categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.06564 | null | null | http://arxiv.org/pdf/2406.06564v1 | 2024-06-03T05:40:34Z | 2024-06-03T05:40:34Z | Revolutionizing Large Language Model Training through Dynamic Parameter
Adjustment | In the era of large language models, the demand for efficient use of computational resources has become critically important. Although parameter-efficient fine-tuning techniques have achieved results comparable to full fine-tuning, their application during the pre-training phase poses significant challenges. Specifically, employing parameter-efficient strategies at the onset of pre-training can severely compromise efficiency, especially in larger models. In this paper, building upon the fine-tuning method LoRA, we introduce a novel parameter-efficient training technique that frequently alters trainable part of parameters, facilitating effective pre-training. Our method not only achieves memory reductions and computational overhead comparable to current state-of-the-art parameter-efficient algorithms during the pre-training phase but also maintains accuracy levels comparable to those of full pre-training. We provide both theoretical analyses and empirical evidence to demonstrate the effectiveness of our approach. | [
"['Kaiye Zhou' 'Shucheng Wang']"
] |
null | null | 2406.06565 | null | null | http://arxiv.org/pdf/2406.06565v1 | 2024-06-03T05:47:05Z | 2024-06-03T05:47:05Z | MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures | Evaluating large language models (LLMs) is challenging. Traditional ground-truth-based benchmarks fail to capture the comprehensiveness and nuance of real-world queries, while LLM-as-judge benchmarks suffer from grading biases and limited query quantity. Both of them may also become contaminated over time. User-facing evaluation, such as Chatbot Arena, provides reliable signals but is costly and slow. In this work, we propose MixEval, a new paradigm for establishing efficient, gold-standard LLM evaluation by strategically mixing off-the-shelf benchmarks. It bridges (1) comprehensive and well-distributed real-world user queries and (2) efficient and fairly-graded ground-truth-based benchmarks, by matching queries mined from the web with similar queries from existing benchmarks. Based on MixEval, we further build MixEval-Hard, which offers more room for model improvement. Our benchmarks' advantages lie in (1) a 0.96 model ranking correlation with Chatbot Arena arising from the highly impartial query distribution and grading mechanism, (2) fast, cheap, and reproducible execution (6% of the time and cost of MMLU), and (3) dynamic evaluation enabled by the rapid and stable data update pipeline. We provide extensive meta-evaluation and analysis for our and existing LLM benchmarks to deepen the community's understanding of LLM evaluation and guide future research directions. | [
"['Jinjie Ni' 'Fuzhao Xue' 'Xiang Yue' 'Yuntian Deng' 'Mahir Shah'\n 'Kabir Jain' 'Graham Neubig' 'Yang You']"
] |
null | null | 2406.06566 | null | null | http://arxiv.org/pdf/2406.06566v2 | 2024-07-11T13:05:37Z | 2024-06-03T07:44:32Z | Natural Language Interaction with a Household Electricity
Knowledge-based Digital Twin | Domain specific digital twins, representing a digital replica of various segments of the smart grid, are foreseen as able to model, simulate, and control the respective segments. At the same time, knowledge-based digital twins, coupled with AI, may also empower humans to understand aspects of the system through natural language interaction in view of planning and policy making. This paper is the first to assess and report on the potential of Retrieval Augmented Generation (RAG) question answers related to household electrical energy measurement aspects leveraging a knowledge-based energy digital twin. Relying on the recently published electricity consumption knowledge graph that actually represents a knowledge-based digital twin, we study the capabilities of ChatGPT, Gemini and Llama in answering electricity related questions. Furthermore, we compare the answers with the ones generated through a RAG techniques that leverages an existing electricity knowledge-based digital twin. Our findings illustrate that the RAG approach not only reduces the incidence of incorrect information typically generated by LLMs but also significantly improves the quality of the output by grounding responses in verifiable data. This paper details our methodology, presents a comparative analysis of responses with and without RAG, and discusses the implications of our findings for future applications of AI in specialized sectors like energy data analysis. | [
"['Carolina Fortuna' 'Vid Hanžel' 'Blaž Bertalanič']"
] |
null | null | 2406.06567 | null | null | http://arxiv.org/pdf/2406.06567v1 | 2024-06-03T13:28:43Z | 2024-06-03T13:28:43Z | DHA: Learning Decoupled-Head Attention from Transformer Checkpoints via
Adaptive Heads Fusion | Large language models (LLMs) with billions of parameters demonstrate impressive performance. However, the widely used Multi-Head Attention (MHA) in LLMs incurs substantial computational and memory costs during inference. While some efforts have optimized attention mechanisms by pruning heads or sharing parameters among heads, these methods often lead to performance degradation or necessitate substantial continued pre-training costs to restore performance. Based on the analysis of attention redundancy, we design a Decoupled-Head Attention (DHA) mechanism. DHA adaptively configures group sharing for key heads and value heads across various layers, achieving a better balance between performance and efficiency. Inspired by the observation of clustering similar heads, we propose to progressively transform the MHA checkpoint into the DHA model through linear fusion of similar head parameters step by step, retaining the parametric knowledge of the MHA checkpoint. We construct DHA models by transforming various scales of MHA checkpoints given target head budgets. Our experiments show that DHA remarkably requires a mere 0.25% of the original model's pre-training budgets to achieve 97.6% of performance while saving 75% of KV cache. Compared to Group-Query Attention (GQA), DHA achieves a 5$times$ training acceleration, a maximum of 13.93% performance improvement under 0.01% pre-training budget, and 4% relative improvement under 0.05% pre-training budget. | [
"['Yilong Chen' 'Linhao Zhang' 'Junyuan Shang' 'Zhenyu Zhang' 'Tingwen Liu'\n 'Shuohuan Wang' 'Yu Sun']"
] |
null | null | 2406.06569 | null | null | http://arxiv.org/abs/2406.06569v1 | 2024-06-03T15:49:03Z | 2024-06-03T15:49:03Z | Enhancing Clinical Documentation with Synthetic Data: Leveraging
Generative Models for Improved Accuracy | Accurate and comprehensive clinical documentation is crucial for delivering high-quality healthcare, facilitating effective communication among providers, and ensuring compliance with regulatory requirements. However, manual transcription and data entry processes can be time-consuming, error-prone, and susceptible to inconsistencies, leading to incomplete or inaccurate medical records. This paper proposes a novel approach to augment clinical documentation by leveraging synthetic data generation techniques to generate realistic and diverse clinical transcripts. We present a methodology that combines state-of-the-art generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), with real-world clinical transcript and other forms of clinical data to generate synthetic transcripts. These synthetic transcripts can then be used to supplement existing documentation workflows, providing additional training data for natural language processing models and enabling more accurate and efficient transcription processes. Through extensive experiments on a large dataset of anonymized clinical transcripts, we demonstrate the effectiveness of our approach in generating high-quality synthetic transcripts that closely resemble real-world data. Quantitative evaluation metrics, including perplexity scores and BLEU scores, as well as qualitative assessments by domain experts, validate the fidelity and utility of the generated synthetic transcripts. Our findings highlight synthetic data generation's potential to address clinical documentation challenges, improving patient care, reducing administrative burdens, and enhancing healthcare system efficiency. | [
"['Anjanava Biswas' 'Wrick Talukdar']"
] |
null | null | 2406.06573 | null | null | http://arxiv.org/pdf/2406.06573v1 | 2024-06-03T18:15:56Z | 2024-06-03T18:15:56Z | MedFuzz: Exploring the Robustness of Large Language Models in Medical
Question Answering | Large language models (LLM) have achieved impressive performance on medical question-answering benchmarks. However, high benchmark accuracy does not imply that the performance generalizes to real-world clinical settings. Medical question-answering benchmarks rely on assumptions consistent with quantifying LLM performance but that may not hold in the open world of the clinic. Yet LLMs learn broad knowledge that can help the LLM generalize to practical conditions regardless of unrealistic assumptions in celebrated benchmarks. We seek to quantify how well LLM medical question-answering benchmark performance generalizes when benchmark assumptions are violated. Specifically, we present an adversarial method that we call MedFuzz (for medical fuzzing). MedFuzz attempts to modify benchmark questions in ways aimed at confounding the LLM. We demonstrate the approach by targeting strong assumptions about patient characteristics presented in the MedQA benchmark. Successful "attacks" modify a benchmark item in ways that would be unlikely to fool a medical expert but nonetheless "trick" the LLM into changing from a correct to an incorrect answer. Further, we present a permutation test technique that can ensure a successful attack is statistically significant. We show how to use performance on a "MedFuzzed" benchmark, as well as individual successful attacks. The methods show promise at providing insights into the ability of an LLM to operate robustly in more realistic settings. | [
"['Robert Osazuwa Ness' 'Katie Matton' 'Hayden Helm' 'Sheng Zhang'\n 'Junaid Bajwa' 'Carey E. Priebe' 'Eric Horvitz']"
] |
null | null | 2406.06576 | null | null | http://arxiv.org/pdf/2406.06576v3 | 2024-06-29T19:13:23Z | 2024-06-04T04:17:40Z | OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step | Despite significant advancements in text generation and reasoning, Large Language Models (LLMs) still face challenges in accurately performing complex arithmetic operations. To achieve accurate calculations, language model systems often enable LLMs to generate code for arithmetic operations. However, this approach compromises speed and security and, if finetuning is involved, risks the language model losing prior capabilities. We propose a framework that enables exact arithmetic in textit{a single autoregressive step}, providing faster, more secure, and more interpretable LLM systems with arithmetic capabilities. We use the hidden states of an LLM to control a symbolic architecture which performs arithmetic. Our implementation using Llama 3 8B Instruct with OccamNet as a symbolic model (OccamLlama) achieves 100% accuracy on single arithmetic operations ($+,-,times,div,sin{},cos{},log{},exp{},sqrt{}$), outperforming GPT 4o and on par with GPT 4o using a code interpreter. OccamLlama also outperforms GPT 4o both with and without a code interpreter on mathematical problem solving benchmarks involving challenging arithmetic, thus enabling small LLMs to match the arithmetic performance of even much larger models. We will make our code public shortly. | [
"['Owen Dugan' 'Donato Manuel Jimenez Beneto' 'Charlotte Loh' 'Zhuo Chen'\n 'Rumen Dangovski' 'Marin Soljačić']"
] |
null | null | 2406.06578 | null | null | http://arxiv.org/abs/2406.06578v1 | 2024-06-04T13:44:36Z | 2024-06-04T13:44:36Z | SMS Spam Detection and Classification to Combat Abuse in Telephone
Networks Using Natural Language Processing | In the modern era, mobile phones have become ubiquitous, and Short Message Service (SMS) has grown to become a multi-million-dollar service due to the widespread adoption of mobile devices and the millions of people who use SMS daily. However, SMS spam has also become a pervasive problem that endangers users' privacy and security through phishing and fraud. Despite numerous spam filtering techniques, there is still a need for a more effective solution to address this problem [1]. This research addresses the pervasive issue of SMS spam, which poses threats to users' privacy and security. Despite existing spam filtering techniques, the high false-positive rate persists as a challenge. The study introduces a novel approach utilizing Natural Language Processing (NLP) and machine learning models, particularly BERT (Bidirectional Encoder Representations from Transformers), for SMS spam detection and classification. Data preprocessing techniques, such as stop word removal and tokenization, are applied, along with feature extraction using BERT. Machine learning models, including SVM, Logistic Regression, Naive Bayes, Gradient Boosting, and Random Forest, are integrated with BERT for differentiating spam from ham messages. Evaluation results revealed that the Na"ive Bayes classifier + BERT model achieves the highest accuracy at 97.31% with the fastest execution time of 0.3 seconds on the test dataset. This approach demonstrates a notable enhancement in spam detection efficiency and a low false-positive rate. The developed model presents a valuable solution to combat SMS spam, ensuring faster and more accurate detection. This model not only safeguards users' privacy but also assists network providers in effectively identifying and blocking SMS spam messages. | [
"['Dare Azeez Oyeyemi' 'Adebola K. Ojo']"
] |
null | null | 2406.06581 | null | null | http://arxiv.org/pdf/2406.06581v2 | 2024-06-12T13:59:13Z | 2024-06-04T16:09:13Z | Set-Based Prompting: Provably Solving the Language Model Order
Dependency Problem | The development of generative language models that can create long and coherent textual outputs via autoregression has lead to a proliferation of uses and a corresponding sweep of analyses as researches work to determine the limitations of this new paradigm. Unlike humans, these 'Large Language Models' (LLMs) are highly sensitive to small changes in their inputs, leading to unwanted inconsistency in their behavior. One problematic inconsistency when LLMs are used to answer multiple-choice questions or analyze multiple inputs is order dependency: the output of an LLM can (and often does) change significantly when sub-sequences are swapped, despite both orderings being semantically identical. In this paper we present Set-Based Prompting, a technique that guarantees the output of an LLM will not have order dependence on a specified set of sub-sequences. We show that this method provably eliminates order dependency, and that it can be applied to any transformer-based LLM to enable text generation that is unaffected by re-orderings. Delving into the implications of our method, we show that, despite our inputs being out of distribution, the impact on expected accuracy is small, where the expectation is over the order of uniformly chosen shuffling of the candidate responses, and usually significantly less in practice. Thus, Set-Based Prompting can be used as a 'dropped-in' method on fully trained models. Finally, we discuss how our method's success suggests that other strong guarantees can be obtained on LLM performance via modifying the input representations. | [
"['Reid McIlroy-Young' 'Katrina Brown' 'Conlan Olson' 'Linjun Zhang'\n 'Cynthia Dwork']"
] |
null | null | 2406.06582 | null | null | http://arxiv.org/pdf/2406.06582v2 | 2024-06-25T17:44:00Z | 2024-06-04T20:08:25Z | Discrete Multimodal Transformers with a Pretrained Large Language Model
for Mixed-Supervision Speech Processing | Recent work on discrete speech tokenization has paved the way for models that can seamlessly perform multiple tasks across modalities, e.g., speech recognition, text to speech, speech to speech translation. Moreover, large language models (LLMs) pretrained from vast text corpora contain rich linguistic information that can improve accuracy in a variety of tasks. In this paper, we present a decoder-only Discrete Multimodal Language Model (DMLM), which can be flexibly applied to multiple tasks (ASR, T2S, S2TT, etc.) and modalities (text, speech, vision). We explore several critical aspects of discrete multi-modal models, including the loss function, weight initialization, mixed training supervision, and codebook. Our results show that DMLM benefits significantly, across multiple tasks and datasets, from a combination of supervised and unsupervised training. Moreover, for ASR, it benefits from initializing DMLM from a pretrained LLM, and from a codebook derived from Whisper activations. | [
"['Viet Anh Trinh' 'Rosy Southwell' 'Yiwen Guan' 'Xinlu He' 'Zhiyong Wang'\n 'Jacob Whitehill']"
] |
null | null | 2406.06583 | null | null | http://arxiv.org/pdf/2406.06583v1 | 2024-06-04T21:18:24Z | 2024-06-04T21:18:24Z | Adaptive multiple optimal learning factors for neural network training | This thesis presents a novel approach to neural network training that addresses the challenge of determining the optimal number of learning factors. The proposed Adaptive Multiple Optimal Learning Factors (AMOLF) algorithm dynamically adjusts the number of learning factors based on the error change per multiply, leading to improved training efficiency and accuracy. The thesis also introduces techniques for grouping weights based on the curvature of the objective function and for compressing large Hessian matrices. Experimental results demonstrate the superior performance of AMOLF compared to existing methods like OWO-MOLF and Levenberg-Marquardt. | [
"['Jeshwanth Challagundla']"
] |
null | null | 2406.06585 | null | null | http://arxiv.org/pdf/2406.06585v1 | 2024-06-05T05:05:29Z | 2024-06-05T05:05:29Z | Expressive Symbolic Regression for Interpretable Models of Discrete-Time
Dynamical Systems | Interpretable mathematical expressions defining discrete-time dynamical systems (iterated maps) can model many phenomena of scientific interest, enabling a deeper understanding of system behaviors. Since formulating governing expressions from first principles can be difficult, it is of particular interest to identify expressions for iterated maps given only their data streams. In this work, we consider a modified Symbolic Artificial Neural Network-Trained Expressions (SymANNTEx) architecture for this task, an architecture more expressive than others in the literature. We make a modification to the model pipeline to optimize the regression, then characterize the behavior of the adjusted model in identifying several classical chaotic maps. With the goal of parsimony, sparsity-inducing weight regularization and information theory-informed simplification are implemented. We show that our modified SymANNTEx model properly identifies single-state maps and achieves moderate success in approximating a dual-state attractor. These performances offer significant promise for data-driven scientific discovery and interpretation. | [
