for probabilistic short-term electricity demand forecasting. Our approach encompasses the utilization of quantile linear regression, quantile regression forest, and post-processing techniques involving residual simulation to generate quantile forecasts. Furthermore, we introduce both global and local variants of meta-learning. In the local-learning mode, the meta-model is trained using patterns most similar to the query pattern.Through extensive experimental studies across 35 forecasting scenarios and employing 16 base forecasting models, our findings underscored the superiority of quantile regression forest over its competitors | [
"['Grzegorz Dudek']"
] |
null | null | 2406.10719 | null | null | http://arxiv.org/pdf/2406.10719v3 | 2024-06-21T17:42:32Z | 2024-06-15T19:12:00Z | Trading Devil: Robust backdoor attack via Stochastic investment models
and Bayesian approach | With the growing use of voice-activated systems and speech recognition technologies, the danger of backdoor attacks on audio data has grown significantly. This research looks at a specific type of attack, known as a Stochastic investment-based backdoor attack (MarketBack), in which adversaries strategically manipulate the stylistic properties of audio to fool speech recognition systems. The security and integrity of machine learning models are seriously threatened by backdoor attacks, in order to maintain the reliability of audio applications and systems, the identification of such attacks becomes crucial in the context of audio data. Experimental results demonstrated that MarketBack is feasible to achieve an average attack success rate close to 100% in seven victim models when poisoning less than 1% of the training data. | [
"['Orson Mengara']"
] |
null | null | 2406.10722 | null | null | http://arxiv.org/pdf/2406.10722v1 | 2024-06-15T19:29:01Z | 2024-06-15T19:29:01Z | GenMM: Geometrically and Temporally Consistent Multimodal Data
Generation for Video and LiDAR | Multimodal synthetic data generation is crucial in domains such as autonomous driving, robotics, augmented/virtual reality, and retail. We propose a novel approach, GenMM, for jointly editing RGB videos and LiDAR scans by inserting temporally and geometrically consistent 3D objects. Our method uses a reference image and 3D bounding boxes to seamlessly insert and blend new objects into target videos. We inpaint the 2D Regions of Interest (consistent with 3D boxes) using a diffusion-based video inpainting model. We then compute semantic boundaries of the object and estimate it's surface depth using state-of-the-art semantic segmentation and monocular depth estimation techniques. Subsequently, we employ a geometry-based optimization algorithm to recover the 3D shape of the object's surface, ensuring it fits precisely within the 3D bounding box. Finally, LiDAR rays intersecting with the new object surface are updated to reflect consistent depths with its geometry. Our experiments demonstrate the effectiveness of GenMM in inserting various 3D objects across video and LiDAR modalities. | [
"['Bharat Singh' 'Viveka Kulharia' 'Luyu Yang' 'Avinash Ravichandran'\n 'Ambrish Tyagi' 'Ashish Shrivastava']"
] |
null | null | 2406.10724 | null | null | http://arxiv.org/pdf/2406.10724v1 | 2024-06-15T19:34:18Z | 2024-06-15T19:34:18Z | Beyond the Visible: Jointly Attending to Spectral and Spatial Dimensions
with HSI-Diffusion for the FINCH Spacecraft | Satellite remote sensing missions have gained popularity over the past fifteen years due to their ability to cover large swaths of land at regular intervals, making them ideal for monitoring environmental trends. The FINCH mission, a 3U+ CubeSat equipped with a hyperspectral camera, aims to monitor crop residue cover in agricultural fields. Although hyperspectral imaging captures both spectral and spatial information, it is prone to various types of noise, including random noise, stripe noise, and dead pixels. Effective denoising of these images is crucial for downstream scientific tasks. Traditional methods, including hand-crafted techniques encoding strong priors, learned 2D image denoising methods applied across different hyperspectral bands, or diffusion generative models applied independently on bands, often struggle with varying noise strengths across spectral bands, leading to significant spectral distortion. This paper presents a novel approach to hyperspectral image denoising using latent diffusion models that integrate spatial and spectral information. We particularly do so by building a 3D diffusion model and presenting a 3-stage training approach on real and synthetically crafted datasets. The proposed method preserves image structure while reducing noise. Evaluations on both popular hyperspectral denoising datasets and synthetically crafted datasets for the FINCH mission demonstrate the effectiveness of this approach. | [
