id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.06485 | WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry | [
"cond-mat.mtrl-sci",
"cs.AI",
"cs.LG"
] | Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its position or element. We propose a generative model, Wyckoff Diffusion (WyckoffDiff), which generates symmetry-based descriptions of crystal... | {
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2502.06486 | Biomechanical Reconstruction with Confidence Intervals from Multiview
Markerless Motion Capture | [
"cs.CV"
] | Advances in multiview markerless motion capture (MMMC) promise high-quality movement analysis for clinical practice and research. While prior validation studies show MMMC performs well on average, they do not provide what is needed in clinical practice or for large-scale utilization of MMMC -- confidence intervals over... | {
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2502.06487 | Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection | [
"cs.CL"
] | Recent advances on instruction fine-tuning have led to the development of various prompting techniques for large language models, such as explicit reasoning steps. However, the success of techniques depends on various parameters, such as the task, language model, and context provided. Finding an effective prompt is, th... | {
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2502.06490 | Recent Advances in Discrete Speech Tokens: A Review | [
"eess.AS",
"cs.AI",
"cs.MM",
"cs.SD",
"eess.SP"
] | The rapid advancement of speech generation technologies in the era of large language models (LLMs) has established discrete speech tokens as a foundational paradigm for speech representation. These tokens, characterized by their discrete, compact, and concise nature, are not only advantageous for efficient transmission... | {
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2502.06491 | Model-Based Offline Reinforcement Learning with Reliability-Guaranteed
Sequence Modeling | [
"cs.LG",
"cs.AI"
] | Model-based offline reinforcement learning (MORL) aims to learn a policy by exploiting a dynamics model derived from an existing dataset. Applying conservative quantification to the dynamics model, most existing works on MORL generate trajectories that approximate the real data distribution to facilitate policy learnin... | {
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2502.06494 | GuideLLM: Exploring LLM-Guided Conversation with Applications in
Autobiography Interviewing | [
"cs.CL",
"cs.AI"
] | Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LLMs direct the discourse and steer the conversation's objectives-remains under-explored. In this study, we first characterize LLM-guided conv... | {
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2502.06498 | Decision Boundary Optimization-Informed Domain Adaptation | [
"cs.CV"
] | Maximum Mean Discrepancy (MMD) is widely used in a number of domain adaptation (DA) methods and shows its effectiveness in aligning data distributions across domains. However, in previous DA research, MMD-based DA methods focus mostly on distribution alignment, and ignore to optimize the decision boundary for classific... | {
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2502.06501 | Learning Clustering-based Prototypes for Compositional Zero-shot
Learning | [
"cs.CV"
] | Learning primitive (i.e., attribute and object) concepts from seen compositions is the primary challenge of Compositional Zero-Shot Learning (CZSL). Existing CZSL solutions typically rely on oversimplified data assumptions, e.g., modeling each primitive with a single centroid primitive representation, ignoring the natu... | {
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2502.06516 | Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | Minority samples are underrepresented instances located in low-density regions of a data manifold, and are valuable in many generative AI applications, such as data augmentation, creative content generation, etc. Unfortunately, existing diffusion-based minority generators often rely on computationally expensive guidanc... | {
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2502.06519 | SIREN: Semantic, Initialization-Free Registration of Multi-Robot
Gaussian Splatting Maps | [
"cs.RO",
"cs.CV"
] | We present SIREN for registration of multi-robot Gaussian Splatting (GSplat) maps, with zero access to camera poses, images, and inter-map transforms for initialization or fusion of local submaps. To realize these capabilities, SIREN harnesses the versatility and robustness of semantics in three critical ways to derive... | {
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2502.06523 | Tighter Value-Function Approximations for POMDPs | [
"cs.AI"
] | Solving partially observable Markov decision processes (POMDPs) typically requires reasoning about the values of exponentially many state beliefs. Towards practical performance, state-of-the-art solvers use value bounds to guide this reasoning. However, sound upper value bounds are often computationally expensive to co... | {
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2502.06525 | Properties of Wasserstein Gradient Flows for the Sliced-Wasserstein
Distance | [
"stat.ML",
"cs.LG"