"['Adarsh Iyer' 'Nibodh Boddupalli' 'Jeff Moehlis']"
] |
null | null | 2406.06588 | null | null | http://arxiv.org/pdf/2406.06588v1 | 2024-06-05T12:22:43Z | 2024-06-05T12:22:43Z | Assessing the Emergent Symbolic Reasoning Abilities of Llama Large
Language Models | Large Language Models (LLMs) achieve impressive performance in a wide range of tasks, even if they are often trained with the only objective of chatting fluently with users. Among other skills, LLMs show emergent abilities in mathematical reasoning benchmarks, which can be elicited with appropriate prompting methods. In this work, we systematically investigate the capabilities and limitations of popular open-source LLMs on different symbolic reasoning tasks. We evaluate three models of the Llama 2 family on two datasets that require solving mathematical formulas of varying degrees of difficulty. We test a generalist LLM (Llama 2 Chat) as well as two fine-tuned versions of Llama 2 (MAmmoTH and MetaMath) specifically designed to tackle mathematical problems. We observe that both increasing the scale of the model and fine-tuning it on relevant tasks lead to significant performance gains. Furthermore, using fine-grained evaluation measures, we find that such performance gains are mostly observed with mathematical formulas of low complexity, which nevertheless often remain challenging even for the largest fine-tuned models. | [
"['Flavio Petruzzellis' 'Alberto Testolin' 'Alessandro Sperduti']"
] |
null | null | 2406.06591 | null | null | http://arxiv.org/pdf/2406.06591v2 | 2024-06-12T15:19:46Z | 2024-06-05T16:11:55Z | Exploring Multilingual Large Language Models for Enhanced TNM
classification of Radiology Report in lung cancer staging | Background: Structured radiology reports remains underdeveloped due to labor-intensive structuring and narrative-style reporting. Deep learning, particularly large language models (LLMs) like GPT-3.5, offers promise in automating the structuring of radiology reports in natural languages. However, although it has been reported that LLMs are less effective in languages other than English, their radiological performance has not been extensively studied. Purpose: This study aimed to investigate the accuracy of TNM classification based on radiology reports using GPT3.5-turbo (GPT3.5) and the utility of multilingual LLMs in both Japanese and English. Material and Methods: Utilizing GPT3.5, we developed a system to automatically generate TNM classifications from chest CT reports for lung cancer and evaluate its performance. We statistically analyzed the impact of providing full or partial TNM definitions in both languages using a Generalized Linear Mixed Model. Results: Highest accuracy was attained with full TNM definitions and radiology reports in English (M = 94%, N = 80%, T = 47%, and ALL = 36%). Providing definitions for each of the T, N, and M factors statistically improved their respective accuracies (T: odds ratio (OR) = 2.35, p < 0.001; N: OR = 1.94, p < 0.01; M: OR = 2.50, p < 0.001). Japanese reports exhibited decreased N and M accuracies (N accuracy: OR = 0.74 and M accuracy: OR = 0.21). Conclusion: This study underscores the potential of multilingual LLMs for automatic TNM classification in radiology reports. Even without additional model training, performance improvements were evident with the provided TNM definitions, indicating LLMs' relevance in radiology contexts. | [
"['Hidetoshi Matsuo' 'Mizuho Nishio' 'Takaaki Matsunaga' 'Koji Fujimoto'\n 'Takamichi Murakami']"
] |
null | null | 2406.06592 | null | null | http://arxiv.org/pdf/2406.06592v1 | 2024-06-05T19:25:40Z | 2024-06-05T19:25:40Z | Improve Mathematical Reasoning in Language Models by Automated Process
Supervision | Complex multi-step reasoning tasks, such as solving mathematical problems or generating code, remain a significant hurdle for even the most advanced large language models (LLMs). Verifying LLM outputs with an Outcome Reward Model (ORM) is a standard inference-time technique aimed at enhancing the reasoning performance of LLMs. However, this still proves insufficient for reasoning tasks with a lengthy or multi-hop reasoning chain, where the intermediate outcomes are neither properly rewarded nor penalized. Process supervision addresses this limitation by assigning intermediate rewards during the reasoning process. To date, the methods used to collect process supervision data have relied on either human annotation or per-step Monte Carlo estimation, both prohibitively expensive to scale, thus hindering the broad application of this technique. In response to this challenge, we propose a novel divide-and-conquer style Monte Carlo Tree Search (MCTS) algorithm named textit{OmegaPRM} for the efficient collection of high-quality process supervision data. This algorithm swiftly identifies the first error in the Chain of Thought (CoT) with binary search and balances the positive and negative examples, thereby ensuring both efficiency and quality. As a result, we are able to collect over 1.5 million process supervision annotations to train a Process Reward Model (PRM). Utilizing this fully automated process supervision alongside the weighted self-consistency algorithm, we have enhanced the instruction tuned Gemini Pro model's math reasoning performance, achieving a 69.4% success rate on the MATH benchmark, a 36% relative improvement from the 51% base model performance. Additionally, the entire process operates without any human intervention, making our method both financially and computationally cost-effective compared to existing methods. | [
"['Liangchen Luo' 'Yinxiao Liu' 'Rosanne Liu' 'Samrat Phatale' 'Harsh Lara'\n 'Yunxuan Li' 'Lei Shu' 'Yun Zhu' 'Lei Meng' 'Jiao Sun' 'Abhinav Rastogi']"
] |
null | null | 2406.06593 | null | null | http://arxiv.org/pdf/2406.06593v1 | 2024-06-06T02:09:39Z | 2024-06-06T02:09:39Z | Differentiable Combinatorial Scheduling at Scale | This paper addresses the complex issue of resource-constrained scheduling, an NP-hard problem that spans critical areas including chip design and high-performance computing. Traditional scheduling methods often stumble over scalability and applicability challenges. We propose a novel approach using a differentiable combinatorial scheduling framework, utilizing Gumbel-Softmax differentiable sampling technique. This new technical allows for a fully differentiable formulation of linear programming (LP) based scheduling, extending its application to a broader range of LP formulations. To encode inequality constraints for scheduling tasks, we introduce textit{constrained Gumbel Trick}, which adeptly encodes arbitrary inequality constraints. Consequently, our method facilitates an efficient and scalable scheduling via gradient descent without the need for training data. Comparative evaluations on both synthetic and real-world benchmarks highlight our capability to significantly improve the optimization efficiency of scheduling, surpassing state-of-the-art solutions offered by commercial and open-source solvers such as CPLEX, Gurobi, and CP-SAT in the majority of the designs. | [
"['Mingju Liu' 'Yingjie Li' 'Jiaqi Yin' 'Zhiru Zhang' 'Cunxi Yu']"
] |
null | null | 2406.06594 | null | null | http://arxiv.org/pdf/2406.06594v1 | 2024-06-06T03:13:34Z | 2024-06-06T03:13:34Z | Stock Movement Prediction with Multimodal Stable Fusion via Gated
Cross-Attention Mechanism | The accurate prediction of stock movements is crucial for investment strategies. Stock prices are subject to the influence of various forms of information, including financial indicators, sentiment analysis, news documents, and relational structures. Predominant analytical approaches, however, tend to address only unimodal or bimodal sources, neglecting the complexity of multimodal data. Further complicating the landscape are the issues of data sparsity and semantic conflicts between these modalities, which are frequently overlooked by current models, leading to unstable performance and limiting practical applicability. To address these shortcomings, this study introduces a novel architecture, named Multimodal Stable Fusion with Gated Cross-Attention (MSGCA), designed to robustly integrate multimodal input for stock movement prediction. The MSGCA framework consists of three integral components: (1) a trimodal encoding module, responsible for processing indicator sequences, dynamic documents, and a relational graph, and standardizing their feature representations; (2) a cross-feature fusion module, where primary and consistent features guide the multimodal fusion of the three modalities via a pair of gated cross-attention networks; and (3) a prediction module, which refines the fused features through temporal and dimensional reduction to execute precise movement forecasting. Empirical evaluations demonstrate that the MSGCA framework exceeds current leading methods, achieving performance gains of 8.1%, 6.1%, 21.7% and 31.6% on four multimodal datasets, respectively, attributed to its enhanced multimodal fusion stability. | [
"['Chang Zong' 'Jian Shao' 'Weiming Lu' 'Yueting Zhuang']"
] |
null | null | 2406.06595 | null | null | http://arxiv.org/pdf/2406.06595v1 | 2024-06-06T07:36:25Z | 2024-06-06T07:36:25Z | Beyond 5G Network Failure Classification for Network Digital Twin Using
Graph Neural Network | Fifth-generation (5G) core networks in network digital twins (NDTs) are complex systems with numerous components, generating considerable data. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classes in multiclass classification. To address this problem, we propose a novel method of integrating a graph Fourier transform (GFT) into a message-passing neural network (MPNN) designed for NDTs. This approach transforms the data into a graph using the GFT to address class imbalance, whereas the MPNN extracts features and models dependencies between network components. This combined approach identifies failure types in real and simulated NDT environments, demonstrating its potential for accurate failure classification in 5G and beyond (B5G) networks. Moreover, the MPNN is adept at learning complex local structures among neighbors in an end-to-end setting. Extensive experiments have demonstrated that the proposed approach can identify failure types in three multiclass domain datasets at multiple failure points in real networks and NDT environments. The results demonstrate that the proposed GFT-MPNN can accurately classify network failures in B5G networks, especially when employed within NDTs to detect failure types. | [
"['Abubakar Isah' 'Ibrahim Aliyu' 'Jaechan Shim' 'Hoyong Ryu' 'Jinsul Kim']"
] |
null | null | 2406.06597 | null | null | http://arxiv.org/abs/2406.06597v1 | 2024-06-06T08:27:28Z | 2024-06-06T08:27:28Z | 1-D CNN-Based Online Signature Verification with Federated Learning | Online signature verification plays a pivotal role in security infrastructures. However, conventional online signature verification models pose significant risks to data privacy, especially during training processes. To mitigate these concerns, we propose a novel federated learning framework that leverages 1-D Convolutional Neural Networks (CNN) for online signature verification. Furthermore, our experiments demonstrate the effectiveness of our framework regarding 1-D CNN and federated learning. Particularly, the experiment results highlight that our framework 1) minimizes local computational resources; 2) enhances transfer effects with substantial initialization data; 3) presents remarkable scalability. The centralized 1-D CNN model achieves an Equal Error Rate (EER) of 3.33% and an accuracy of 96.25%. Meanwhile, configurations with 2, 5, and 10 agents yield EERs of 5.42%, 5.83%, and 5.63%, along with accuracies of 95.21%, 94.17%, and 94.06%, respectively. | [
"['Lingfeng Zhang' 'Yuheng Guo' 'Yepeng Ding' 'Hiroyuki Sato']"
] |
null | null | 2406.06599 | null | null | http://arxiv.org/pdf/2406.06599v1 | 2024-06-06T10:36:48Z | 2024-06-06T10:36:48Z | Anna Karenina Strikes Again: Pre-Trained LLM Embeddings May Favor
High-Performing Learners | Unsupervised clustering of student responses to open-ended questions into behavioral and cognitive profiles using pre-trained LLM embeddings is an emerging technique, but little is known about how well this captures pedagogically meaningful information. We investigate this in the context of student responses to open-ended questions in biology, which were previously analyzed and clustered by experts into theory-driven Knowledge Profiles (KPs). Comparing these KPs to ones discovered by purely data-driven clustering techniques, we report poor discoverability of most KPs, except for the ones including the correct answers. We trace this "discoverability bias" to the representations of KPs in the pre-trained LLM embeddings space. | [
"['Abigail Gurin Schleifer' 'Beata Beigman Klebanov' 'Moriah Ariely'\n 'Giora Alexandron']"
] |
null | null | 2406.06600 | null | null | http://arxiv.org/pdf/2406.06600v1 | 2024-06-06T13:44:57Z | 2024-06-06T13:44:57Z | HORAE: A Domain-Agnostic Modeling Language for Automating Multimodal
Service Regulation | Artificial intelligence is rapidly encroaching on the field of service regulation. This work presents the design principles behind HORAE, a unified specification language to model multimodal regulation rules across a diverse set of domains. We show how HORAE facilitates an intelligent service regulation pipeline by further exploiting a fine-tuned large language model named HORAE that automates the HORAE modeling process, thereby yielding an end-to-end framework for fully automated intelligent service regulation. | [
"['Yutao Sun' 'Mingshuai Chen' 'Kangjia Zhao' 'He Li' 'Jintao Chen'\n 'Linyu Yang' 'Zhongyi Wang' 'Tiancheng Zhao' 'Jianwei Yin']"
] |
null | null | 2406.06602 | null | null | http://arxiv.org/pdf/2406.06602v1 | 2024-06-06T14:03:52Z | 2024-06-06T14:03:52Z | Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on
Behavior | The surging demand for new energy vehicles is driven by the imperative to conserve energy, reduce emissions, and enhance the ecological ambiance. By conducting behavioral analysis and mining usage patterns of new energy vehicles, particular patterns can be identified. For instance, overloading the battery, operating with low battery power, and driving at excessive speeds can all detrimentally affect the battery's performance. To assess the impact of such driving behavior on the urban ecology, an environmental computational modeling method has been proposed to simulate the interaction between new energy vehicles and the environment. To extend the time series data of the vehicle's entire life cycle and the ecological environment within the model sequence data, the LSTM model with Bayesian optimizer is utilized for simulation. The analysis revealed the detrimental effects of poor driving behavior on the environment. | [
"['Run-Xuan Tang']"
] |
null | null | 2406.06603 | null | null | http://arxiv.org/pdf/2406.06603v1 | 2024-06-06T14:34:26Z | 2024-06-06T14:34:26Z | FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model | This study presents a novel time series prediction model, FPN-fusion, designed with linear computational complexity, demonstrating superior predictive performance compared to DLiner without increasing parameter count or computational demands. Our model introduces two key innovations: first, a Feature Pyramid Network (FPN) is employed to effectively capture time series data characteristics, bypassing the traditional decomposition into trend and seasonal components. Second, a multi-level fusion structure is developed to integrate deep and shallow features seamlessly. Empirically, FPN-fusion outperforms DLiner in 31 out of 32 test cases on eight open-source datasets, with an average reduction of 16.8% in mean squared error (MSE) and 11.8% in mean absolute error (MAE). Additionally, compared to the transformer-based PatchTST, FPN-fusion achieves 10 best MSE and 15 best MAE results, using only 8% of PatchTST's total computational load in the 32 test projects. | [
"['Chu Li' 'Pingjia Xiao' 'Qiping Yuan']"
] |
null | null | 2406.06607 | null | null | http://arxiv.org/pdf/2406.06607v1 | 2024-06-06T15:53:14Z | 2024-06-06T15:53:14Z | Continuous Test-time Domain Adaptation for Efficient Fault Detection
under Evolving Operating Conditions | Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have been gaining popularity for fault detection tasks as the amount of condition monitoring data from complex industrial systems increases. Despite these advances, early fault detection remains a challenge under real-world scenarios. The high variability of operating conditions and environments makes it difficult to collect comprehensive training datasets that can represent all possible operating conditions, especially in the early stages of system operation. Furthermore, these variations often evolve over time, potentially leading to entirely new data distributions in the future that were previously unseen. These challenges prevent direct knowledge transfer across different units and over time, leading to the distribution gap between training and testing data and inducing performance degradation of those methods in real-world scenarios. To overcome this, our work introduces a novel approach for continuous test-time domain adaptation. This enables early-stage robust anomaly detection by addressing domain shifts and limited data representativeness issues. We propose a Test-time domain Adaptation Anomaly Detection (TAAD) framework that separates input variables into system parameters and measurements, employing two domain adaptation modules to independently adapt to each input category. This method allows for effective adaptation to evolving operating conditions and is particularly beneficial in systems with scarce data. Our approach, tested on a real-world pump monitoring dataset, shows significant improvements over existing domain adaptation methods in fault detection, demonstrating enhanced accuracy and reliability. | [
"['Han Sun' 'Kevin Ammann' 'Stylianos Giannoulakis' 'Olga Fink']"
] |
null | null | 2406.06609 | null | null | http://arxiv.org/pdf/2406.06609v2 | 2024-07-10T17:58:14Z | 2024-06-06T18:52:28Z | Mitigating Bias in Dataset Distillation | Dataset Distillation has emerged as a technique for compressing large datasets into smaller synthetic counterparts, facilitating downstream training tasks. In this paper, we study the impact of bias inside the original dataset on the performance of dataset distillation. With a comprehensive empirical evaluation on canonical datasets with color, corruption and background biases, we found that color and background biases in the original dataset will be amplified through the distillation process, resulting in a notable decline in the performance of models trained on the distilled dataset, while corruption bias is suppressed through the distillation process. To reduce bias amplification in dataset distillation, we introduce a simple yet highly effective approach based on a sample reweighting scheme utilizing kernel density estimation. Empirical results on multiple real-world and synthetic datasets demonstrate the effectiveness of the proposed method. Notably, on CMNIST with 5% bias-conflict ratio and IPC 50, our method achieves 91.5% test accuracy compared to 23.8% from vanilla DM, boosting the performance by 67.7%, whereas applying state-of-the-art debiasing method on the same dataset only achieves 53.7% accuracy. Our findings highlight the importance of addressing biases in dataset distillation and provide a promising avenue to address bias amplification in the process. | [