"['Ian Vyse' 'Rishit Dagli' 'Dav Vrat Chadha' 'John P. Ma' 'Hector Chen'\n 'Isha Ruparelia' 'Prithvi Seran' 'Matthew Xie' 'Eesa Aamer'\n 'Aidan Armstrong' 'Naveen Black' 'Ben Borstein' 'Kevin Caldwell'\n 'Orrin Dahanaggamaarachchi' 'Joe Dai' 'Abeer Fatima' 'Stephanie Lu'\n 'Maxime Michet' 'Anoushka Paul' 'Carrie Ann Po' 'Shivesh Prakash'\n 'Noa Prosser' 'Riddhiman Roy' 'Mirai Shinjo' 'Iliya Shofman'\n 'Coby Silayan' 'Reid Sox-Harris' 'Shuhan Zheng' 'Khang Nguyen']"
] |
null | null | 2406.10727 | null | null | http://arxiv.org/pdf/2406.10727v1 | 2024-06-15T19:56:21Z | 2024-06-15T19:56:21Z | Text-space Graph Foundation Models: Comprehensive Benchmarks and New
Insights | Given the ubiquity of graph data and its applications in diverse domains, building a Graph Foundation Model (GFM) that can work well across different graphs and tasks with a unified backbone has recently garnered significant interests. A major obstacle to achieving this goal stems from the fact that graphs from different domains often exhibit diverse node features. Inspired by multi-modal models that align different modalities with natural language, the text has recently been adopted to provide a unified feature space for diverse graphs. Despite the great potential of these text-space GFMs, current research in this field is hampered by two problems. First, the absence of a comprehensive benchmark with unified problem settings hinders a clear understanding of the comparative effectiveness and practical value of different text-space GFMs. Second, there is a lack of sufficient datasets to thoroughly explore the methods' full potential and verify their effectiveness across diverse settings. To address these issues, we conduct a comprehensive benchmark providing novel text-space datasets and comprehensive evaluation under unified problem settings. Empirical results provide new insights and inspire future research directions. Our code and data are publicly available from url{https://github.com/CurryTang/TSGFM}. | [
"['Zhikai Chen' 'Haitao Mao' 'Jingzhe Liu' 'Yu Song' 'Bingheng Li'\n 'Wei Jin' 'Bahare Fatemi' 'Anton Tsitsulin' 'Bryan Perozzi' 'Hui Liu'\n 'Jiliang Tang']"
] |
null | null | 2406.10729 | null | null | http://arxiv.org/pdf/2406.10729v1 | 2024-06-15T20:04:06Z | 2024-06-15T20:04:06Z | A Comprehensive Survey of Foundation Models in Medicine | Foundation models (FMs) are large-scale deep-learning models trained on extensive datasets using self-supervised techniques. These models serve as a base for various downstream tasks, including healthcare. FMs have been adopted with great success across various domains within healthcare, including natural language processing (NLP), computer vision, graph learning, biology, and omics. Existing healthcare-based surveys have not yet included all of these domains. Therefore, this survey provides a comprehensive overview of FMs in healthcare. We focus on the history, learning strategies, flagship models, applications, and challenges of FMs. We explore how FMs such as the BERT and GPT families are reshaping various healthcare domains, including clinical large language models, medical image analysis, and omics data. Furthermore, we provide a detailed taxonomy of healthcare applications facilitated by FMs, such as clinical NLP, medical computer vision, graph learning, and other biology-related tasks. Despite the promising opportunities FMs provide, they also have several associated challenges, which are explained in detail. We also outline potential future directions to provide researchers and practitioners with insights into the potential and limitations of FMs in healthcare to advance their deployment and mitigate associated risks. | [
"['Wasif Khan' 'Seowung Leem' 'Kyle B. See' 'Joshua K. Wong'\n 'Shaoting Zhang' 'Ruogu Fang']"
] |
null | null | 2406.10737 | null | null | http://arxiv.org/pdf/2406.10737v1 | 2024-06-15T20:47:38Z | 2024-06-15T20:47:38Z | Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time