] | In this paper, we investigate the properties of the Sliced Wasserstein Distance (SW) when employed as an objective functional. The SW metric has gained significant interest in the optimal transport and machine learning literature, due to its ability to capture intricate geometric properties of probability distributions... | {
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2502.06526 | Convex Split Lemma without Inequalities | [
"quant-ph",
"cs.IT",
"math-ph",
"math.IT",
"math.MP"
] | We introduce a refinement to the convex split lemma by replacing the max mutual information with the collision mutual information, transforming the inequality into an equality. This refinement yields tighter achievability bounds for quantum source coding tasks, including state merging and state splitting. Furthermore, ... | {
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2502.06527 | CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for
Zero-Shot Customized Video Diffusion Transformers | [
"cs.CV"
] | Customized generation has achieved significant progress in image synthesis, yet personalized video generation remains challenging due to temporal inconsistencies and quality degradation. In this paper, we introduce CustomVideoX, an innovative framework leveraging the video diffusion transformer for personalized video g... | {
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2502.06533 | Ignore the KL Penalty! Boosting Exploration on Critical Tokens to
Enhance RL Fine-Tuning | [
"cs.CL",
"cs.LG"
] | The ability to achieve long-term goals is a key challenge in the current development of large language models (LLMs). To address this, pre-trained LLMs can be fine-tuned with reinforcement learning (RL) to explore solutions that optimize a given goal. However, exploration with LLMs is difficult, as a balance has to be ... | {
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2502.06536 | Sample-efficient Learning of Concepts with Theoretical Guarantees: from
Data to Concepts without Interventions | [
"stat.ML",
"cs.LG"
] | Machine learning is a vital part of many real-world systems, but several concerns remain about the lack of interpretability, explainability and robustness of black-box AI systems. Concept-based models (CBM) address some of these challenges by learning interpretable concepts from high-dimensional data, e.g. images, whic... | {
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2502.06543 | Unsupervised Learning for Feature Extraction and Temporal Alignment of
3D+t Point Clouds of Zebrafish Embryos | [
"cs.CV"
] | Zebrafish are widely used in biomedical research and developmental stages of their embryos often need to be synchronized for further analysis. We present an unsupervised approach to extract descriptive features from 3D+t point clouds of zebrafish embryos and subsequently use those features to temporally align correspon... | {
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2502.06544 | Sequence Transferability and Task Order Selection in Continual Learning | [
"cs.LG",
"cs.CV"
] | In continual learning, understanding the properties of task sequences and their relationships to model performance is important for developing advanced algorithms with better accuracy. However, efforts in this direction remain underdeveloped despite encouraging progress in methodology development. In this work, we inve... | {
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2502.06545 | Dimension-free Regret for Learning Asymmetric Linear Dynamical Systems | [
"cs.LG",
"stat.ML"
] | Previously, methods for learning marginally stable linear dynamical systems either required the transition matrix to be symmetric or incurred regret bounds that scale polynomially with the system's hidden dimension. In this work, we introduce a novel method that overcomes this trade-off, achieving dimension-free regret... | {
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2502.06547 | Data Augmentation and Regularization for Learning Group Equivariance | [
"stat.ML",
"cs.LG",
"math.OC"
] | In many machine learning tasks, known symmetries can be used as an inductive bias to improve model performance. In this paper, we consider learning group equivariance through training with data augmentation. We summarize results from a previous paper of our own, and extend the results to show that equivariance of the t... | {
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2502.06551 | Efficient Scientific Full Text Classification: The Case of EICAT Impact
Assessments | [
"cs.CL"
] | This study explores strategies for efficiently classifying scientific full texts using both small, BERT-based models and local large language models like Llama-3.1 8B. We focus on developing methods for selecting subsets of input sentences to reduce input size while simultaneously enhancing classification performance. ... | {
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2502.06552 | Diffusion Models for Computational Neuroimaging: A Survey | [
"cs.CV"
] | Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition and behavior. While diffusion models have shown stability and high-quality generation in natural images, there is increasing interest in adapting them to analyze brain data for ... | {
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2502.06555 | Is API Access to LLMs Useful for Generating Private Synthetic Tabular
Data? | [