"['Justin Cui' 'Ruochen Wang' 'Yuanhao Xiong' 'Cho-Jui Hsieh']"
] |
null | null | 2406.06610 | null | null | http://arxiv.org/pdf/2406.06610v1 | 2024-06-06T20:38:35Z | 2024-06-06T20:38:35Z | Reinterpreting 'the Company a Word Keeps': Towards Explainable and
Ontologically Grounded Language Models | We argue that the relative success of large language models (LLMs) is not a reflection on the symbolic vs. subsymbolic debate but a reflection on employing a successful bottom-up strategy of a reverse engineering of language at scale. However, and due to their subsymbolic nature whatever knowledge these systems acquire about language will always be buried in millions of weights none of which is meaningful on its own, rendering such systems utterly unexplainable. Furthermore, and due to their stochastic nature, LLMs will often fail in making the correct inferences in various linguistic contexts that require reasoning in intensional, temporal, or modal contexts. To remedy these shortcomings we suggest employing the same successful bottom-up strategy employed in LLMs but in a symbolic setting, resulting in explainable, language-agnostic, and ontologically grounded language models. | [
"['Walid S. Saba']"
] |
null | null | 2406.06611 | null | null | http://arxiv.org/pdf/2406.06611v1 | 2024-06-06T21:54:59Z | 2024-06-06T21:54:59Z | Building Hybrid B-Spline And Neural Network Operators | Control systems are indispensable for ensuring the safety of cyber-physical systems (CPS), spanning various domains such as automobiles, airplanes, and missiles. Safeguarding CPS necessitates runtime methodologies that continuously monitor safety-critical conditions and respond in a verifiably safe manner. A fundamental aspect of many safety approaches involves predicting the future behavior of systems. However, achieving this requires accurate models that can operate in real time. Motivated by DeepONets, we propose a novel strategy that combines the inductive bias of B-splines with data-driven neural networks to facilitate real-time predictions of CPS behavior. We introduce our hybrid B-spline neural operator, establishing its capability as a universal approximator and providing rigorous bounds on the approximation error. These findings are applicable to a broad class of nonlinear autonomous systems and are validated through experimentation on a controlled 6-degree-of-freedom (DOF) quadrotor with a 12 dimensional state space. Furthermore, we conduct a comparative analysis of different network architectures, specifically fully connected networks (FCNN) and recurrent neural networks (RNN), to elucidate the practical utility and trade-offs associated with each architecture in real-world scenarios. | [
"['Raffaele Romagnoli' 'Jasmine Ratchford' 'Mark H. Klein']"
] |
null | null | 2406.06612 | null | null | http://arxiv.org/pdf/2406.06612v1 | 2024-06-06T22:55:01Z | 2024-06-06T22:55:01Z | SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound | Generating combined visual and auditory sensory experiences is critical for the consumption of immersive content. Recent advances in neural generative models have enabled the creation of high-resolution content across multiple modalities such as images, text, speech, and videos. Despite these successes, there remains a significant gap in the generation of high-quality spatial audio that complements generated visual content. Furthermore, current audio generation models excel in either generating natural audio or speech or music but fall short in integrating spatial audio cues necessary for immersive experiences. In this work, we introduce SEE-2-SOUND, a zero-shot approach that decomposes the task into (1) identifying visual regions of interest; (2) locating these elements in 3D space; (3) generating mono-audio for each; and (4) integrating them into spatial audio. Using our framework, we demonstrate compelling results for generating spatial audio for high-quality videos, images, and dynamic images from the internet, as well as media generated by learned approaches. | [
"['Rishit Dagli' 'Shivesh Prakash' 'Robert Wu' 'Houman Khosravani']"
] |
null | null | 2406.06615 | null | null | http://arxiv.org/pdf/2406.06615v1 | 2024-06-07T04:25:38Z | 2024-06-07T04:25:38Z | Language Guided Skill Discovery | Skill discovery methods enable agents to learn diverse emergent behaviors without explicit rewards. To make learned skills useful for unknown downstream tasks, obtaining a semantically diverse repertoire of skills is essential. While some approaches introduce a discriminator to distinguish skills and others aim to increase state coverage, no existing work directly addresses the "semantic diversity" of skills. We hypothesize that leveraging the semantic knowledge of large language models (LLMs) can lead us to improve semantic diversity of resulting behaviors. In this sense, we introduce Language Guided Skill Discovery (LGSD), a skill discovery framework that aims to directly maximize the semantic diversity between skills. LGSD takes user prompts as input and outputs a set of semantically distinctive skills. The prompts serve as a means to constrain the search space into a semantically desired subspace, and the generated LLM outputs guide the agent to visit semantically diverse states within the subspace. We demonstrate that LGSD enables legged robots to visit different user-intended areas on a plane by simply changing the prompt. Furthermore, we show that language guidance aids in discovering more diverse skills compared to five existing skill discovery methods in robot-arm manipulation environments. Lastly, LGSD provides a simple way of utilizing learned skills via natural language. | [
"['Seungeun Rho' 'Laura Smith' 'Tianyu Li' 'Sergey Levine' 'Xue Bin Peng'\n 'Sehoon Ha']"
] |
null | null | 2406.06616 | null | null | http://arxiv.org/pdf/2406.06616v1 | 2024-06-07T06:44:09Z | 2024-06-07T06:44:09Z | Transforming Dental Diagnostics with Artificial Intelligence: Advanced
Integration of ChatGPT and Large Language Models for Patient Care | Artificial intelligence has dramatically reshaped our interaction with digital technologies, ushering in an era where advancements in AI algorithms and Large Language Models (LLMs) have natural language processing (NLP) systems like ChatGPT. This study delves into the impact of cutting-edge LLMs, notably OpenAI's ChatGPT, on medical diagnostics, with a keen focus on the dental sector. Leveraging publicly accessible datasets, these models augment the diagnostic capabilities of medical professionals, streamline communication between patients and healthcare providers, and enhance the efficiency of clinical procedures. The advent of ChatGPT-4 is poised to make substantial inroads into dental practices, especially in the realm of oral surgery. This paper sheds light on the current landscape and explores potential future research directions in the burgeoning field of LLMs, offering valuable insights for both practitioners and developers. Furthermore, it critically assesses the broad implications and challenges within various sectors, including academia and healthcare, thus mapping out an overview of AI's role in transforming dental diagnostics for enhanced patient care. | [
"['Masoumeh Farhadi Nia' 'Mohsen Ahmadi' 'Elyas Irankhah']"
] |
null | null | 2406.06617 | null | null | http://arxiv.org/pdf/2406.06617v1 | 2024-06-07T07:12:35Z | 2024-06-07T07:12:35Z | Collaborative Team Recognition: A Core Plus Extension Structure | Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collaborative teams and exploring inner patterns using scholarly big graph data. We propose a collaborative team recognition (CORE) model with a "core + extension" team structure to recognize collaborative teams in large academic networks. In CORE, we combine an effective evaluation index called the collaboration intensity index with a series of structural features to recognize collaborative teams in which members are in close collaboration relationships. Then, CORE is used to guide the core team members to their extension members. CORE can also serve as the foundation for team-based research. The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly. The experimental results demonstrate that CORE is promising compared with state-of-the-art methods. | [
"['Shuo Yu' 'Fayez Alqahtani' 'Amr Tolba' 'Ivan Lee' 'Tao Jia' 'Feng Xia']"
] |
null | null | 2406.06618 | null | null | http://arxiv.org/pdf/2406.06618v1 | 2024-06-07T07:27:22Z | 2024-06-07T07:27:22Z | PANDORA: Deep graph learning based COVID-19 infection risk level
forecasting | COVID-19 as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. Policymakers and all elements of society must deliver measurable actions based on the pandemic's severity to minimize the detrimental impact of COVID-19. A proper forecasting system is arguably important to provide an early signal of the risk of COVID-19 infection so that the authorities are ready to protect the people from the worst. However, making a good forecasting model for infection risks in different cities or regions is not an easy task, because it has a lot of influential factors that are difficult to be identified manually. To address the current limitations, we propose a deep graph learning model, called PANDORA, to predict the infection risks of COVID-19, by considering all essential factors and integrating them into a geographical network. The framework uses geographical position relations and transportation frequency as higher-order structural properties formulated by higher-order network structures (i.e., network motifs). Moreover, four significant node attributes (i.e., multiple features of a particular area, including climate, medical condition, economy, and human mobility) are also considered. We propose three different aggregators to better aggregate node attributes and structural features, namely, Hadamard, Summation, and Connection. Experimental results over real data show that PANDORA outperforms the baseline method with higher accuracy and faster convergence speed, no matter which aggregator is chosen. We believe that PANDORA using deep graph learning provides a promising approach to get superior performance in infection risk level forecasting and help humans battle the COVID-19 crisis. | [
"['Shuo Yu' 'Feng Xia' 'Yueru Wang' 'Shihao Li' 'Falih Febrinanto'\n 'Madhu Chetty']"
] |
null | null | 2406.06620 | null | null | http://arxiv.org/pdf/2406.06620v1 | 2024-06-07T14:34:28Z | 2024-06-07T14:34:28Z | DualTime: A Dual-Adapter Multimodal Language Model for Time Series
Representation | The recent rapid development of language models (LMs) has attracted attention in the field of time series, including multimodal time series modeling. However, we note that current time series multimodal methods are biased, often assigning a primary role to one modality while the other assumes a secondary role. They overlook the mutual benefits and complementary of different modalities. For example, in seizure diagnosis, relying solely on textual clinical reports makes it difficult to pinpoint the area and type of the disease, while electroencephalograms (EEGs) alone cannot provide an accurate diagnosis without considering the symptoms. In this study, based on the complementary information mining of time series multimodal data, we propose DualTime, a Dual-adapter multimodal language model for Time series representation implementing temporal-primary and textual-primary modeling simultaneously. By injecting lightweight adaption tokens, the LM pipeline shared by dual adapters encourages embedding alignment and achieves efficient fine-tuning. Empirically, our method outperforms state-of-the-art models in both supervised and unsupervised settings, highlighting the complementary benefits of different modalities. In addition, we conduct few-shot label transfer experiments, which further verifies the transferability and expressiveness of our proposed DualTime. | [
"['Weiqi Zhang' 'Jiexia Ye' 'Ziyue Li' 'Jia Li' 'Fugee Tsung']"
] |
null | null | 2406.06621 | null | null | http://arxiv.org/pdf/2406.06621v1 | 2024-06-07T15:28:31Z | 2024-06-07T15:28:31Z | LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph
Question-Answering | We present LinkQ, a system that leverages a large language model (LLM) to facilitate knowledge graph (KG) query construction through natural language question-answering. Traditional approaches often require detailed knowledge of complex graph querying languages, limiting the ability for users -- even experts -- to acquire valuable insights from KG data. LinkQ simplifies this process by first interpreting a user's question, then converting it into a well-formed KG query. By using the LLM to construct a query instead of directly answering the user's question, LinkQ guards against the LLM hallucinating or generating false, erroneous information. By integrating an LLM into LinkQ, users are able to conduct both exploratory and confirmatory data analysis, with the LLM helping to iteratively refine open-ended questions into precise ones. To demonstrate the efficacy of LinkQ, we conducted a qualitative study with five KG practitioners and distill their feedback. Our results indicate that practitioners find LinkQ effective for KG question-answering, and desire future LLM-assisted systems for the exploratory analysis of graph databases. | [
"['Harry Li' 'Gabriel Appleby' 'Ashley Suh']"
] |
null | null | 2406.06623 | null | null | http://arxiv.org/pdf/2406.06623v1 | 2024-06-07T21:20:57Z | 2024-06-07T21:20:57Z | Spectrum: Targeted Training on Signal to Noise Ratio | Efficiently post-training large language models remains a challenging task due to the vast computational resources required. We present Spectrum, a method that accelerates LLM training by selectively targeting layer modules based on their signal-to-noise ratio (SNR), and freezing the remaining modules. Our approach, which utilizes an algorithm to compute module SNRs prior to training, has shown to effectively match the performance of full fine-tuning while reducing GPU memory usage. Experiments comparing Spectrum to existing methods such as QLoRA demonstrate its effectiveness in terms of model quality and VRAM efficiency in distributed environments. | [
"['Eric Hartford' 'Lucas Atkins' 'Fernando Fernandes Neto'\n 'David Golchinfar']"
] |
null | null | 2406.06624 | null | null | http://arxiv.org/abs/2406.06624v1 | 2024-06-07T22:02:36Z | 2024-06-07T22:02:36Z | Exploring the Determinants of Pedestrian Crash Severity Using an AutoML
Approach | This study investigates pedestrian crash severity through Automated Machine Learning (AutoML), offering a streamlined and accessible method for analyzing critical factors. Utilizing a detailed dataset from Utah spanning 2010-2021, the research employs AutoML to assess the effects of various explanatory variables on crash outcomes. The study incorporates SHAP (SHapley Additive exPlanations) to interpret the contributions of individual features in the predictive model, enhancing the understanding of influential factors such as lighting conditions, road type, and weather on pedestrian crash severity. Emphasizing the efficiency and democratization of data-driven methodologies, the paper discusses the benefits of using AutoML in traffic safety analysis. This integration of AutoML with SHAP analysis not only bolsters predictive accuracy but also improves interpretability, offering critical insights into effective pedestrian safety measures. The findings highlight the potential of this approach in advancing the analysis of pedestrian crash severity. | [
"['Amir Rafe' 'Patrick A. Singleton']"
] |
null | null | 2406.06626 | null | null | http://arxiv.org/pdf/2406.06626v1 | 2024-06-08T02:45:36Z | 2024-06-08T02:45:36Z | Benchmarking Neural Decoding Backbones towards Enhanced On-edge iBCI
Applications | Traditional invasive Brain-Computer Interfaces (iBCIs) typically depend on neural decoding processes conducted on workstations within laboratory settings, which prevents their everyday usage. Implementing these decoding processes on edge devices, such as the wearables, introduces considerable challenges related to computational demands, processing speed, and maintaining accuracy. This study seeks to identify an optimal neural decoding backbone that boasts robust performance and swift inference capabilities suitable for edge deployment. We executed a series of neural decoding experiments involving nonhuman primates engaged in random reaching tasks, evaluating four prospective models, Gated Recurrent Unit (GRU), Transformer, Receptance Weighted Key Value (RWKV), and Selective State Space model (Mamba), across several metrics: single-session decoding, multi-session decoding, new session fine-tuning, inference speed, calibration speed, and scalability. The findings indicate that although the GRU model delivers sufficient accuracy, the RWKV and Mamba models are preferable due to their superior inference and calibration speeds. Additionally, RWKV and Mamba comply with the scaling law, demonstrating improved performance with larger data sets and increased model sizes, whereas GRU shows less pronounced scalability, and the Transformer model requires computational resources that scale prohibitively. This paper presents a thorough comparative analysis of the four models in various scenarios. The results are pivotal in pinpointing an optimal backbone that can handle increasing data volumes and is viable for edge implementation. This analysis provides essential insights for ongoing research and practical applications in the field. | [
"['Zhou Zhou' 'Guohang He' 'Zheng Zhang' 'Luziwei Leng' 'Qinghai Guo'\n 'Jianxing Liao' 'Xuan Song' 'Ran Cheng']"
] |
null | null | 2406.06627 | null | null | http://arxiv.org/pdf/2406.06627v1 | 2024-06-08T03:34:47Z | 2024-06-08T03:34:47Z | Rapid Review of Generative AI in Smart Medical Applications | With the continuous advancement of technology, artificial intelligence has significantly impacted various fields, particularly healthcare. Generative models, a key AI technology, have revolutionized medical image generation, data analysis, and diagnosis. This article explores their application in intelligent medical devices. Generative models enhance diagnostic speed and accuracy, improving medical service quality and efficiency while reducing equipment costs. These models show great promise in medical image generation, data analysis, and diagnosis. Additionally, integrating generative models with IoT technology facilitates real-time data analysis and predictions, offering smarter healthcare services and aiding in telemedicine. Challenges include computational demands, ethical concerns, and scenario-specific limitations. | [