Adaptation | Continual Test-Time Adaptation (TTA) seeks to adapt a source pre-trained model to continually changing, unlabeled target domains. Existing TTA methods are typically designed for environments where domain changes occur gradually and can struggle in more dynamic scenarios. Inspired by the principles of online K-Means, this paper introduces a novel approach to continual TTA through visual prompting. We propose a Dynamic Prompt Coreset that not only preserves knowledge from previously visited domains but also accommodates learning from new potential domains. This is complemented by a distance-based weight updating mechanism that ensures the coreset remains current and relevant. Our approach employs a fixed model architecture alongside the coreset and an innovative updating system to effectively mitigate challenges such as catastrophic forgetting and error accumulation. Extensive testing across various benchmarks-including ImageNet-C, CIFAR100-C, and CIFAR10-C-demonstrates that our method consistently outperforms state-of-the-art (SOTA) alternatives, particularly excelling in dynamically changing environments. | [
"['Yunbei Zhang' 'Akshay Mehra' 'Jihun Hamm']"
] |
null | null | 2406.10738 | null | null | http://arxiv.org/pdf/2406.10738v1 | 2024-06-15T20:54:48Z | 2024-06-15T20:54:48Z | Adaptive Experimentation When You Can't Experiment | This paper introduces the emph{confounded pure exploration transductive linear bandit} (texttt{CPET-LB}) problem. As a motivating example, often online services cannot directly assign users to specific control or treatment experiences either for business or practical reasons. In these settings, naively comparing treatment and control groups that may result from self-selection can lead to biased estimates of underlying treatment effects. Instead, online services can employ a properly randomized encouragement that incentivizes users toward a specific treatment. Our methodology provides online services with an adaptive experimental design approach for learning the best-performing treatment for such textit{encouragement designs}. We consider a more general underlying model captured by a linear structural equation and formulate pure exploration linear bandits in this setting. Though pure exploration has been extensively studied in standard adaptive experimental design settings, we believe this is the first work considering a setting where noise is confounded. Elimination-style algorithms using experimental design methods in combination with a novel finite-time confidence interval on an instrumental variable style estimator are presented with sample complexity upper bounds nearly matching a minimax lower bound. Finally, experiments are conducted that demonstrate the efficacy of our approach. | [
"['Yao Zhao' 'Kwang-Sung Jun' 'Tanner Fiez' 'Lalit Jain']"
] |
null | null | 2406.10741 | null | null | http://arxiv.org/pdf/2406.10741v1 | 2024-06-15T21:33:03Z | 2024-06-15T21:33:03Z | Speech Emotion Recognition Using CNN and Its Use Case in Digital
Healthcare | The process of identifying human emotion and affective states from speech is known as speech emotion recognition (SER). This is based on the observation that tone and pitch in the voice frequently convey underlying emotion. Speech recognition includes the ability to recognize emotions, which is becoming increasingly popular and in high demand. With the help of appropriate factors (such modalities, emotions, intensities, repetitions, etc.) found in the data, my research seeks to use the Convolutional Neural Network (CNN) to distinguish emotions from audio recordings and label them in accordance with the range of different emotions. I have developed a machine learning model to identify emotions from supplied audio files with the aid of machine learning methods. The evaluation is mostly focused on precision, recall, and F1 score, which are common machine learning metrics. To properly set up and train the machine learning framework, the main objective is to investigate the influence and cross-relation of all input and output parameters. To improve the ability to recognize intentions, a key condition for communication, I have evaluated emotions using my specialized machine learning algorithm via voice that would address the emotional state from voice with the help of digital healthcare, bridging the gap between human and artificial intelligence (AI). | [
"['Nishargo Nigar']"
] |
null | null | 2406.10743 | null | null | http://arxiv.org/pdf/2406.10743v1 | 2024-06-15T21:42:15Z | 2024-06-15T21:42:15Z | Occam's Razor for Self Supervised Learning: What is Sufficient to Learn