"cs.LG",
"cs.CR"
] | Differentially private (DP) synthetic data is a versatile tool for enabling the analysis of private data. Recent advancements in large language models (LLMs) have inspired a number of algorithm techniques for improving DP synthetic data generation. One family of approaches uses DP finetuning on the foundation model wei... | {
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2502.06556 | ProjectTest: A Project-level LLM Unit Test Generation Benchmark and
Impact of Error Fixing Mechanisms | [
"cs.SE",
"cs.CL"
] | Unit test generation has become a promising and important use case of LLMs. However, existing evaluation benchmarks for assessing LLM unit test generation capabilities focus on function- or class-level code rather than more practical and challenging project-level codebases. To address such limitation, we propose Projec... | {
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2502.06557 | LiveForesighter: Generating Future Information for Live-Streaming
Recommendations at Kuaishou | [
"cs.IR"
] | Live-streaming, as a new-generation media to connect users and authors, has attracted a lot of attention and experienced rapid growth in recent years. Compared with the content-static short-video recommendation, the live-streaming recommendation faces more challenges in giving our users a satisfactory experience: (1) L... | {
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2502.06559 | Can We Trust AI Benchmarks? An Interdisciplinary Review of Current
Issues in AI Evaluation | [
"cs.AI"
] | Quantitative Artificial Intelligence (AI) Benchmarks have emerged as fundamental tools for evaluating the performance, capability, and safety of AI models and systems. Currently, they shape the direction of AI development and are playing an increasingly prominent role in regulatory frameworks. As their influence grows,... | {
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2502.06560 | Position: It's Time to Act on the Risk of Efficient Personalized Text
Generation | [
"cs.CL",
"cs.CY"
] | The recent surge in high-quality open-sourced Generative AI text models (colloquially: LLMs), as well as efficient finetuning techniques, has opened the possibility of creating high-quality personalized models, i.e., models generating text attuned to a specific individual's needs and capable of credibly imitating their... | {
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2502.06563 | Large Language Models Meet Symbolic Provers for Logical Reasoning
Evaluation | [
"cs.CL"
] | First-order logic (FOL) reasoning, which involves sequential deduction, is pivotal for intelligent systems and serves as a valuable task for evaluating reasoning capabilities, particularly in chain-of-thought (CoT) contexts. Existing benchmarks often rely on extensive human annotation or handcrafted templates, making i... | {
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2502.06564 | Robust Scatter Matrix Estimation for Elliptical Distributions in
Polynomial Time | [
"cs.DS",
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | We study the problem of computationally efficient robust estimation of scatter matrices of elliptical distributions under the strong contamination model. We design polynomial time algorithms that achieve dimension-independent error in Frobenius norm. Our first result is a sequence of efficient algorithms that approac... | {
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2502.06567 | Membership Inference Risks in Quantized Models: A Theoretical and
Empirical Study | [
"stat.ML",
"cs.LG"
] | Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance levels comparable to the original models. In this work, we investigate the impact of quantization procedures on the privacy of data-driven models, specifically focusing on their vul... | {
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2502.06572 | LawGPT: Knowledge-Guided Data Generation and Its Application to Legal
LLM | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs), both proprietary and open-source, have demonstrated remarkable capabilities across various natural language processing tasks. However, they face significant limitations in legal reasoning tasks. Proprietary models introduce data privacy risks and high inference costs, while open-source mod... | {
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2502.06574 | On the Impact of the Utility in Semivalue-based Data Valuation | [
"cs.AI",
"cs.GT",
"cs.LG"
] | Semivalue-based data valuation in machine learning (ML) quantifies the contribution of individual data points to a downstream ML task by leveraging principles from cooperative game theory and the notion of utility. While this framework has been used in practice for assessing data quality, our experiments reveal inconsi... | {
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2502.06575 | Predictive Red Teaming: Breaking Policies Without Breaking Robots | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | Visuomotor policies trained via imitation learning are capable of performing challenging manipulation tasks, but are often extremely brittle to lighting, visual distractors, and object locations. These vulnerabilities can depend unpredictably on the specifics of training, and are challenging to expose without time-cons... | {