"['Yuan Sun' 'Jorge Ortiz']"
] |
null | null | 2406.06629 | null | null | http://arxiv.org/pdf/2406.06629v1 | 2024-06-08T11:11:14Z | 2024-06-08T11:11:14Z | A Survey of Meta-features Used for Automated Selection of Algorithms for
Black-box Single-objective Continuous Optimization | The selection of the most appropriate algorithm to solve a given problem instance, known as algorithm selection, is driven by the potential to capitalize on the complementary performance of different algorithms across sets of problem instances. However, determining the optimal algorithm for an unseen problem instance has been shown to be a challenging task, which has garnered significant attention from researchers in recent years. In this survey, we conduct an overview of the key contributions to algorithm selection in the field of single-objective continuous black-box optimization. We present ongoing work in representation learning of meta-features for optimization problem instances, algorithm instances, and their interactions. We also study machine learning models for automated algorithm selection, configuration, and performance prediction. Through this analysis, we identify gaps in the state of the art, based on which we present ideas for further development of meta-feature representations. | [
"['Gjorgjina Cenikj' 'Ana Nikolikj' 'Gašper Petelin' 'Niki van Stein'\n 'Carola Doerr' 'Tome Eftimov']"
] |
null | null | 2406.06631 | null | null | http://arxiv.org/pdf/2406.06631v1 | 2024-06-08T17:52:24Z | 2024-06-08T17:52:24Z | Hinge-FM2I: An Approach using Image Inpainting for Interpolating Missing
Data in Univariate Time Series | Accurate time series forecasts are crucial for various applications, such as traffic management, electricity consumption, and healthcare. However, limitations in models and data quality can significantly impact forecasts accuracy. One common issue with data quality is the absence of data points, referred to as missing data. It is often caused by sensor malfunctions, equipment failures, or human errors. This paper proposes Hinge-FM2I, a novel method for handling missing data values in univariate time series data. Hinge-FM2I builds upon the strengths of the Forecasting Method by Image Inpainting (FM2I). FM2I has proven effective, but selecting the most accurate forecasts remain a challenge. To overcome this issue, we proposed a selection algorithm. Inspired by door hinges, Hinge-FM2I drops a data point either before or after the gap (left/right-hinge), then use FM2I for imputation, and then select the imputed gap based on the lowest error of the dropped data point. Hinge-FM2I was evaluated on a comprehensive sample composed of 1356 time series, extracted from the M3 competition benchmark dataset, with missing value rates ranging from 3.57% to 28.57%. Experimental results demonstrate that Hinge-FM2I significantly outperforms established methods such as, linear/spline interpolation, K-Nearest Neighbors (K-NN), and ARIMA. Notably, Hinge-FM2I achieves an average Symmetric Mean Absolute Percentage Error (sMAPE) score of 5.6% for small gaps, and up to 10% for larger ones. These findings highlight the effectiveness of Hinge-FM2I as a promising new method for addressing missing values in univariate time series data. | [
"['Noufel Saad' 'Maaroufi Nadir' 'Najib Mehdi' 'Bakhouya Mohamed']"
] |
null | null | 2406.06632 | null | null | http://arxiv.org/pdf/2406.06632v1 | 2024-06-08T20:09:17Z | 2024-06-08T20:09:17Z | Transfer Entropy in Graph Convolutional Neural Networks | Graph Convolutional Networks (GCN) are Graph Neural Networks where the convolutions are applied over a graph. In contrast to Convolutional Neural Networks, GCN's are designed to perform inference on graphs, where the number of nodes can vary, and the nodes are unordered. In this study, we address two important challenges related to GCNs: i) oversmoothing; and ii) the utilization of node relational properties (i.e., heterophily and homophily). Oversmoothing is the degradation of the discriminative capacity of nodes as a result of repeated aggregations. Heterophily is the tendency for nodes of different classes to connect, whereas homophily is the tendency of similar nodes to connect. We propose a new strategy for addressing these challenges in GCNs based on Transfer Entropy (TE), which measures of the amount of directed transfer of information between two time varying nodes. Our findings indicate that using node heterophily and degree information as a node selection mechanism, along with feature-based TE calculations, enhances accuracy across various GCN models. Our model can be easily modified to improve classification accuracy of a GCN model. As a trade off, this performance boost comes with a significant computational overhead when the TE is computed for many graph nodes. | [
"['Adrian Moldovan' 'Angel Caţaron' 'Răzvan Andonie']"
] |
null | null | 2406.06633 | null | null | http://arxiv.org/pdf/2406.06633v1 | 2024-06-09T07:29:55Z | 2024-06-09T07:29:55Z | PairCFR: Enhancing Model Training on Paired Counterfactually Augmented
Data through Contrastive Learning | Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classes. Training with CAD enhances model robustness against spurious features that happen to correlate with labels by spreading the casual relationships across different classes. Yet, recent research reveals that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information, inadvertently introducing biases that may impair performance on out-ofdistribution (OOD) datasets. To mitigate this issue, we employ contrastive learning to promote global feature alignment in addition to learning counterfactual clues. We theoretically prove that contrastive loss can encourage models to leverage a broader range of features beyond those modified ones. Comprehensive experiments on two human-edited CAD datasets demonstrate that our proposed method outperforms the state-of-the-art on OOD datasets. | [
"['Xiaoqi Qiu' 'Yongjie Wang' 'Xu Guo' 'Zhiwei Zeng' 'Yue Yu' 'Yuhong Feng'\n 'Chunyan Miao']"
] |
null | null | 2406.06634 | null | null | http://arxiv.org/pdf/2406.06634v1 | 2024-06-09T08:03:48Z | 2024-06-09T08:03:48Z | Sparse Binarization for Fast Keyword Spotting | With the increasing prevalence of voice-activated devices and applications, keyword spotting (KWS) models enable users to interact with technology hands-free, enhancing convenience and accessibility in various contexts. Deploying KWS models on edge devices, such as smartphones and embedded systems, offers significant benefits for real-time applications, privacy, and bandwidth efficiency. However, these devices often possess limited computational power and memory. This necessitates optimizing neural network models for efficiency without significantly compromising their accuracy. To address these challenges, we propose a novel keyword-spotting model based on sparse input representation followed by a linear classifier. The model is four times faster than the previous state-of-the-art edge device-compatible model with better accuracy. We show that our method is also more robust in noisy environments while being fast. Our code is available at: https://github.com/jsvir/sparknet. | [
"['Jonathan Svirsky' 'Uri Shaham' 'Ofir Lindenbaum']"
] |
null | null | 2406.06636 | null | null | http://arxiv.org/pdf/2406.06636v1 | 2024-06-09T09:03:11Z | 2024-06-09T09:03:11Z | LLM Questionnaire Completion for Automatic Psychiatric Assessment | We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The LLM is prompted to answer these questionnaires by impersonating the interviewee. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C), using a Random Forest regressor. Our approach is shown to enhance diagnostic accuracy compared to multiple baselines. It thus establishes a novel framework for interpreting unstructured psychological interviews, bridging the gap between narrative-driven and data-driven approaches for mental health assessment. | [
"['Gony Rosenman' 'Lior Wolf' 'Talma Hendler']"
] |
null | null | 2406.06638 | null | null | http://arxiv.org/pdf/2406.06638v1 | 2024-06-09T10:34:16Z | 2024-06-09T10:34:16Z | Particle Multi-Axis Transformer for Jet Tagging | Jet tagging is an essential categorization problem in high energy physics. In recent times, Deep Learning has not only risen to the challenge of jet tagging but also significantly improved its performance. In this article, we propose an idea of a new architecture, Particle Multi-Axis transformer (ParMAT) which is a modified version of Particle transformer (ParT). ParMAT contains local and global spatial interactions within a single unit which improves its ability to handle various input lengths. We trained our model on JETCLASS, a publicly available large dataset that contains 100M jets of 10 different classes of particles. By integrating a parallel attention mechanism and pairwise interactions of particles in the attention mechanism,ParMAT achieves robustness and higher accuracy over the ParT and ParticleNet. The scalability of the model to huge datasets and its ability to automatically extract essential features demonstrate its potential for enhancing jet tagging. | [
"['Muhammad Usman' 'M Husnain Shahid' 'Maheen Ejaz' 'Ummay Hani'\n 'Nayab Fatima' 'Abdul Rehman Khan' 'Asifullah Khan' 'Nasir Majid Mirza']"
] |
null | null | 2406.06641 | null | null | http://arxiv.org/pdf/2406.06641v1 | 2024-06-09T16:25:14Z | 2024-06-09T16:25:14Z | Investigation of the Impact of Economic and Social Factors on Energy
Demand through Natural Language Processing | The relationship between energy demand and variables such as economic activity and weather is well established. However, this paper aims to explore the connection between energy demand and other social aspects, which receive little attention. Through the use of natural language processing on a large news corpus, we shed light on this important link. This study was carried out in five regions of the UK and Ireland and considers multiple horizons from 1 to 30 days. It also considers economic variables such as GDP, unemployment and inflation. We found that: 1) News about military conflicts, transportation, the global pandemic, regional economics, and the international energy market are related to electricity demand. 2) Economic indicators are more important in the East Midlands and Northern Ireland, while social indicators are more useful in the West Midlands and the South West of England. 3) The use of these indices improved forecasting performance by up to 9%. | [
"['Yun Bai' 'Simon Camal' 'Andrea Michiorri']"
] |
null | null | 2406.06642 | null | null | http://arxiv.org/pdf/2406.06642v1 | 2024-06-09T18:31:19Z | 2024-06-09T18:31:19Z | TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning | This work introduces TopoBenchmarkX, a modular open-source library designed to standardize benchmarking and accelerate research in Topological Deep Learning (TDL). TopoBenchmarkX maps the TDL pipeline into a sequence of independent and modular components for data loading and processing, as well as model training, optimization, and evaluation. This modular organization provides flexibility for modifications and facilitates the adaptation and optimization of various TDL pipelines. A key feature of TopoBenchmarkX is that it allows for the transformation and lifting between topological domains. This enables, for example, to obtain richer data representations and more fine-grained analyses by mapping the topology and features of a graph to higher-order topological domains such as simplicial and cell complexes. The range of applicability of TopoBenchmarkX is demonstrated by benchmarking several TDL architectures for various tasks and datasets. | [
"['Lev Telyatnikov' 'Guillermo Bernardez' 'Marco Montagna'\n 'Pavlo Vasylenko' 'Ghada Zamzmi' 'Mustafa Hajij' 'Michael T Schaub'\n 'Nina Miolane' 'Simone Scardapane' 'Theodore Papamarkou']"
] |
null | null | 2406.06644 | null | null | http://arxiv.org/pdf/2406.06644v2 | 2024-06-24T23:41:23Z | 2024-06-09T23:39:31Z | Latent Diffusion Model-Enabled Real-Time Semantic Communication
Considering Semantic Ambiguities and Channel Noises | Semantic communication (SemCom) has emerged as a new paradigm for 6G communication, with deep learning (DL) models being one of the key drives to shift from the accuracy of bit/symbol to the semantics and pragmatics of data. Nevertheless, DL-based SemCom systems often face performance bottlenecks due to overfitting, poor generalization, and sensitivity to outliers. Furthermore, the varying-fading gains and noises with uncertain signal-to-noise ratios (SNRs) commonly present in wireless channels usually restrict the accuracy of semantic information transmission. Consequently, this paper constructs a latent diffusion model-enabled SemCom system, and proposes three improvements compared to existing works: i) To handle potential outliers in the source data, semantic errors obtained by projected gradient descent based on the vulnerabilities of DL models, are utilized to update the parameters and obtain an outlier-robust encoder. ii) A lightweight single-layer latent space transformation adapter completes one-shot learning at the transmitter and is placed before the decoder at the receiver, enabling adaptation for out-of-distribution data and enhancing human-perceptual quality. iii) An end-to-end consistency distillation (EECD) strategy is used to distill the diffusion models trained in latent space, enabling deterministic single or few-step real-time denoising in various noisy channels while maintaining high semantic quality. Extensive numerical experiments across different datasets demonstrate the superiority of the proposed SemCom system, consistently proving its robustness to outliers, the capability to transmit data with unknown distributions, and the ability to perform real-time channel denoising tasks while preserving high human perceptual quality, outperforming the existing denoising approaches in semantic metrics. | [
"['Jianhua Pei' 'Cheng Feng' 'Ping Wang' 'Hina Tabassum' 'Dongyuan Shi']"
] |
null | null | 2406.06645 | null | null | http://arxiv.org/pdf/2406.06645v2 | 2024-06-13T18:02:17Z | 2024-06-10T00:51:20Z | Network-Based Transfer Learning Helps Improve Short-Term Crime
Prediction Accuracy | Deep learning architectures enhanced with human mobility data have been shown to improve the accuracy of short-term crime prediction models trained with historical crime data. However, human mobility data may be scarce in some regions, negatively impacting the correct training of these models. To address this issue, we propose a novel transfer learning framework for short-term crime prediction models, whereby weights from the deep learning crime prediction models trained in source regions with plenty of mobility data are transferred to target regions to fine-tune their local crime prediction models and improve crime prediction accuracy. Our results show that the proposed transfer learning framework improves the F1 scores for target cities with mobility data scarcity, especially when the number of months of available mobility data is small. We also show that the F1 score improvements are pervasive across different types of crimes and diverse cities in the US. | [
"['Jiahui Wu' 'Vanessa Frias-Martinez']"
] |
null | null | 2406.06647 | null | null | http://arxiv.org/pdf/2406.06647v2 | 2024-06-16T19:34:04Z | 2024-06-10T04:19:20Z | How Efficient is LLM-Generated Code? A Rigorous & High-Standard
Benchmark | The emergence of large language models (LLMs) has significantly pushed the frontiers of program synthesis. Advancement of LLM-based program synthesis calls for a thorough evaluation of LLM-generated code. Most evaluation frameworks focus on the (functional) correctness of generated code; efficiency, as an important measure of code quality, has been overlooked in existing evaluations. In this work, we develop ENAMEL (EfficeNcy AutoMatic EvaLuator), a rigorous and high-standard benchmark for evaluating the capability of LLMs in generating efficient code. Firstly, we propose a new efficiency metric called eff@k, which generalizes the pass@k metric from correctness to efficiency and appropriately handles right-censored execution time. Furthermore, we derive an unbiased and variance-reduced estimator of eff@k via Rao--Blackwellization; we also provide a numerically stable implementation for the new estimator. Secondly, to set a high-standard for efficiency evaluation, we employ a human expert to design best algorithms and implementations as our reference solutions of efficiency, many of which are much more efficient than existing canonical solutions in HumanEval and HumanEval+. Moreover, to ensure a rigorous evaluation, we employ a human expert to curate strong test case generators to filter out wrong code and differentiate suboptimal algorithms. An extensive study across 30 popular LLMs using our benchmark ENAMEL shows that LLMs still fall short of generating expert-level efficient code. Using two subsets of our problem set, we demonstrate that such deficiency is because current LLMs struggle in designing advanced algorithms and are barely aware of implementation optimization. Our benchmark is publicly available at https://github.com/q-rz/enamel . | [
"['Ruizhong Qiu' 'Weiliang Will Zeng' 'Hanghang Tong' 'James Ezick'\n 'Christopher Lott']"
] |
null | null | 2406.06648 | null | null | http://arxiv.org/pdf/2406.06648v1 | 2024-06-10T05:01:26Z | 2024-06-10T05:01:26Z | SignBLEU: Automatic Evaluation of Multi-channel Sign Language
Translation | Sign languages are multi-channel languages that communicate information through not just the hands (manual signals) but also facial expressions and upper body movements (non-manual signals). However, since automatic sign language translation is usually performed by generating a single sequence of glosses, researchers eschew non-manual and co-occurring manual signals in favor of a simplified list of manual glosses. This can lead to significant information loss and ambiguity. In this paper, we introduce a new task named multi-channel sign language translation (MCSLT) and present a novel metric, SignBLEU, designed to capture multiple signal channels. We validated SignBLEU on a system-level task using three sign language corpora with varied linguistic structures and transcription methodologies and examined its correlation with human judgment through two segment-level tasks. We found that SignBLEU consistently correlates better with human judgment than competing metrics. To facilitate further MCSLT research, we report benchmark scores for the three sign language corpora and release the source code for SignBLEU at https://github.com/eq4all-projects/SignBLEU. | [
"['Jung-Ho Kim' 'Mathew Huerta-Enochian' 'Changyong Ko' 'Du Hui Lee']"
] |