Good Representations? | Deep Learning is often depicted as a trio of data-architecture-loss. Yet, recent Self Supervised Learning (SSL) solutions have introduced numerous additional design choices, e.g., a projector network, positive views, or teacher-student networks. These additions pose two challenges. First, they limit the impact of theoretical studies that often fail to incorporate all those intertwined designs. Second, they slow-down the deployment of SSL methods to new domains as numerous hyper-parameters need to be carefully tuned. In this study, we bring forward the surprising observation that--at least for pretraining datasets of up to a few hundred thousands samples--the additional designs introduced by SSL do not contribute to the quality of the learned representations. That finding not only provides legitimacy to existing theoretical studies, but also simplifies the practitioner's path to SSL deployment in numerous small and medium scale settings. Our finding answers a long-lasting question: the often-experienced sensitivity to training settings and hyper-parameters encountered in SSL come from their design, rather than the absence of supervised guidance. | [
"['Mark Ibrahim' 'David Klindt' 'Randall Balestriero']"
] |
null | null | 2406.10771 | null | null | http://arxiv.org/pdf/2406.10771v1 | 2024-06-16T01:07:15Z | 2024-06-16T01:07:15Z | Predicting Exoplanetary Features with a Residual Model for Uniform and
Gaussian Distributions | The advancement of technology has led to rampant growth in data collection across almost every field, including astrophysics, with researchers turning to machine learning to process and analyze this data. One prominent example of this data in astrophysics is the atmospheric retrievals of exoplanets. In order to help bridge the gap between machine learning and astrophysics domain experts, the 2023 Ariel Data Challenge was hosted to predict posterior distributions of 7 exoplanetary features. The procedure outlined in this paper leveraged a combination of two deep learning models to address this challenge: a Multivariate Gaussian model that generates the mean and covariance matrix of a multivariate Gaussian distribution, and a Uniform Quantile model that predicts quantiles for use as the upper and lower bounds of a uniform distribution. Training of the Multivariate Gaussian model was found to be unstable, while training of the Uniform Quantile model was stable. An ensemble of uniform distributions was found to have competitive results during testing (posterior score of 696.43), and when combined with a multivariate Gaussian distribution achieved a final rank of third in the 2023 Ariel Data Challenge (final score of 681.57). | [
"['Andrew Sweet']"
] |
null | null | 2406.10774 | null | null | http://arxiv.org/pdf/2406.10774v1 | 2024-06-16T01:33:02Z | 2024-06-16T01:33:02Z | Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference | As the demand for long-context large language models (LLMs) increases, models with context windows of up to 128K or 1M tokens are becoming increasingly prevalent. However, long-context LLM inference is challenging since the inference speed decreases significantly as the sequence length grows. This slowdown is primarily caused by loading a large KV cache during self-attention. Previous works have shown that a small portion of critical tokens will dominate the attention outcomes. However, we observe the criticality of a token highly depends on the query. To this end, we propose Quest, a query-aware KV cache selection algorithm. Quest keeps track of the minimal and maximal Key values in KV cache pages and estimates the criticality of a given page using Query vectors. By only loading the Top-K critical KV cache pages for attention, Quest significantly speeds up self-attention without sacrificing accuracy. We show that Quest can achieve up to 2.23x self-attention speedup, which reduces inference latency by 7.03x while performing well on tasks with long dependencies with negligible accuracy loss. Code is available at http://github.com/mit-han-lab/Quest . | [
"['Jiaming Tang' 'Yilong Zhao' 'Kan Zhu' 'Guangxuan Xiao' 'Baris Kasikci'\n 'Song Han']"
] |