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2502.06577 | The Minimal Search Space for Conditional Causal Bandits | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Causal knowledge can be used to support decision-making problems. This has been recognized in the causal bandits literature, where a causal (multi-armed) bandit is characterized by a causal graphical model and a target variable. The arms are then interventions on the causal model, and rewards are samples of the target ... | {
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2502.06580 | Inventory Consensus Control in Supply Chain Networks using
Dissipativity-Based Control and Topology Co-Design | [
"eess.SY",
"cs.SY"
] | Recent global and local phenomena have exposed vulnerabilities in critical supply chain networks (SCNs), drawing significant attention from researchers across various fields. Typically, SCNs are viewed as static entities regularly optimized to maintain their optimal operation. However, the dynamic nature of SCNs and th... | {
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2502.06581 | A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems | [
"cs.NI",
"cs.CV",
"cs.LG"
] | The explosive growth of video data has driven the development of distributed video analytics in cloud-edge-terminal collaborative (CETC) systems, enabling efficient video processing, real-time inference, and privacy-preserving analysis. Among multiple advantages, CETC systems can distribute video processing tasks and e... | {
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2502.06583 | Adaptive Perception for Unified Visual Multi-modal Object Tracking | [
"cs.CV"
] | Recently, many multi-modal trackers prioritize RGB as the dominant modality, treating other modalities as auxiliary, and fine-tuning separately various multi-modal tasks. This imbalance in modality dependence limits the ability of methods to dynamically utilize complementary information from each modality in complex sc... | {
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2502.06584 | Deep Reinforcement Learning based Triggering Function for Early
Classifiers of Time Series | [
"cs.LG"
] | Early Classification of Time Series (ECTS) has been recognized as an important problem in many areas where decisions have to be taken as soon as possible, before the full data availability, while time pressure increases. Numerous ECTS approaches have been proposed, based on different triggering functions, each taking i... | {
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2502.06585 | Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy,
Stochastic or Uncertain Domains | [
"cs.NE"
] | Quality-Diversity (QD) has demonstrated potential in discovering collections of diverse solutions to optimisation problems. Originally designed for deterministic environments, QD has been extended to noisy, stochastic, or uncertain domains through various Uncertain-QD (UQD) methods. However, the large number of UQD met... | {
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2502.06587 | evclust: Python library for evidential clustering | [
"cs.SE",
"cs.CV",
"cs.LG"
] | A recent developing trend in clustering is the advancement of algorithms that not only identify clusters within data, but also express and capture the uncertainty of cluster membership. Evidential clustering addresses this by using the Dempster-Shafer theory of belief functions, a framework designed to manage and repre... | {
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2502.06589 | Hephaestus: Improving Fundamental Agent Capabilities of Large Language
Models through Continual Pre-Training | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce new capabilities while preserving strong generalizability. We introduce Hephaestus-Forge, the first large-scale pre-training corpus designed t... | {
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2502.06591 | Diffeomorphic Temporal Alignment Nets for Time-series Joint Alignment
and Averaging | [
"cs.LG"
] | In time-series analysis, nonlinear temporal misalignment remains a pivotal challenge that forestalls even simple averaging. Since its introduction, the Diffeomorphic Temporal Alignment Net (DTAN), which we first introduced (Weber et al., 2019) and further developed in (Weber & Freifeld, 2023), has proven itself as an e... | {
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2502.06593 | A Large-scale AI-generated Image Inpainting Benchmark | [
"cs.CV"
] | Recent advances in generative models enable highly realistic image manipulations, creating an urgent need for robust forgery detection methods. Current datasets for training and evaluating these methods are limited in scale and diversity. To address this, we propose a methodology for creating high-quality inpainting da... | {
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2502.06594 | A Review of Conceptualizations of Safety and Risk in Current Automated
Driving Regulation | [
"eess.SY",
"cs.SY"
] | "Safety" and "Risk" are key concepts for the design and development of automated vehicles. For the market introduction or large-scale field tests, both concepts are not only relevant for engineers developing the vehicles, but for all stakeholders (e.g., regulators, lawyers, or the general public) who have stakes in the... | {