null | null | 2406.06649 | null | null | http://arxiv.org/pdf/2406.06649v1 | 2024-06-10T06:06:11Z | 2024-06-10T06:06:11Z | 2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution | Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment, which allows advanced SR models to enjoy compact low-bit parameters and efficient integer/bitwise constructions for storage compression and inference acceleration, respectively. However, it is notorious that low-bit quantization degrades the accuracy of SR models compared to their full-precision (FP) counterparts. Despite several efforts to alleviate the degradation, the transformer-based SR model still suffers severe degradation due to its distinctive activation distribution. In this work, we present a dual-stage low-bit post-training quantization (PTQ) method for image super-resolution, namely 2DQuant, which achieves efficient and accurate SR under low-bit quantization. The proposed method first investigates the weight and activation and finds that the distribution is characterized by coexisting symmetry and asymmetry, long tails. Specifically, we propose Distribution-Oriented Bound Initialization (DOBI), using different searching strategies to search a coarse bound for quantizers. To obtain refined quantizer parameters, we further propose Distillation Quantization Calibration (DQC), which employs a distillation approach to make the quantized model learn from its FP counterpart. Through extensive experiments on different bits and scaling factors, the performance of DOBI can reach the state-of-the-art (SOTA) while after stage two, our method surpasses existing PTQ in both metrics and visual effects. 2DQuant gains an increase in PSNR as high as 4.52dB on Set5 (x2) compared with SOTA when quantized to 2-bit and enjoys a 3.60x compression ratio and 5.08x speedup ratio. The code and models will be available at https://github.com/Kai-Liu001/2DQuant. | [
"['Kai Liu' 'Haotong Qin' 'Yong Guo' 'Xin Yuan' 'Linghe Kong' 'Guihai Chen'\n 'Yulun Zhang']"
] |
null | null | 2406.06651 | null | null | http://arxiv.org/pdf/2406.06651v1 | 2024-06-10T09:02:07Z | 2024-06-10T09:02:07Z | Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with
Stacked BiLSTM | The precise forecasting of electricity demand also referred to as load forecasting, is essential for both planning and managing a power system. It is crucial for many tasks, including choosing which power units to commit to, making plans for future power generation capacity, enhancing the power network, and controlling electricity consumption. As Bangladesh is a developing country, the electricity infrastructure is critical for economic growth and employment in this country. Accurate forecasting of electricity demand is crucial for ensuring that this country has a reliable and sustainable electricity supply to meet the needs of its growing population and economy. The complex and nonlinear behavior of such energy systems inhibits the creation of precise algorithms. Within this context, this paper aims to propose a hybrid model of Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture to perform an accurate short-term forecast of the electricity demand of Dhaka city. Short-term forecasting is ordinarily done to anticipate load for the following few hours to a few weeks. Normalization techniques have been also investigated because of the sensitivity of these models towards the input range. The proposed approach produced the best prediction results in comparison to the other benchmark models (LSTM, CNN- BiLSTM and CNN-LSTM) used in the study, with MAPE 1.64%, MSE 0.015, RMSE 0.122 and MAE 0.092. The result of the proposed model also outperformed some of the existing works on load-forecasting. | [
"['Kazi Fuad Bin Akhter' 'Sadia Mobasshira' 'Saief Nowaz Haque'\n 'Mahjub Alam Khan Hesham' 'Tanvir Ahmed']"
] |
null | null | 2406.06652 | null | null | http://arxiv.org/pdf/2406.06652v2 | 2024-06-17T14:02:57Z | 2024-06-10T09:03:17Z | Improving Generalization of Neural Vehicle Routing Problem Solvers
Through the Lens of Model Architecture | Neural models produce promising results when solving Vehicle Routing Problems (VRPs), but often fall short in generalization. Recent attempts to enhance model generalization often incur unnecessarily large training cost or cannot be directly applied to other models solving different VRP variants. To address these issues, we take a novel perspective on model architecture in this study. Specifically, we propose a plug-and-play Entropy-based Scaling Factor (ESF) and a Distribution-Specific (DS) decoder to enhance the size and distribution generalization, respectively. ESF adjusts the attention weight pattern of the model towards familiar ones discovered during training when solving VRPs of varying sizes. The DS decoder explicitly models VRPs of multiple training distribution patterns through multiple auxiliary light decoders, expanding the model representation space to encompass a broader range of distributional scenarios. We conduct extensive experiments on both synthetic and widely recognized real-world benchmarking datasets and compare the performance with seven baseline models. The results demonstrate the effectiveness of using ESF and DS decoder to obtain a more generalizable model and showcase their applicability to solve different VRP variants, i.e., travelling salesman problem and capacitated VRP. Notably, our proposed generic components require minimal computational resources, and can be effortlessly integrated into conventional generalization strategies to further elevate model generalization. | [
"['Yubin Xiao' 'Di Wang' 'Xuan Wu' 'Yuesong Wu' 'Boyang Li' 'Wei Du'\n 'Liupu Wang' 'You Zhou']"
] |
null | null | 2406.06653 | null | null | http://arxiv.org/pdf/2406.06653v2 | 2024-06-20T22:35:13Z | 2024-06-10T09:09:08Z | DKDL-Net: A Lightweight Bearing Fault Detection Model via Decoupled
Knowledge Distillation and Low-Rank Adaptation Fine-tuning | Rolling bearing fault detection has developed rapidly in the field of fault diagnosis technology, and it occupies a very important position in this field. Deep learning-based bearing fault diagnosis models have achieved significant success. At the same time, with the continuous improvement of new signal processing technologies such as Fourier transform, wavelet transform and empirical mode decomposition, the fault diagnosis technology of rolling bearings has also been greatly developed, and it can be said that it has entered a new research stage. However, most of the existing methods are limited to varying degrees in the industrial field. The main ones are fast feature extraction and computational complexity. The key to this paper is to propose a lightweight bearing fault diagnosis model DKDL-Net to solve these challenges. The model is trained on the CWRU data set by decoupling knowledge distillation and low rank adaptive fine tuning. Specifically, we built and trained a teacher model based on a 6-layer neural network with 69,626 trainable parameters, and on this basis, using decoupling knowledge distillation (DKD) and Low-Rank adaptive (LoRA) fine-tuning, we trained the student sag model DKDL-Net, which has only 6838 parameters. Experiments show that DKDL-Net achieves 99.48% accuracy in computational complexity on the test set while maintaining model performance, which is 0.58% higher than the state-of-the-art (SOTA) model, and our model has lower parameters. Our code is available at Github link: https://github.com/SPBU-LiPengyi/DKDL-Net.git. | [
"['Ovanes Petrosian' 'Li Pengyi' 'He Yulong' 'Liu Jiarui' 'Sun Zhaoruikun'\n 'Fu Guofeng' 'Meng Liping']"
] |
null | null | 2406.06654 | null | null | http://arxiv.org/pdf/2406.06654v1 | 2024-06-10T09:23:00Z | 2024-06-10T09:23:00Z | Training and Validating a Treatment Recommender with Partial
Verification Evidence | Current clinical decision support systems (DSS) are trained and validated on observational data from the target clinic. This is problematic for treatments validated in a randomized clinical trial (RCT), but not yet introduced in any clinic. In this work, we report on a method for training and validating the DSS using the RCT data. The key challenges we address are of missingness -- missing rationale for treatment assignment (the assignment is at random), and missing verification evidence, since the effectiveness of a treatment for a patient can only be verified (ground truth) for treatments what were actually assigned to a patient. We use data from a multi-armed RCT that investigated the effectiveness of single- and combination- treatments for 240+ tinnitus patients recruited and treated in 5 clinical centers. To deal with the 'missing rationale' challenge, we re-model the target variable (outcome) in order to suppress the effect of the randomly-assigned treatment, and control on the effect of treatment in general. Our methods are also robust to missing values in features and with a small number of patients per RCT arm. We deal with 'missing verification evidence' by using counterfactual treatment verification, which compares the effectiveness of the DSS recommendations to the effectiveness of the RCT assignments when they are aligned v/s not aligned. We demonstrate that our approach leverages the RCT data for learning and verification, by showing that the DSS suggests treatments that improve the outcome. The results are limited through the small number of patients per treatment; while our ensemble is designed to mitigate this effect, the predictive performance of the methods is affected by the smallness of the data. We provide a basis for the establishment of decision supporting routines on treatments that have been tested in RCTs but have not yet been deployed clinically. | [
"['Vishnu Unnikrishnan' 'Clara Puga' 'Miro Schleicher' 'Uli Niemann'\n 'Berthod Langguth' 'Stefan Schoisswohl' 'Birgit Mazurek' 'Rilana Cima'\n 'Jose Antonio Lopez-Escamez' 'Dimitris Kikidis' 'Eleftheria Vellidou'\n 'Ruediger Pryss' 'Winfried Schlee' 'Myra Spiliopoulou']"
] |
null | null | 2406.06655 | null | null | http://arxiv.org/pdf/2406.06655v1 | 2024-06-10T09:57:30Z | 2024-06-10T09:57:30Z | Fed-Sophia: A Communication-Efficient Second-Order Federated Learning
Algorithm | Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradient-based optimization techniques are widely adopted in this domain, the curvature information that second-order methods exhibit is crucial to guide and speed up the convergence. This paper introduces a scalable second-order method, allowing the adoption of curvature information in federated large models. Our method, coined Fed-Sophia, combines a weighted moving average of the gradient with a clipping operation to find the descent direction. In addition to that, a lightweight estimation of the Hessian's diagonal is used to incorporate the curvature information. Numerical evaluation shows the superiority, robustness, and scalability of the proposed Fed-Sophia scheme compared to first and second-order baselines. | [
"['Ahmed Elbakary' 'Chaouki Ben Issaid' 'Mohammad Shehab' 'Karim Seddik'\n 'Tamer ElBatt' 'Mehdi Bennis']"
] |
null | null | 2406.06660 | null | null | http://arxiv.org/pdf/2406.06660v1 | 2024-06-10T11:49:11Z | 2024-06-10T11:49:11Z | Space-Time Continuous PDE Forecasting using Equivariant Neural Fields | Recently, Conditional Neural Fields (NeFs) have emerged as a powerful modelling paradigm for PDEs, by learning solutions as flows in the latent space of the Conditional NeF. Although benefiting from favourable properties of NeFs such as grid-agnosticity and space-time-continuous dynamics modelling, this approach limits the ability to impose known constraints of the PDE on the solutions -- e.g. symmetries or boundary conditions -- in favour of modelling flexibility. Instead, we propose a space-time continuous NeF-based solving framework that - by preserving geometric information in the latent space - respects known symmetries of the PDE. We show that modelling solutions as flows of pointclouds over the group of interest $G$ improves generalization and data-efficiency. We validated that our framework readily generalizes to unseen spatial and temporal locations, as well as geometric transformations of the initial conditions - where other NeF-based PDE forecasting methods fail - and improve over baselines in a number of challenging geometries. | [
"['David M. Knigge' 'David R. Wessels' 'Riccardo Valperga' 'Samuele Papa'\n 'Jan-Jakob Sonke' 'Efstratios Gavves' 'Erik J. Bekkers']"
] |
null | null | 2406.06662 | null | null | http://arxiv.org/pdf/2406.06662v1 | 2024-06-10T12:37:47Z | 2024-06-10T12:37:47Z | Proximity Matters: Analyzing the Role of Geographical Proximity in
Shaping AI Research Collaborations | The role of geographical proximity in facilitating inter-regional or inter-organizational collaborations has been studied thoroughly in recent years. However, the effect of geographical proximity on forming scientific collaborations at the individual level still needs to be addressed. Using publication data in the field of artificial intelligence from 2001 to 2019, in this work, the effect of geographical proximity on the likelihood of forming future scientific collaborations among researchers is studied. In addition, the interaction between geographical and network proximities is examined to see whether network proximity can substitute geographical proximity in encouraging long-distance scientific collaborations. Employing conventional and machine learning techniques, our results suggest that geographical distance impedes scientific collaboration at the individual level despite the tremendous improvements in transportation and communication technologies during recent decades. Moreover, our findings show that the effect of network proximity on the likelihood of scientific collaboration increases with geographical distance, implying that network proximity can act as a substitute for geographical proximity. | [
"['Mohammadmahdi Toobaee' 'Andrea Schiffauerova' 'Ashkan Ebadi']"
] |
null | null | 2406.06663 | null | null | http://arxiv.org/pdf/2406.06663v1 | 2024-06-10T13:13:39Z | 2024-06-10T13:13:39Z | SecureNet: A Comparative Study of DeBERTa and Large Language Models for
Phishing Detection | Phishing, whether through email, SMS, or malicious websites, poses a major threat to organizations by using social engineering to trick users into revealing sensitive information. It not only compromises company's data security but also incurs significant financial losses. In this paper, we investigate whether the remarkable performance of Large Language Models (LLMs) can be leveraged for particular task like text classification, particularly detecting malicious content and compare its results with state-of-the-art Deberta V3 (DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing) model. We systematically assess the potential and limitations of both approaches using comprehensive public datasets comprising diverse data sources such as email, HTML, URL, SMS, and synthetic data generation. Additionally, we demonstrate how LLMs can generate convincing phishing emails, making it harder to spot scams and evaluate the performance of both models in this context. Our study delves further into the challenges encountered by DeBERTa V3 during its training phases, fine-tuning methodology and transfer learning processes. Similarly, we examine the challenges associated with LLMs and assess their respective performance. Among our experimental approaches, the transformer-based DeBERTa method emerged as the most effective, achieving a test dataset (HuggingFace phishing dataset) recall (sensitivity) of 95.17% closely followed by GPT-4 providing a recall of 91.04%. We performed additional experiments with other datasets on the trained DeBERTa V3 model and LLMs like GPT 4 and Gemini 1.5. Based on our findings, we provide valuable insights into the effectiveness and robustness of these advanced language models, offering a detailed comparative analysis that can inform future research efforts in strengthening cybersecurity measures for detecting and mitigating phishing threats. | [
"['Sakshi Mahendru' 'Tejul Pandit']"
] |
null | null | 2406.06664 | null | null | http://arxiv.org/pdf/2406.06664v2 | 2024-06-13T15:39:03Z | 2024-06-10T15:39:04Z | ASTRA: Aligning Speech and Text Representations for Asr without Sampling | This paper introduces ASTRA, a novel method for improving Automatic Speech Recognition (ASR) through text injection.Unlike prevailing techniques, ASTRA eliminates the need for sampling to match sequence lengths between speech and text modalities. Instead, it leverages the inherent alignments learned within CTC/RNNT models. This approach offers the following two advantages, namely, avoiding potential misalignment between speech and text features that could arise from upsampling and eliminating the need for models to accurately predict duration of sub-word tokens. This novel formulation of modality (length) matching as a weighted RNNT objective matches the performance of the state-of-the-art duration-based methods on the FLEURS benchmark, while opening up other avenues of research in speech processing. | [
"['Neeraj Gaur' 'Rohan Agrawal' 'Gary Wang' 'Parisa Haghani'\n 'Andrew Rosenberg' 'Bhuvana Ramabhadran']"
] |
null | null | 2406.06671 | null | null | http://arxiv.org/pdf/2406.06671v1 | 2024-06-10T18:00:00Z | 2024-06-10T18:00:00Z | Controlling Counterfactual Harm in Decision Support Systems Based on
Prediction Sets | Decision support systems based on prediction sets help humans solve multiclass classification tasks by narrowing down the set of potential label values to a subset of them, namely a prediction set, and asking them to always predict label values from the prediction sets. While this type of systems have been proven to be effective at improving the average accuracy of the predictions made by humans, by restricting human agency, they may cause harm$unicode{x2014}$a human who has succeeded at predicting the ground-truth label of an instance on their own may have failed had they used these systems. In this paper, our goal is to control how frequently a decision support system based on prediction sets may cause harm, by design. To this end, we start by characterizing the above notion of harm using the theoretical framework of structural causal models. Then, we show that, under a natural, albeit unverifiable, monotonicity assumption, we can estimate how frequently a system may cause harm using only predictions made by humans on their own. Further, we also show that, under a weaker monotonicity assumption, which can be verified experimentally, we can bound how frequently a system may cause harm again using only predictions made by humans on their own. Building upon these assumptions, we introduce a computational framework to design decision support systems based on prediction sets that are guaranteed to cause harm less frequently than a user-specified value using conformal risk control. We validate our framework using real human predictions from two different human subject studies and show that, in decision support systems based on prediction sets, there is a trade-off between accuracy and counterfactual harm. | [
"['Eleni Straitouri' 'Suhas Thejaswi' 'Manuel Gomez Rodriguez']"
] |