null | null | 2406.10775 | null | null | http://arxiv.org/pdf/2406.10775v2 | 2024-06-18T12:41:43Z | 2024-06-16T01:33:22Z | A Rate-Distortion View of Uncertainty Quantification | In supervised learning, understanding an input's proximity to the training data can help a model decide whether it has sufficient evidence for reaching a reliable prediction. While powerful probabilistic models such as Gaussian Processes naturally have this property, deep neural networks often lack it. In this paper, we introduce Distance Aware Bottleneck (DAB), i.e., a new method for enriching deep neural networks with this property. Building on prior information bottleneck approaches, our method learns a codebook that stores a compressed representation of all inputs seen during training. The distance of a new example from this codebook can serve as an uncertainty estimate for the example. The resulting model is simple to train and provides deterministic uncertainty estimates by a single forward pass. Finally, our method achieves better out-of-distribution (OOD) detection and misclassification prediction than prior methods, including expensive ensemble methods, deep kernel Gaussian Processes, and approaches based on the standard information bottleneck. | [
"['Ifigeneia Apostolopoulou' 'Benjamin Eysenbach' 'Frank Nielsen'\n 'Artur Dubrawski']"
] |
null | null | 2406.10787 | null | null | http://arxiv.org/pdf/2406.10787v2 | 2024-07-02T02:38:45Z | 2024-06-16T03:00:16Z | Evidential Uncertainty Sets in Deep Classifiers Using Conformal
Prediction | In this paper, we propose Evidential Conformal Prediction (ECP) method for image classifiers to generate the conformal prediction sets. Our method is designed based on a non-conformity score function that has its roots in Evidential Deep Learning (EDL) as a method of quantifying model (epistemic) uncertainty in DNN classifiers. We use evidence that are derived from the logit values of target labels to compute the components of our non-conformity score function: the heuristic notion of uncertainty in CP, uncertainty surprisal, and expected utility. Our extensive experimental evaluation demonstrates that ECP outperforms three state-of-the-art methods for generating CP sets, in terms of their set sizes and adaptivity while maintaining the coverage of true labels. | [
"['Hamed Karimi' 'Reza Samavi']"
] |
null | null | 2406.10795 | null | null | http://arxiv.org/pdf/2406.10795v1 | 2024-06-16T03:43:55Z | 2024-06-16T03:43:55Z | Improving Reward-Conditioned Policies for Multi-Armed Bandits using
Normalized Weight Functions | Recently proposed reward-conditioned policies (RCPs) offer an appealing alternative in reinforcement learning. Compared with policy gradient methods, policy learning in RCPs is simpler since it is based on supervised learning, and unlike value-based methods, it does not require optimization in the action space to take actions. However, for multi-armed bandit (MAB) problems, we find that RCPs are slower to converge and have inferior expected rewards at convergence, compared with classic methods such as the upper confidence bound and Thompson sampling. In this work, we show that the performance of RCPs can be enhanced by constructing policies through the marginalization of rewards using normalized weight functions, whose sum or integral equal $1$, although the function values may be negative. We refer to this technique as generalized marginalization, whose advantage is that negative weights for policies conditioned on low rewards can make the resulting policies more distinct from them. Strategies to perform generalized marginalization in MAB with discrete action spaces are studied. Through simulations, we demonstrate that the proposed technique improves RCPs and makes them competitive with classic methods, showing superior performance on challenging MABs with large action spaces and sparse reward signals. | [
"['Kai Xu' 'Farid Tajaddodianfar' 'Ben Allison']"
] |
null | null | 2406.10796 | null | null | http://arxiv.org/pdf/2406.10796v1 | 2024-06-16T03:45:03Z | 2024-06-16T03:45:03Z | Diffusion Models Are Promising for Ab Initio Structure Solutions from
Nanocrystalline Powder Diffraction Data | A major challenge in materials science is the determination of the structure of nanometer sized objects. Here we present a novel approach that uses a generative machine learning model based on a Diffusion model that is trained on 45,229 known structures. The model factors both the measured diffraction pattern as well as relevant statistical priors on the unit cell of atomic cluster structures. Conditioned only on the chemical formula and the information-scarce finite-size broadened powder diffraction pattern, we find that our model, PXRDnet, can successfully solve simulated nanocrystals as small as 10 angstroms across 200 materials of varying symmetry and complexity, including structures from all seven crystal systems. We show that our model can determine structural solutions with up to $81.5%$ accuracy, as measured by structural correlation. Furthermore, PXRDnet is capable of solving structures from noisy diffraction patterns gathered in real-world experiments. We suggest that data driven approaches, bootstrapped from theoretical simulation, will ultimately provide a path towards determining the structure of previously unsolved nano-materials. | [
"['Gabe Guo' 'Tristan Saidi' 'Maxwell Terban' 'Simon JL Billinge'\n 'Hod Lipson']"
] |
null | null | 2406.10798 | null | null | http://arxiv.org/pdf/2406.10798v1 | 2024-06-16T03:46:23Z | 2024-06-16T03:46:23Z | Federated Learning Optimization: A Comparative Study of Data and Model
Exchange Strategies in Dynamic Networks | The promise and proliferation of large-scale dynamic federated learning gives rise to a prominent open question - is it prudent to share data or model across nodes, if efficiency of transmission and fast knowledge transfer are the prime objectives. This work investigates exactly that. Specifically, we study the choices of exchanging raw data, synthetic data, or (partial) model updates among devices. The implications of these strategies in the context of foundational models are also examined in detail. Accordingly, we obtain key insights about optimal data and model exchange mechanisms considering various environments with different data distributions and dynamic device and network connections. Across various scenarios that we considered, time-limited knowledge transfer efficiency can differ by up to 9.08%, thus highlighting the importance of this work. | [
"['Alka Luqman' 'Yeow Wei Liang Brandon' 'Anupam Chattopadhyay']"
] |
null | null | 2406.10807 | null | null | http://arxiv.org/pdf/2406.10807v2 | 2024-06-18T02:20:19Z | 2024-06-16T05:43:24Z | Bayesian Networks and Machine Learning for COVID-19 Severity Explanation
and Demographic Symptom Classification | With the prevailing efforts to combat the coronavirus disease 2019 (COVID-19) pandemic, there are still uncertainties that are yet to be discovered about its spread, future impact, and resurgence. In this paper, we present a three-stage data-driven approach to distill the hidden information about COVID-19. The first stage employs a Bayesian network structure learning method to identify the causal relationships among COVID-19 symptoms and their intrinsic demographic variables. As a second stage, the output from the Bayesian network structure learning, serves as a useful guide to train an unsupervised machine learning (ML) algorithm that uncovers the similarities in patients' symptoms through clustering. The final stage then leverages the labels obtained from clustering to train a demographic symptom identification (DSID) model which predicts a patient's symptom class and the corresponding demographic probability distribution. We applied our method on the COVID-19 dataset obtained from the Centers for Disease Control and Prevention (CDC) in the United States. Results from the experiments show a testing accuracy of 99.99%, as against the 41.15% accuracy of a heuristic ML method. This strongly reveals the viability of our Bayesian network and ML approach in understanding the relationship between the virus symptoms, and providing insights on patients' stratification towards reducing the severity of the virus. | [
"['Oluwaseun T. Ajayi' 'Yu Cheng']"
] |
null | null | 2406.10808 | null | null | http://arxiv.org/pdf/2406.10808v1 | 2024-06-16T05:47:12Z | 2024-06-16T05:47:12Z | Diffusion Model With Optimal Covariance Matching | The probabilistic diffusion model has become highly effective across various domains. Typically, sampling from a diffusion model involves using a denoising distribution characterized by a Gaussian with a learned mean and either fixed or learned covariances. In this paper, we leverage the recently proposed full covariance moment matching technique and introduce a novel method for learning covariances. Unlike traditional data-driven covariance approximation approaches, our method involves directly regressing the optimal analytic covariance using a new, unbiased objective named Optimal Covariance Matching (OCM). This approach can significantly reduce the approximation error in covariance prediction. We demonstrate how our method can substantially enhance the sampling efficiency of both Markovian (DDPM) and non-Markovian (DDIM) diffusion model families. | [