null | null | 2406.06699 | null | null | http://arxiv.org/pdf/2406.06699v1 | 2024-06-10T18:01:55Z | 2024-06-10T18:01:55Z | In-Context Learning and Fine-Tuning GPT for Argument Mining | Large Language Models (LLMs) have become ubiquitous in NLP and deep learning. In-Context Learning (ICL) has been suggested as a bridging paradigm between the training-free and fine-tuning LLMs settings. In ICL, an LLM is conditioned to solve tasks by means of a few solved demonstration examples included as prompt. Argument Mining (AM) aims to extract the complex argumentative structure of a text, and Argument Type Classification (ATC) is an essential sub-task of AM. We introduce an ICL strategy for ATC combining kNN-based examples selection and majority vote ensembling. In the training-free ICL setting, we show that GPT-4 is able to leverage relevant information from only a few demonstration examples and achieve very competitive classification accuracy on ATC. We further set up a fine-tuning strategy incorporating well-crafted structural features given directly in textual form. In this setting, GPT-3.5 achieves state-of-the-art performance on ATC. Overall, these results emphasize the emergent ability of LLMs to grasp global discursive flow in raw text in both off-the-shelf and fine-tuned setups. | [
"['Jérémie Cabessa' 'Hugo Hernault' 'Umer Mushtaq']"
] |
null | null | 2406.06700 | null | null | http://arxiv.org/pdf/2406.06700v1 | 2024-06-10T18:02:48Z | 2024-06-10T18:02:48Z | Forget Sharpness: Perturbed Forgetting of Model Biases Within SAM
Dynamics | Despite attaining high empirical generalization, the sharpness of models trained with sharpness-aware minimization (SAM) do not always correlate with generalization error. Instead of viewing SAM as minimizing sharpness to improve generalization, our paper considers a new perspective based on SAM's training dynamics. We propose that perturbations in SAM perform perturbed forgetting, where they discard undesirable model biases to exhibit learning signals that generalize better. We relate our notion of forgetting to the information bottleneck principle, use it to explain observations like the better generalization of smaller perturbation batches, and show that perturbed forgetting can exhibit a stronger correlation with generalization than flatness. While standard SAM targets model biases exposed by the steepest ascent directions, we propose a new perturbation that targets biases exposed through the model's outputs. Our output bias forgetting perturbations outperform standard SAM, GSAM, and ASAM on ImageNet, robustness benchmarks, and transfer to CIFAR-{10,100}, while sometimes converging to sharper regions. Our results suggest that the benefits of SAM can be explained by alternative mechanistic principles that do not require flatness of the loss surface. | [
"['Ankit Vani' 'Frederick Tung' 'Gabriel L. Oliveira'\n 'Hossein Sharifi-Noghabi']"
] |
null | null | 2406.06703 | null | null | http://arxiv.org/pdf/2406.06703v1 | 2024-06-10T18:05:02Z | 2024-06-10T18:05:02Z | Video-based Exercise Classification and Activated Muscle Group
Prediction with Hybrid X3D-SlowFast Network | This paper introduces a simple yet effective strategy for exercise classification and muscle group activation prediction (MGAP). These tasks have significant implications for personal fitness, facilitating more affordable, accessible, safer, and simpler exercise routines. This is particularly relevant for novices and individuals with disabilities. Previous research in the field is mostly dominated by the reliance on mounted sensors and a limited scope of exercises, reducing practicality for everyday use. Furthermore, existing MGAP methodologies suffer from a similar dependency on sensors and a restricted range of muscle groups, often excluding strength training exercises, which are pivotal for a comprehensive fitness regimen. Addressing these limitations, our research employs a video-based deep learning framework that encompasses a broad spectrum of exercises and muscle groups, including those vital for strength training. Utilizing the "Workout/Exercises Video" dataset, our approach integrates the X3D and SlowFast video activity recognition models in an effective way to enhance exercise classification and MGAP performance. Our findings demonstrate that this hybrid method obtained via weighted ensemble outperforms existing baseline models in accuracy. Pretrained models play a crucial role in enhancing overall performance, with optimal channel reduction values for the SlowFast model identified near 10. Through an ablation study that explores fine-tuning, we further elucidate the interrelation between the two tasks. Our composite model, a weighted-average ensemble of X3D and SlowFast, sets a new benchmark in both exercise classification and MGAP across all evaluated categories, offering a robust solution to the limitations of previous approaches. | [
"['Manvik Pasula' 'Pramit Saha']"
] |
null | null | 2406.06714 | null | null | http://arxiv.org/pdf/2406.06714v1 | 2024-06-10T18:23:03Z | 2024-06-10T18:23:03Z | Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach
For Adaptive Brain Stimulation | Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each patient requires a unique stimulation policy to achieve optimal neural responses. Model-free reinforcement learning (MFRL) holds promise in learning effective policies for a variety of similar control tasks, but is limited in domains like brain stimulation by a need for numerous costly environment interactions. In this work we introduce Coprocessor Actor Critic, a novel, model-based reinforcement learning (MBRL) approach for learning neural coprocessor policies for brain stimulation. Our key insight is that coprocessor policy learning is a combination of learning how to act optimally in the world and learning how to induce optimal actions in the world through stimulation of an injured brain. We show that our approach overcomes the limitations of traditional MFRL methods in terms of sample efficiency and task success and outperforms baseline MBRL approaches in a neurologically realistic model of an injured brain. | [
"['Michelle Pan' 'Mariah Schrum' 'Vivek Myers' 'Erdem Bıyık' 'Anca Dragan']"
] |
null | null | 2406.06728 | null | null | http://arxiv.org/pdf/2406.06728v1 | 2024-06-10T18:46:14Z | 2024-06-10T18:46:14Z | AI-Driven Predictive Analytics Approach for Early Prognosis of Chronic
Kidney Disease Using Ensemble Learning and Explainable AI | Chronic Kidney Disease (CKD) is one of the widespread Chronic diseases with no known ultimo cure and high morbidity. Research demonstrates that progressive Chronic Kidney Disease (CKD) is a heterogeneous disorder that significantly impacts kidney structure and functions, eventually leading to kidney failure. With the progression of time, chronic kidney disease has moved from a life-threatening disease affecting few people to a common disorder of varying severity. The goal of this research is to visualize dominating features, feature scores, and values exhibited for early prognosis and detection of CKD using ensemble learning and explainable AI. For that, an AI-driven predictive analytics approach is proposed to aid clinical practitioners in prescribing lifestyle modifications for individual patients to reduce the rate of progression of this disease. Our dataset is collected on body vitals from individuals with CKD and healthy subjects to develop our proposed AI-driven solution accurately. In this regard, blood and urine test results are provided, and ensemble tree-based machine-learning models are applied to predict unseen cases of CKD. Our research findings are validated after lengthy consultations with nephrologists. Our experiments and interpretation results are compared with existing explainable AI applications in various healthcare domains, including CKD. The comparison shows that our developed AI models, particularly the Random Forest model, have identified more features as significant contributors than XgBoost. Interpretability (I), which measures the ratio of important to masked features, indicates that our XgBoost model achieved a higher score, specifically a Fidelity of 98%, in this metric and naturally in the FII index compared to competing models. | [
"['K M Tawsik Jawad' 'Anusha Verma' 'Fathi Amsaad']"
] |
null | null | 2406.06736 | null | null | http://arxiv.org/pdf/2406.06736v1 | 2024-06-10T18:57:06Z | 2024-06-10T18:57:06Z | Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey
of Notions, Methods, and Challenges | The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications. While prior works have investigated static fairness measures, recent studies reveal that automated decision-making has long-term implications and that off-the-shelf fairness approaches may not serve the purpose of achieving long-term fairness. Additionally, the existence of feedback loops and the interaction between models and the environment introduces additional complexities that may deviate from the initial fairness goals. In this survey, we review existing literature on long-term fairness from different perspectives and present a taxonomy for long-term fairness studies. We highlight key challenges and consider future research directions, analyzing both current issues and potential further explorations. | [
"['Usman Gohar' 'Zeyu Tang' 'Jialu Wang' 'Kun Zhang' 'Peter L. Spirtes'\n 'Yang Liu' 'Lu Cheng']"
] |
null | null | 2406.06739 | null | null | http://arxiv.org/pdf/2406.06739v1 | 2024-06-10T19:01:15Z | 2024-06-10T19:01:15Z | Scaling the Vocabulary of Non-autoregressive Models for Efficient
Generative Retrieval | Generative Retrieval introduces a new approach to Information Retrieval by reframing it as a constrained generation task, leveraging recent advancements in Autoregressive (AR) language models. However, AR-based Generative Retrieval methods suffer from high inference latency and cost compared to traditional dense retrieval techniques, limiting their practical applicability. This paper investigates fully Non-autoregressive (NAR) language models as a more efficient alternative for generative retrieval. While standard NAR models alleviate latency and cost concerns, they exhibit a significant drop in retrieval performance (compared to AR models) due to their inability to capture dependencies between target tokens. To address this, we question the conventional choice of limiting the target token space to solely words or sub-words. We propose PIXAR, a novel approach that expands the target vocabulary of NAR models to include multi-word entities and common phrases (up to 5 million tokens), thereby reducing token dependencies. PIXAR employs inference optimization strategies to maintain low inference latency despite the significantly larger vocabulary. Our results demonstrate that PIXAR achieves a relative improvement of 31.0% in MRR@10 on MS MARCO and 23.2% in Hits@5 on Natural Questions compared to standard NAR models with similar latency and cost. Furthermore, online A/B experiments on a large commercial search engine show that PIXAR increases ad clicks by 5.08% and revenue by 4.02%. | [
"['Ravisri Valluri' 'Akash Kumar Mohankumar' 'Kushal Dave' 'Amit Singh'\n 'Jian Jiao' 'Manik Varma' 'Gaurav Sinha']"
] |
null | null | 2406.06744 | null | null | http://arxiv.org/pdf/2406.06744v1 | 2024-06-10T19:05:21Z | 2024-06-10T19:05:21Z | A Multi-module Robust Method for Transient Stability Assessment against
False Label Injection Cyberattacks | The success of deep learning in transient stability assessment (TSA) heavily relies on high-quality training data. However, the label information in TSA datasets is vulnerable to contamination through false label injection (FLI) cyberattacks, resulting in degraded performance of deep TSA models. To address this challenge, a Multi-Module Robust TSA method (MMR) is proposed to rectify the supervised training process misguided by FLI in an unsupervised manner. In MMR, a supervised classification module and an unsupervised clustering module are alternatively trained to improve the clustering friendliness of representation leaning, thereby achieving accurate clustering assignments. Leveraging the clustering assignments, we construct a training label corrector to rectify the injected false labels and progressively enhance robustness and resilience against FLI. However, there is still a gap on accuracy and convergence speed between MMR and FLI-free deep TSA models. To narrow this gap, we further propose a human-in-the-loop training strategy, named MMR-HIL. In MMR-HIL, potential false samples can be detected by modeling the training loss with a Gaussian distribution. From these samples, the most likely false samples and most ambiguous samples are re-labeled by a TSA experts guided bi-directional annotator and then subjected to penalized optimization, aimed at improving accuracy and convergence speed. Extensive experiments indicate that MMR and MMR-HIL both exhibit powerful robustness against FLI in TSA performance. Moreover, the contaminated labels can also be effectively corrected, demonstrating superior resilience of the proposed methods. | [
"['Hanxuan Wang' 'Na Lu' 'Yinhong Liu' 'Zhuqing Wang' 'Zixuan Wang']"
] |
null | null | 2406.06746 | null | null | http://arxiv.org/pdf/2406.06746v1 | 2024-06-10T19:17:09Z | 2024-06-10T19:17:09Z | Multi-Objective Neural Architecture Search for In-Memory Computing | In this work, we employ neural architecture search (NAS) to enhance the efficiency of deploying diverse machine learning (ML) tasks on in-memory computing (IMC) architectures. Initially, we design three fundamental components inspired by the convolutional layers found in VGG and ResNet models. Subsequently, we utilize Bayesian optimization to construct a convolutional neural network (CNN) model with adaptable depths, employing these components. Through the Bayesian search algorithm, we explore a vast search space comprising over 640 million network configurations to identify the optimal solution, considering various multi-objective cost functions like accuracy/latency and accuracy/energy. Our evaluation of this NAS approach for IMC architecture deployment spans three distinct image classification datasets, demonstrating the effectiveness of our method in achieving a balanced solution characterized by high accuracy and reduced latency and energy consumption. | [
"['Md Hasibul Amin' 'Mohammadreza Mohammadi' 'Ramtin Zand']"
] |
null | null | 2406.06749 | null | null | http://arxiv.org/pdf/2406.06749v1 | 2024-06-10T19:25:19Z | 2024-06-10T19:25:19Z | Federated Nonparametric Hypothesis Testing with Differential Privacy
Constraints: Optimal Rates and Adaptive Tests | Federated learning has attracted significant recent attention due to its applicability across a wide range of settings where data is collected and analyzed across disparate locations. In this paper, we study federated nonparametric goodness-of-fit testing in the white-noise-with-drift model under distributed differential privacy (DP) constraints. We first establish matching lower and upper bounds, up to a logarithmic factor, on the minimax separation rate. This optimal rate serves as a benchmark for the difficulty of the testing problem, factoring in model characteristics such as the number of observations, noise level, and regularity of the signal class, along with the strictness of the $(epsilon,delta)$-DP requirement. The results demonstrate interesting and novel phase transition phenomena. Furthermore, the results reveal an interesting phenomenon that distributed one-shot protocols with access to shared randomness outperform those without access to shared randomness. We also construct a data-driven testing procedure that possesses the ability to adapt to an unknown regularity parameter over a large collection of function classes with minimal additional cost, all while maintaining adherence to the same set of DP constraints. | [
"['T. Tony Cai' 'Abhinav Chakraborty' 'Lasse Vuursteen']"
] |
null | null | 2406.06751 | null | null | http://arxiv.org/pdf/2406.06751v1 | 2024-06-10T19:29:10Z | 2024-06-10T19:29:10Z | Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking
Policy Gradients | This paper proposes a novel deep symbolic regression approach to enhance the robustness and interpretability of data-driven mathematical expression discovery. Despite the success of the state-of-the-art method, DSR, it is built on recurrent neural networks, purely guided by data fitness, and potentially meet tail barriers, which can zero out the policy gradient and cause inefficient model updates. To overcome these limitations, we use transformers in conjunction with breadth-first-search to improve the learning performance. We use Bayesian information criterion (BIC) as the reward function to explicitly account for the expression complexity and optimize the trade-off between interpretability and data fitness. We propose a modified risk-seeking policy that not only ensures the unbiasness of the gradient, but also removes the tail barriers, thus ensuring effective updates from top performers. Through a series of benchmarks and systematic experiments, we demonstrate the advantages of our approach. | [
"['Zachary Bastiani' 'Robert M. Kirby' 'Jacob Hochhalter' 'Shandian Zhe']"
] |
null | null | 2406.06755 | null | null | http://arxiv.org/pdf/2406.06755v1 | 2024-06-10T19:34:07Z | 2024-06-10T19:34:07Z | Optimal Federated Learning for Nonparametric Regression with
Heterogeneous Distributed Differential Privacy Constraints | This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy constraints. The setting we consider is heterogeneous, encompassing both varying sample sizes and differential privacy constraints across servers. Within this framework, both global and pointwise estimation are considered, and optimal rates of convergence over the Besov spaces are established. Distributed privacy-preserving estimators are proposed and their risk properties are investigated. Matching minimax lower bounds, up to a logarithmic factor, are established for both global and pointwise estimation. Together, these findings shed light on the tradeoff between statistical accuracy and privacy preservation. In particular, we characterize the compromise not only in terms of the privacy budget but also concerning the loss incurred by distributing data within the privacy framework as a whole. This insight captures the folklore wisdom that it is easier to retain privacy in larger samples, and explores the differences between pointwise and global estimation under distributed privacy constraints. | [
"['T. Tony Cai' 'Abhinav Chakraborty' 'Lasse Vuursteen']"
] |
null | null | 2406.06768 | null | null | http://arxiv.org/pdf/2406.06768v1 | 2024-06-10T20:06:53Z | 2024-06-10T20:06:53Z | Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical
Bayes Designs | We study the design and analysis of switchback experiments conducted on a single aggregate unit. The design problem is to partition the continuous time space into intervals and switch treatments between intervals, in order to minimize the estimation error of the treatment effect. We show that the estimation error depends on four factors: carryover effects, periodicity, serially correlated outcomes, and impacts from simultaneous experiments. We derive a rigorous bias-variance decomposition and show the tradeoffs of the estimation error from these factors. The decomposition provides three new insights in choosing a design: First, balancing the periodicity between treated and control intervals reduces the variance; second, switching less frequently reduces the bias from carryover effects while increasing the variance from correlated outcomes, and vice versa; third, randomizing interval start and end points reduces both bias and variance from simultaneous experiments. Combining these insights, we propose a new empirical Bayes design approach. This approach uses prior data and experiments for designing future experiments. We illustrate this approach using real data from a ride-sharing platform, yielding a design that reduces MSE by 33% compared to the status quo design used on the platform. | [
"['Ruoxuan Xiong' 'Alex Chin' 'Sean J. Taylor']"
] |
null | null | 2406.06776 | null | null | http://arxiv.org/pdf/2406.06776v1 | 2024-06-10T20:24:14Z | 2024-06-10T20:24:14Z | SeeFar: Satellite Agnostic Multi-Resolution Dataset for Geospatial
Foundation Models | SeeFar is an evolving collection of multi-resolution satellite images from public and commercial satellites. We specifically curated this dataset for training geospatial foundation models, unconstrained by satellite type. In recent years, advances in technology have made satellite imagery more accessible than ever. More earth-observing satellites have been launched in the last five years than in the previous fifty. Modern commercial satellites now offer up to 100 times the spatial resolution of public access satellites. However, the high cost and limited historical availability of commercial satellite imagery is a barrier to the training of foundational models, impacting what images can be used during inference. The SeeFar dataset represents a step towards training models that are satellite-agnostic by combining multi-resolution commercial and public access pre-processed images. This will enable users to utilize historical data alongside higher-resolution, more expensive satellite imagery, offering greater flexibility during inference. To achieve this, we describe a process for standardizing data from diverse satellite sources, normalizing different data formats, and aligning spectral bands to enhance interoperability. The SeeFar dataset includes images at a resolution of 384x384 pixels, spanning four spectral bands (Blue, Green, Red, and Near-Infrared) and expanding spatial resolutions (starting with 30, 10, 1.5, and 1.0 meters), all in cloud-optimized GeoTIFF format. It also provides consistent and comprehensive metadata to enhance data transparency and reliability. By aggregating data from multiple sources, SeeFar makes processed and consistent satellite data accessible to a wider range of users - from researchers to policymakers - fostering competition and innovation in satellite imagery analysis. The dataset is available at url{coastalcarbon.ai/seefar}. | [
"['James Lowman' 'Kelly Liu Zheng' 'Roydon Fraser'\n 'Jesse Van Griensven The' 'Mojtaba Valipour']"
] |
null | null | 2406.06792 | null | null | http://arxiv.org/pdf/2406.06792v2 | 2024-06-14T03:59:05Z | 2024-06-10T20:59:52Z | Reinforced Compressive Neural Architecture Search for Versatile
Adversarial Robustness | Prior neural architecture search (NAS) for adversarial robustness works have discovered that a lightweight and adversarially robust neural network architecture could exist in a non-robust large teacher network, generally disclosed by heuristic rules through statistical analysis and neural architecture search, generally disclosed by heuristic rules from neural architecture search. However, heuristic methods cannot uniformly handle different adversarial attacks and "teacher" network capacity. To solve this challenge, we propose a Reinforced Compressive Neural Architecture Search (RC-NAS) for Versatile Adversarial Robustness. Specifically, we define task settings that compose datasets, adversarial attacks, and teacher network information. Given diverse tasks, we conduct a novel dual-level training paradigm that consists of a meta-training and a fine-tuning phase to effectively expose the RL agent to diverse attack scenarios (in meta-training), and making it adapt quickly to locate a sub-network (in fine-tuning) for any previously unseen scenarios. Experiments show that our framework could achieve adaptive compression towards different initial teacher networks, datasets, and adversarial attacks, resulting in more lightweight and adversarially robust architectures. | [
"['Dingrong Wang' 'Hitesh Sapkota' 'Zhiqiang Tao' 'Qi Yu']"
] |
null | null | 2406.06793 | null | null | http://arxiv.org/pdf/2406.06793v1 | 2024-06-10T20:59:53Z | 2024-06-10T20:59:53Z | PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer | Despite the recent advancements in offline RL, no unified algorithm could achieve superior performance across a broad range of tasks. Offline textit{value function learning}, in particular, struggles with sparse-reward, long-horizon tasks due to the difficulty of solving credit assignment and extrapolation errors that accumulates as the horizon of the task grows.~On the other hand, models that can perform well in long-horizon tasks are designed specifically for goal-conditioned tasks, which commonly perform worse than value function learning methods on short-horizon, dense-reward scenarios. To bridge this gap, we propose a hierarchical planner designed for offline RL called PlanDQ. PlanDQ incorporates a diffusion-based planner at the high level, named D-Conductor, which guides the low-level policy through sub-goals. At the low level, we used a Q-learning based approach called the Q-Performer to accomplish these sub-goals. Our experimental results suggest that PlanDQ can achieve superior or competitive performance on D4RL continuous control benchmark tasks as well as AntMaze, Kitchen, and Calvin as long-horizon tasks. | [
"['Chang Chen' 'Junyeob Baek' 'Fei Deng' 'Kenji Kawaguchi'\n 'Caglar Gulcehre' 'Sungjin Ahn']"
] |
null | null | 2406.06796 | null | null | http://arxiv.org/pdf/2406.06796v1 | 2024-06-10T21:02:53Z | 2024-06-10T21:02:53Z | FlexLoc: Conditional Neural Networks for Zero-Shot Sensor Perspective
Invariance in Object Localization with Distributed Multimodal Sensors | Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sensors viewing the scene from different perspectives to estimate the target location while also employing multiple modalities for enhanced robustness and accuracy. Recently, such systems have employed end-to-end deep neural models trained on large datasets due to their superior performance and ability to handle data from diverse sensor modalities. However, such neural models are often trained on data collected from a particular set of sensor poses (i.e., locations and orientations). During real-world deployments, slight deviations from these sensor poses can result in extreme inaccuracies. To address this challenge, we introduce FlexLoc, which employs conditional neural networks to inject node perspective information to adapt the localization pipeline. Specifically, a small subset of model weights are derived from node poses at run time, enabling accurate generalization to unseen perspectives with minimal additional overhead. Our evaluations on a multimodal, multiview indoor tracking dataset showcase that FlexLoc improves the localization accuracy by almost 50% in the zero-shot case (no calibration data available) compared to the baselines. The source code of FlexLoc is available at https://github.com/nesl/FlexLoc. | [
"['Jason Wu' 'Ziqi Wang' 'Xiaomin Ouyang' 'Ho Lyun Jeong'\n 'Colin Samplawski' 'Lance Kaplan' 'Benjamin Marlin' 'Mani Srivastava']"
] |
null | null | 2406.06802 | null | null | http://arxiv.org/pdf/2406.06802v1 | 2024-06-10T21:15:28Z | 2024-06-10T21:15:28Z | Satisficing Exploration in Bandit Optimization | Motivated by the concept of satisficing in decision-making, we consider the problem of satisficing exploration in bandit optimization. In this setting, the learner aims at selecting satisficing arms (arms with mean reward exceeding a certain threshold value) as frequently as possible. The performance is measured by satisficing regret, which is the cumulative deficit of the chosen arm's mean reward compared to the threshold. We propose SELECT, a general algorithmic template for Satisficing Exploration via LowEr Confidence bound Testing, that attains constant satisficing regret for a wide variety of bandit optimization problems in the realizable case (i.e., a satisficing arm exists). Specifically, given a class of bandit optimization problems and a corresponding learning oracle with sub-linear (standard) regret upper bound, SELECT iteratively makes use of the oracle to identify a potential satisficing arm with low regret. Then, it collects data samples from this arm, and continuously compares the LCB of the identified arm's mean reward against the threshold value to determine if it is a satisficing arm. As a complement, SELECT also enjoys the same (standard) regret guarantee as the oracle in the non-realizable case. Finally, we conduct numerical experiments to validate the performance of SELECT for several popular bandit optimization settings. | [
"['Qing Feng' 'Tianyi Ma' 'Ruihao Zhu']"
] |
null | null | 2406.06808 | null | null | http://arxiv.org/pdf/2406.06808v1 | 2024-06-10T21:23:19Z | 2024-06-10T21:23:19Z | Fast White-Box Adversarial Streaming Without a Random Oracle | Recently, the question of adversarially robust streaming, where the stream is allowed to depend on the randomness of the streaming algorithm, has gained a lot of attention. In this work, we consider a strong white-box adversarial model (Ajtai et al. PODS 2022), in which the adversary has access to all past random coins and the parameters used by the streaming algorithm. We focus on the sparse recovery problem and extend our result to other tasks such as distinct element estimation and low-rank approximation of matrices and tensors. The main drawback of previous work is that it requires a random oracle, which is especially problematic in the streaming model since the amount of randomness is counted in the space complexity of a streaming algorithm. Also, the previous work suffers from large update time. We construct a near-optimal solution for the sparse recovery problem in white-box adversarial streams, based on the subexponentially secure Learning with Errors assumption. Importantly, our solution does not require a random oracle and has a polylogarithmic per item processing time. We also give results in a related white-box adversarially robust distributed model. Our constructions are based on homomorphic encryption schemes satisfying very mild structural properties that are currently satisfied by most known schemes. | [
"['Ying Feng' 'Aayush Jain' 'David P. Woodruff']"
] |
null | null | 2406.06811 | null | null | http://arxiv.org/pdf/2406.06811v1 | 2024-06-10T21:34:43Z | 2024-06-10T21:34:43Z | Learning Continually by Spectral Regularization | Loss of plasticity is a phenomenon where neural networks become more difficult to train during the course of learning. Continual learning algorithms seek to mitigate this effect by sustaining good predictive performance while maintaining network trainability. We develop new techniques for improving continual learning by first reconsidering how initialization can ensure trainability during early phases of learning. From this perspective, we derive new regularization strategies for continual learning that ensure beneficial initialization properties are better maintained throughout training. In particular, we investigate two new regularization techniques for continual learning: (i) Wasserstein regularization toward the initial weight distribution, which is less restrictive than regularizing toward initial weights; and (ii) regularizing weight matrix singular values, which directly ensures gradient diversity is maintained throughout training. We present an experimental analysis that shows these alternative regularizers can improve continual learning performance across a range of supervised learning tasks and model architectures. The alternative regularizers prove to be less sensitive to hyperparameters while demonstrating better training in individual tasks, sustaining trainability as new tasks arrive, and achieving better generalization performance. | [
"['Alex Lewandowski' 'Saurabh Kumar' 'Dale Schuurmans' 'András György'\n 'Marlos C. Machado']"
] |
null | null | 2406.06812 | null | null | http://arxiv.org/pdf/2406.06812v1 | 2024-06-10T21:37:36Z | 2024-06-10T21:37:36Z | On Learning what to Learn: heterogeneous observations of dynamics and
establishing (possibly causal) relations among them | Before we attempt to learn a function between two (sets of) observables of a physical process, we must first decide what the inputs and what the outputs of the desired function are going to be. Here we demonstrate two distinct, data-driven ways of initially deciding ``the right quantities'' to relate through such a function, and then proceed to learn it. This is accomplished by processing multiple simultaneous heterogeneous data streams (ensembles of time series) from observations of a physical system: multiple observation processes of the system. We thus determine (a) what subsets of observables are common between the observation processes (and therefore observable from each other, relatable through a function); and (b) what information is unrelated to these common observables, and therefore particular to each observation process, and not contributing to the desired function. Any data-driven function approximation technique can subsequently be used to learn the input-output relation, from k-nearest neighbors and Geometric Harmonics to Gaussian Processes and Neural Networks. Two particular ``twists'' of the approach are discussed. The first has to do with the identifiability of particular quantities of interest from the measurements. We now construct mappings from a single set of observations of one process to entire level sets of measurements of the process, consistent with this single set. The second attempts to relate our framework to a form of causality: if one of the observation processes measures ``now'', while the second observation process measures ``in the future'', the function to be learned among what is common across observation processes constitutes a dynamical model for the system evolution. | [
"['David W. Sroczynski' 'Felix Dietrich' 'Eleni D. Koronaki' 'Ronen Talmon'\n 'Ronald R. Coifman' 'Erik Bollt' 'Ioannis G. Kevrekidis']"
] |
null | null | 2406.06818 | null | null | http://arxiv.org/pdf/2406.06818v1 | 2024-06-10T22:01:34Z | 2024-06-10T22:01:34Z | Conformal Prediction for Class-wise Coverage via Augmented Label Rank
Calibration | Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or conditional probability. Although the valid coverage guarantee has been extensively studied for classification problems, CP often produces large prediction sets which may not be practically useful. This issue is exacerbated for the setting of class-conditional coverage on imbalanced classification tasks. This paper proposes the Rank Calibrated Class-conditional CP (RC3P) algorithm to reduce the prediction set sizes to achieve class-conditional coverage, where the valid coverage holds for each class. In contrast to the standard class-conditional CP (CCP) method that uniformly thresholds the class-wise conformity score for each class, the augmented label rank calibration step allows RC3P to selectively iterate this class-wise thresholding subroutine only for a subset of classes whose class-wise top-k error is small. We prove that agnostic to the classifier and data distribution, RC3P achieves class-wise coverage. We also show that RC3P reduces the size of prediction sets compared to the CCP method. Comprehensive experiments on multiple real-world datasets demonstrate that RC3P achieves class-wise coverage and 26.25% reduction in prediction set sizes on average. | [
"['Yuanjie Shi' 'Subhankar Ghosh' 'Taha Belkhouja' 'Janardhan Rao Doppa'\n 'Yan Yan']"
] |
null | null | 2406.06820 | null | null | http://arxiv.org/pdf/2406.06820v1 | 2024-06-10T22:07:57Z | 2024-06-10T22:07:57Z | Adapters Strike Back | Adapters provide an efficient and lightweight mechanism for adapting trained transformer models to a variety of different tasks. However, they have often been found to be outperformed by other adaptation mechanisms, including low-rank adaptation. In this paper, we provide an in-depth study of adapters, their internal structure, as well as various implementation choices. We uncover pitfalls for using adapters and suggest a concrete, improved adapter architecture, called Adapter+, that not only outperforms previous adapter implementations but surpasses a number of other, more complex adaptation mechanisms in several challenging settings. Despite this, our suggested adapter is highly robust and, unlike previous work, requires little to no manual intervention when addressing a novel scenario. Adapter+ reaches state-of-the-art average accuracy on the VTAB benchmark, even without a per-task hyperparameter optimization. | [
"['Jan-Martin O. Steitz' 'Stefan Roth']"
] |
null | null | 2406.06823 | null | null | http://arxiv.org/pdf/2406.06823v1 | 2024-06-10T22:11:00Z | 2024-06-10T22:11:00Z | Locally Interdependent Multi-Agent MDP: Theoretical Framework for
Decentralized Agents with Dynamic Dependencies | Many multi-agent systems in practice are decentralized and have dynamically varying dependencies. There has been a lack of attempts in the literature to analyze these systems theoretically. In this paper, we propose and theoretically analyze a decentralized model with dynamically varying dependencies called the Locally Interdependent Multi-Agent MDP. This model can represent problems in many disparate domains such as cooperative navigation, obstacle avoidance, and formation control. Despite the intractability that general partially observable multi-agent systems suffer from, we propose three closed-form policies that are theoretically near-optimal in this setting and can be scalable to compute and store. Consequentially, we reveal a fundamental property of Locally Interdependent Multi-Agent MDP's that the partially observable decentralized solution is exponentially close to the fully observable solution with respect to the visibility radius. We then discuss extensions of our closed-form policies to further improve tractability. We conclude by providing simulations to investigate some long horizon behaviors of our closed-form policies. | [
"['Alex DeWeese' 'Guannan Qu']"
] |
null | null | 2406.06825 | null | null | http://arxiv.org/pdf/2406.06825v1 | 2024-06-10T22:15:55Z | 2024-06-10T22:15:55Z | A local squared Wasserstein-2 method for efficient reconstruction of