"['Zijing Ou' 'Mingtian Zhang' 'Andi Zhang' 'Tim Z. Xiao' 'Yingzhen Li'\n 'David Barber']"
] |
null | null | 2406.10815 | null | null | http://arxiv.org/pdf/2406.10815v1 | 2024-06-16T06:43:15Z | 2024-06-16T06:43:15Z | On the Effectiveness of Supervision in Asymmetric Non-Contrastive
Learning | Supervised contrastive representation learning has been shown to be effective in various transfer learning scenarios. However, while asymmetric non-contrastive learning (ANCL) often outperforms its contrastive learning counterpart in self-supervised representation learning, the extension of ANCL to supervised scenarios is less explored. To bridge the gap, we study ANCL for supervised representation learning, coined SupSiam and SupBYOL, leveraging labels in ANCL to achieve better representations. The proposed supervised ANCL framework improves representation learning while avoiding collapse. Our analysis reveals that providing supervision to ANCL reduces intra-class variance, and the contribution of supervision should be adjusted to achieve the best performance. Experiments demonstrate the superiority of supervised ANCL across various datasets and tasks. The code is available at: https://github.com/JH-Oh-23/Sup-ANCL. | [
"['Jeongheon Oh' 'Kibok Lee']"
] |
null | null | 2406.10834 | null | null | http://arxiv.org/pdf/2406.10834v1 | 2024-06-16T08:06:05Z | 2024-06-16T08:06:05Z | Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes
in Mathematical Reasoning | Large Language Models (LLMs) have been applied to Math Word Problems (MWPs) with transformative impacts, revolutionizing how these complex problems are approached and solved in various domains including educational settings. However, the evaluation of these models often prioritizes final accuracy, overlooking the crucial aspect of reasoning capabilities. This work addresses this gap by focusing on the ability of LLMs to detect and correct reasoning mistakes. We introduce a novel dataset MWP-MISTAKE, incorporating MWPs with both correct and incorrect reasoning steps generated through rule-based methods and smaller language models. Our comprehensive benchmarking reveals significant insights into the strengths and weaknesses of state-of-the-art models, such as GPT-4o, GPT-4, GPT-3.5Turbo, and others. We highlight GPT-$o's superior performance in mistake detection and rectification and the persistent challenges faced by smaller models. Additionally, we identify issues related to data contamination and memorization, impacting the reliability of LLMs in real-world applications. Our findings emphasize the importance of rigorous evaluation of reasoning processes and propose future directions to enhance the generalization and robustness of LLMs in mathematical problem-solving. | [
"['Joykirat Singh' 'Akshay Nambi' 'Vibhav Vineet']"
] |
null | null | 2406.10840 | null | null | http://arxiv.org/pdf/2406.10840v1 | 2024-06-16T08:20:24Z | 2024-06-16T08:20:24Z | CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph | Structure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. However, a lack of systematic understanding persists due to the diverse settings, complex implementation, difficult reproducibility, and task singularity. Firstly, the absence of standardization can lead to unfair comparisons and inconclusive insights. To address this dilemma, we propose CBGBench, a comprehensive benchmark for SBDD, that unifies the task as a generative heterogeneous graph completion, analogous to fill-in-the-blank of the 3D complex binding graph. By categorizing existing methods based on their attributes, CBGBench facilitates a modular and extensible framework that implements various cutting-edge methods. Secondly, a single task on textit{de novo} molecule generation can hardly reflect their capabilities. To broaden the scope, we have adapted these models to a range of tasks essential in drug design, which are considered sub-tasks within the graph fill-in-the-blank tasks. These tasks include the generative designation of textit{de novo} molecules, linkers, fragments, scaffolds, and sidechains, all conditioned on the structures of protein pockets. Our evaluations are conducted with fairness, encompassing comprehensive perspectives on interaction, chemical properties, geometry authenticity, and substructure validity. We further provide the pre-trained versions of the state-of-the-art models and deep insights with analysis from empirical studies. The codebase for CBGBench is publicly accessible at url{https://github.com/Edapinenut/CBGBench}. | [
"['Haitao Lin' 'Guojiang Zhao' 'Odin Zhang' 'Yufei Huang' 'Lirong Wu'\n 'Zicheng Liu' 'Siyuan Li' 'Cheng Tan' 'Zhifeng Gao' 'Stan Z. Li']"
] |
null | null | 2406.10843 | null | null | http://arxiv.org/pdf/2406.10843v1 | 2024-06-16T08:32:28Z | 2024-06-16T08:32:28Z | Enriching the Machine Learning Workloads in BigBench | In the era of Big Data and the growing support for Machine Learning, Deep Learning and Artificial Intelligence algorithms in the current software systems, there is an urgent need of standardized application benchmarks that stress test and evaluate these new technologies. Relying on the standardized BigBench (TPCx-BB) benchmark, this work enriches the improved BigBench V2 with three new workloads and expands the coverage of machine learning algorithms. Our workloads utilize multiple algorithms and compare different implementations for the same algorithm across several popular libraries like MLlib, SystemML, Scikit-learn and Pandas, demonstrating the relevance and usability of our benchmark extension. | [
"['Matthias Polag' 'Todor Ivanov' 'Timo Eichhorn']"
] |
null | null | 2406.10861 | null | null | http://arxiv.org/pdf/2406.10861v1 | 2024-06-16T09:12:16Z | 2024-06-16T09:12:16Z | Knowledge Distillation in Federated Learning: a Survey on Long Lasting
Challenges and New Solutions | Federated Learning (FL) is a distributed and privacy-preserving machine learning paradigm that coordinates multiple clients to train a model while keeping the raw data localized. However, this traditional FL poses some challenges, including privacy risks, data heterogeneity, communication bottlenecks, and system heterogeneity issues. To tackle these challenges, knowledge distillation (KD) has been widely applied in FL since 2020. KD is a validated and efficacious model compression and enhancement algorithm. The core concept of KD involves facilitating knowledge transfer between models by exchanging logits at intermediate or output layers. These properties make KD an excellent solution for the long-lasting challenges in FL. Up to now, there have been few reviews that summarize and analyze the current trend and methods for how KD can be applied in FL efficiently. This article aims to provide a comprehensive survey of KD-based FL, focusing on addressing the above challenges. First, we provide an overview of KD-based FL, including its motivation, basics, taxonomy, and a comparison with traditional FL and where KD should execute. We also analyze the critical factors in KD-based FL in the appendix, including teachers, knowledge, data, and methods. We discuss how KD can address the challenges in FL, including privacy protection, data heterogeneity, communication efficiency, and personalization. Finally, we discuss the challenges facing KD-based FL algorithms and future research directions. We hope this survey can provide insights and guidance for researchers and practitioners in the FL area. | [
"['Laiqiao Qin' 'Tianqing Zhu' 'Wanlei Zhou' 'Philip S. Yu']"
] |
null | null | 2406.10863 | null | null | http://arxiv.org/pdf/2406.10863v1 | 2024-06-16T09:13:30Z | 2024-06-16T09:13:30Z | Global-Local Graph Neural Networks for Node-Classification | The task of graph node classification is often approached by utilizing a local Graph Neural Network (GNN), that learns only local information from the node input features and their adjacency. In this paper, we propose to improve the performance of node classification GNNs by utilizing both global and local information, specifically by learning label- and node- features. We therefore call our method Global-Local-GNN (GLGNN). To learn proper label features, for each label, we maximize the similarity between its features and nodes features that belong to the label, while maximizing the distance between nodes that do not belong to the considered label. We then use the learnt label features to predict the node classification map. We demonstrate our GLGNN using three different GNN backbones, and show that our approach improves baseline performance, revealing the importance of global information utilization for node classification. | [
"['Moshe Eliasof' 'Eran Treister']"
] |
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