models with uncertainty | In this paper, we propose a local squared Wasserstein-2 (W_2) method to solve the inverse problem of reconstructing models with uncertain latent variables or parameters. A key advantage of our approach is that it does not require prior information on the distribution of the latent variables or parameters in the underlying models. Instead, our method can efficiently reconstruct the distributions of the output associated with different inputs based on empirical distributions of observation data. We demonstrate the effectiveness of our proposed method across several uncertainty quantification (UQ) tasks, including linear regression with coefficient uncertainty, training neural networks with weight uncertainty, and reconstructing ordinary differential equations (ODEs) with a latent random variable. | [
"['Mingtao Xia' 'Qijing Shen']"
] |
null | null | 2406.06829 | null | null | http://arxiv.org/pdf/2406.06829v1 | 2024-06-10T22:33:24Z | 2024-06-10T22:33:24Z | Personalized Binomial DAGs Learning with Network Structured Covariates | The causal dependence in data is often characterized by Directed Acyclic Graphical (DAG) models, widely used in many areas. Causal discovery aims to recover the DAG structure using observational data. This paper focuses on causal discovery with multi-variate count data. We are motivated by real-world web visit data, recording individual user visits to multiple websites. Building a causal diagram can help understand user behavior in transitioning between websites, inspiring operational strategy. A challenge in modeling is user heterogeneity, as users with different backgrounds exhibit varied behaviors. Additionally, social network connections can result in similar behaviors among friends. We introduce personalized Binomial DAG models to address heterogeneity and network dependency between observations, which are common in real-world applications. To learn the proposed DAG model, we develop an algorithm that embeds the network structure into a dimension-reduced covariate, learns each node's neighborhood to reduce the DAG search space, and explores the variance-mean relation to determine the ordering. Simulations show our algorithm outperforms state-of-the-art competitors in heterogeneous data. We demonstrate its practical usefulness on a real-world web visit dataset. | [
"['Boxin Zhao' 'Weishi Wang' 'Dingyuan Zhu' 'Ziqi Liu' 'Dong Wang'\n 'Zhiqiang Zhang' 'Jun Zhou' 'Mladen Kolar']"
] |
null | null | 2406.06838 | null | null | http://arxiv.org/pdf/2406.06838v1 | 2024-06-10T22:57:27Z | 2024-06-10T22:57:27Z | Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization
by Large Step Sizes | We study the generalization of two-layer ReLU neural networks in a univariate nonparametric regression problem with noisy labels. This is a problem where kernels (emph{e.g.} NTK) are provably sub-optimal and benign overfitting does not happen, thus disqualifying existing theory for interpolating (0-loss, global optimal) solutions. We present a new theory of generalization for local minima that gradient descent with a constant learning rate can emph{stably} converge to. We show that gradient descent with a fixed learning rate $eta$ can only find local minima that represent smooth functions with a certain weighted emph{first order total variation} bounded by $1/eta - 1/2 + widetilde{O}(sigma + sqrt{mathrm{MSE}})$ where $sigma$ is the label noise level, $mathrm{MSE}$ is short for mean squared error against the ground truth, and $widetilde{O}(cdot)$ hides a logarithmic factor. Under mild assumptions, we also prove a nearly-optimal MSE bound of $widetilde{O}(n^{-4/5})$ within the strict interior of the support of the $n$ data points. Our theoretical results are validated by extensive simulation that demonstrates large learning rate training induces sparse linear spline fits. To the best of our knowledge, we are the first to obtain generalization bound via minima stability in the non-interpolation case and the first to show ReLU NNs without regularization can achieve near-optimal rates in nonparametric regression. | [
"['Dan Qiao' 'Kaiqi Zhang' 'Esha Singh' 'Daniel Soudry' 'Yu-Xiang Wang']"
] |
null | null | 2406.06840 | null | null | http://arxiv.org/pdf/2406.06840v2 | 2024-06-18T19:11:11Z | 2024-06-10T23:09:19Z | Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded
Dog Whistles | A dog whistle is a form of coded communication that carries a secondary meaning to specific audiences and is often weaponized for racial and socioeconomic discrimination. Dog whistling historically originated from United States politics, but in recent years has taken root in social media as a means of evading hate speech detection systems and maintaining plausible deniability. In this paper, we present an approach for word-sense disambiguation of dog whistles from standard speech using Large Language Models (LLMs), and leverage this technique to create a dataset of 16,550 high-confidence coded examples of dog whistles used in formal and informal communication. Silent Signals is the largest dataset of disambiguated dog whistle usage, created for applications in hate speech detection, neology, and political science. The dataset can be found at https://huggingface.co/datasets/SALT-NLP/silent_signals. | [
"['Julia Kruk' 'Michela Marchini' 'Rijul Magu' 'Caleb Ziems'\n 'David Muchlinski' 'Diyi Yang']"
] |
null | null | 2406.06841 | null | null | http://arxiv.org/pdf/2406.06841v1 | 2024-06-10T23:23:36Z | 2024-06-10T23:23:36Z | Compass: A Comprehensive Tool for Accurate and Efficient Molecular
Docking in Inference and Fine-Tuning | While there has been discussion about noise levels in molecular docking datasets such as PDBBind, a thorough analysis of their physical/chemical and bioactivity noise characteristics is still lacking. PoseCheck addresses this issue by examining molecular strain energy, molecular-protein clashes, and interactions, but it is primarily created for $de$ $novo$ drug design. Another important metric in molecular docking, Binding Affinity Energy, is better assessed by the new empirical score function, AA-Score, which has demonstrated improved performance over existing methods. To tackle these challenges, we propose the COMPASS method, which integrates the PoseCheck and AA-Score modules. This approach evaluates dataset noise levels and the physical/chemical and bioactivity feasibility of docked molecules. Our analysis of the PDBBind dataset using COMPASS reveals significant noise in the ground truth data. Additionally, we incorporate COMPASS with the state-of-the-art molecular docking method, DiffDock, in inference mode to achieve efficient and accurate assessments of docked ligands. Finally, we propose a new paradigm to enhance model performance for molecular docking through fine-tuning and discuss the potential benefits of this approach. The source code is available publicly at https://github.com/BIMSBbioinfo/Compass. | [
"['Ahmet Sarigun' 'Vedran Franke' 'Altuna Akalin']"
] |
null | null | 2406.06849 | null | null | http://arxiv.org/pdf/2406.06849v2 | 2024-06-17T13:18:18Z | 2024-06-10T23:40:16Z | Flexible Parametric Inference for Space-Time Hawkes Processes | Many modern spatio-temporal data sets, in sociology, epidemiology or seismology, for example, exhibit self-exciting characteristics, triggering and clustering behaviors both at the same time, that a suitable Hawkes space-time process can accurately capture. This paper aims to develop a fast and flexible parametric inference technique to recover the parameters of the kernel functions involved in the intensity function of a space-time Hawkes process based on such data. Our statistical approach combines three key ingredients: 1) kernels with finite support are considered, 2) the space-time domain is appropriately discretized, and 3) (approximate) precomputations are used. The inference technique we propose then consists of a $ell_2$ gradient-based solver that is fast and statistically accurate. In addition to describing the algorithmic aspects, numerical experiments have been carried out on synthetic and real spatio-temporal data, providing solid empirical evidence of the relevance of the proposed methodology. | [
"['Emilia Siviero' 'Guillaume Staerman' 'Stephan Clémençon' 'Thomas Moreau']"
] |
null | null | 2406.06855 | null | null | http://arxiv.org/pdf/2406.06855v1 | 2024-06-11T00:01:42Z | 2024-06-11T00:01:42Z | Design and Scheduling of an AI-based Queueing System | To leverage prediction models to make optimal scheduling decisions in service systems, we must understand how predictive errors impact congestion due to externalities on the delay of other jobs. Motivated by applications where prediction models interact with human servers (e.g., content moderation), we consider a large queueing system comprising of many single server queues where the class of a job is estimated using a prediction model. By characterizing the impact of mispredictions on congestion cost in heavy traffic, we design an index-based policy that incorporates the predicted class information in a near-optimal manner. Our theoretical results guide the design of predictive models by providing a simple model selection procedure with downstream queueing performance as a central concern, and offer novel insights on how to design queueing systems with AI-based triage. We illustrate our framework on a content moderation task based on real online comments, where we construct toxicity classifiers by finetuning large language models. | [
"['Jiung Lee' 'Hongseok Namkoong' 'Yibo Zeng']"
] |
null | null | 2406.06856 | null | null | http://arxiv.org/pdf/2406.06856v1 | 2024-06-11T00:02:19Z | 2024-06-11T00:02:19Z | Sample Complexity Reduction via Policy Difference Estimation in Tabular
Reinforcement Learning | In this paper, we study the non-asymptotic sample complexity for the pure exploration problem in contextual bandits and tabular reinforcement learning (RL): identifying an epsilon-optimal policy from a set of policies with high probability. Existing work in bandits has shown that it is possible to identify the best policy by estimating only the difference between the behaviors of individual policies, which can be substantially cheaper than estimating the behavior of each policy directly. However, the best-known complexities in RL fail to take advantage of this and instead estimate the behavior of each policy directly. Does it suffice to estimate only the differences in the behaviors of policies in RL? We answer this question positively for contextual bandits but in the negative for tabular RL, showing a separation between contextual bandits and RL. However, inspired by this, we show that it almost suffices to estimate only the differences in RL: if we can estimate the behavior of a single reference policy, it suffices to only estimate how any other policy deviates from this reference policy. We develop an algorithm which instantiates this principle and obtains, to the best of our knowledge, the tightest known bound on the sample complexity of tabular RL. | [
"['Adhyyan Narang' 'Andrew Wagenmaker' 'Lillian Ratliff' 'Kevin Jamieson']"
] |
null | null | 2406.06858 | null | null | http://arxiv.org/pdf/2406.06858v4 | 2024-06-18T20:25:56Z | 2024-06-11T00:17:39Z | FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel
Fusion | Large deep learning models have demonstrated strong ability to solve many tasks across a wide range of applications. Those large models typically require training and inference to be distributed. Tensor parallelism is a common technique partitioning computation of an operation or layer across devices to overcome the memory capacity limitation of a single processor, and/or to accelerate computation to meet a certain latency requirement. However, this kind of parallelism introduces additional communication that might contribute a significant portion of overall runtime. Thus limits scalability of this technique within a group of devices with high speed interconnects, such as GPUs with NVLinks in a node. This paper proposes a novel method, Flux, to significantly hide communication latencies with dependent computations for GPUs. Flux over-decomposes communication and computation operations into much finer-grained operations and further fuses them into a larger kernel to effectively hide communication without compromising kernel efficiency. Flux can potentially overlap up to 96% of communication given a fused kernel. Overall, it can achieve up to 1.24x speedups for training over Megatron-LM on a cluster of 128 GPUs with various GPU generations and interconnects, and up to 1.66x and 1.30x speedups for prefill and decoding inference over vLLM on a cluster with 8 GPUs with various GPU generations and interconnects. | [
"['Li-Wen Chang' 'Wenlei Bao' 'Qi Hou' 'Chengquan Jiang' 'Ningxin Zheng'\n 'Yinmin Zhong' 'Xuanrun Zhang' 'Zuquan Song' 'Ziheng Jiang' 'Haibin Lin'\n 'Xin Jin' 'Xin Liu']"
] |
null | null | 2406.06887 | null | null | http://arxiv.org/pdf/2406.06887v1 | 2024-06-11T02:07:18Z | 2024-06-11T02:07:18Z | PLUM: Preference Learning Plus Test Cases Yields Better Code Language
Models | Instruction-finetuned code language models (LMs) have shown promise in various programming tasks. They are trained, using a language modeling objective, on natural language instructions and gold code snippet pairs. Recent evidence suggests that these models, never exposed to incorrect solutions during training, often struggle to distinguish between correct and incorrect solutions. This observation raises our inquiry: Can preference learning, which trains models to prefer correct solutions over incorrect ones, help push the boundaries of code LMs even further? We propose PLUM, a novel textbf{p}reference textbf{l}earning framework atextbf{u}gmented with test cases tailored for code Ltextbf{M}s.PLUM aims to investigate the key success factors and potential benefits of preference learning in code LMs, which remain elusive despite its success in aligning LMs with human values. PLUM consists of three stages: (1) Generating test cases for natural language instructions, (2) sampling candidate solutions from the policy and evaluating them against the test cases to create a preference dataset, which is then used to (3) train the policy with a preference learning algorithm. Experiments demonstrate that PLUM substantially improves the performance of existing code LMs on established code generation benchmarks such as HumanEval (+) and MBPP (+), even for the state-of-the-art open-source language model CodeQwen-1.5-7B-Chat. PLUM complements the supervised fine-tuning (SFT) stage, demonstrating synergistic effects. | [
"['Dylan Zhang' 'Shizhe Diao' 'Xueyan Zou' 'Hao Peng']"
] |
null | null | 2406.06891 | null | null | http://arxiv.org/pdf/2406.06891v1 | 2024-06-11T02:13:46Z | 2024-06-11T02:13:46Z | Tokenize features, enhancing tables: the FT-TABPFN model for tabular
classification | Traditional methods for tabular classification usually rely on supervised learning from scratch, which requires extensive training data to determine model parameters. However, a novel approach called Prior-Data Fitted Networks (TabPFN) has changed this paradigm. TabPFN uses a 12-layer transformer trained on large synthetic datasets to learn universal tabular representations. This method enables fast and accurate predictions on new tasks with a single forward pass and no need for additional training. Although TabPFN has been successful on small datasets, it generally shows weaker performance when dealing with categorical features. To overcome this limitation, we propose FT-TabPFN, which is an enhanced version of TabPFN that includes a novel Feature Tokenization layer to better handle classification features. By fine-tuning it for downstream tasks, FT-TabPFN not only expands the functionality of the original model but also significantly improves its applicability and accuracy in tabular classification. Our full source code is available for community use and development. | [
"['Quangao Liu' 'Wei Yang' 'Chen Liang' 'Longlong Pang' 'Zhuozhang Zou']"
] |
null | null | 2406.06893 | null | null | http://arxiv.org/pdf/2406.06893v1 | 2024-06-11T02:15:53Z | 2024-06-11T02:15:53Z | Transformers Provably Learn Sparse Token Selection While Fully-Connected
Nets Cannot | The transformer architecture has prevailed in various deep learning settings due to its exceptional capabilities to select and compose structural information. Motivated by these capabilities, Sanford et al. proposed the sparse token selection task, in which transformers excel while fully-connected networks (FCNs) fail in the worst case. Building upon that, we strengthen the FCN lower bound to an average-case setting and establish an algorithmic separation of transformers over FCNs. Specifically, a one-layer transformer trained with gradient descent provably learns the sparse token selection task and, surprisingly, exhibits strong out-of-distribution length generalization. We provide empirical simulations to justify our theoretical findings. | [
"['Zixuan Wang' 'Stanley Wei' 'Daniel Hsu' 'Jason D. Lee']"
] |
null | null | 2406.06894 | null | null | http://arxiv.org/pdf/2406.06894v1 | 2024-06-11T02:19:31Z | 2024-06-11T02:19:31Z | Nonlinear time-series embedding by monotone variational inequality | In the wild, we often encounter collections of sequential data such as electrocardiograms, motion capture, genomes, and natural language, and sequences may be multichannel or symbolic with nonlinear dynamics. We introduce a new method to learn low-dimensional representations of nonlinear time series without supervision and can have provable recovery guarantees. The learned representation can be used for downstream machine-learning tasks such as clustering and classification. The method is based on the assumption that the observed sequences arise from a common domain, but each sequence obeys its own autoregressive models that are related to each other through low-rank regularization. We cast the problem as a computationally efficient convex matrix parameter recovery problem using monotone Variational Inequality and encode the common domain assumption via low-rank constraint across the learned representations, which can learn the geometry for the entire domain as well as faithful representations for the dynamics of each individual sequence using the domain information in totality. We show the competitive performance of our method on real-world time-series data with the baselines and demonstrate its effectiveness for symbolic text modeling and RNA sequence clustering. | [
"['Jonathan Y. Zhou' 'Yao Xie']"
] |
null | null | 2406.06903 | null | null | http://arxiv.org/pdf/2406.06903v1 | 2024-06-11T02:56:13Z | 2024-06-11T02:56:13Z | On the Limitation of Kernel Dependence Maximization for Feature
Selection | A simple and intuitive method for feature selection consists of choosing the feature subset that maximizes a nonparametric measure of dependence between the response and the features. A popular proposal from the literature uses the Hilbert-Schmidt Independence Criterion (HSIC) as the nonparametric dependence measure. The rationale behind this approach to feature selection is that important features will exhibit a high dependence with the response and their inclusion in the set of selected features will increase the HSIC. Through counterexamples, we demonstrate that this rationale is flawed and that feature selection via HSIC maximization can miss critical features. | [
"['Keli Liu' 'Feng Ruan']"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.