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2502.06595 | Surrogate models for diffusion on graphs via sparse polynomials | [
"math.NA",
"cs.LG",
"cs.NA"
] | Diffusion kernels over graphs have been widely utilized as effective tools in various applications due to their ability to accurately model the flow of information through nodes and edges. However, there is a notable gap in the literature regarding the development of surrogate models for diffusion processes on graphs. ... | {
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2502.06597 | Continual Release Moment Estimation with Differential Privacy | [
"cs.LG",
"stat.ML"
] | We propose Joint Moment Estimation (JME), a method for continually and privately estimating both the first and second moments of data with reduced noise compared to naive approaches. JME uses the matrix mechanism and a joint sensitivity analysis to allow the second moment estimation with no additional privacy cost, the... | {
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2502.06599 | Joint parameter and state estimation for regularized time-discrete
multibody dynamics | [
"math.OC",
"cs.SY",
"eess.SY"
] | We develop a method for offline parameter estimation of discrete multibody dynamics with regularized and frictional kinematic constraints. This setting leads to unobserved degrees of freedom, which we handle using joint state and parameter estimation. Our method finds the states and parameters as the solution to a nonl... | {
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2502.06600 | Evaluation of Multilingual Image Captioning: How far can we get with
CLIP models? | [
"cs.CL",
"cs.AI"
] | The evaluation of image captions, looking at both linguistic fluency and semantic correspondence to visual contents, has witnessed a significant effort. Still, despite advancements such as the CLIPScore metric, multilingual captioning evaluation has remained relatively unexplored. This work presents several strategies,... | {
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2502.06601 | Amortized In-Context Bayesian Posterior Estimation | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Bayesian inference provides a natural way of incorporating prior beliefs and assigning a probability measure to the space of hypotheses. Current solutions rely on iterative routines like Markov Chain Monte Carlo (MCMC) sampling and Variational Inference (VI), which need to be re-run whenever new observations are availa... | {
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2502.06604 | Do we really have to filter out random noise in pre-training data for
language models? | [
"cs.CL"
] | Web-scale pre-training datasets are the cornerstone of LLMs' success. However, text data curated from the internet inevitably contains random noise caused by decoding errors or unregulated web content. In contrast to previous works that focus on low quality or synthetic data, our study \textbf{provides the first system... | {
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2502.06606 | MaterialFusion: High-Quality, Zero-Shot, and Controllable Material
Transfer with Diffusion Models | [
"cs.CV"
] | Manipulating the material appearance of objects in images is critical for applications like augmented reality, virtual prototyping, and digital content creation. We present MaterialFusion, a novel framework for high-quality material transfer that allows users to adjust the degree of material application, achieving an o... | {
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2502.06607 | Illegal Waste Detection in Remote Sensing Images: A Case Study | [
"cs.CV",
"cs.AI"
] | Environmental crime currently represents the third largest criminal activity worldwide while threatening ecosystems as well as human health. Among the crimes related to this activity, improper waste management can nowadays be countered more easily thanks to the increasing availability and decreasing cost of Very-High-R... | {
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2502.06608 | TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified
Flow Models | [
"cs.CV",
"cs.AI"
] | Recent advancements in diffusion techniques have propelled image and video generation to unprecedented levels of quality, significantly accelerating the deployment and application of generative AI. However, 3D shape generation technology has so far lagged behind, constrained by limitations in 3D data scale, complexity ... | {
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2502.06615 | Multi-Scale Feature Fusion with Image-Driven Spatial Integration for
Left Atrium Segmentation from Cardiac MRI Images | [
"cs.CV",
"eess.IV"
] | Accurate segmentation of the left atrium (LA) from late gadolinium-enhanced magnetic resonance imaging plays a vital role in visualizing diseased atrial structures, enabling the diagnosis and management of cardiovascular diseases. It is particularly essential for planning treatment with ablation therapy, a key interven... | {
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2502.06617 | Scaling Multi-Document Event Summarization: Evaluating Compression vs.
Full-Text Approaches | [
"cs.CL"
] | Automatically summarizing large text collections is a valuable tool for document research, with applications in journalism, academic research, legal work, and many other fields. In this work, we contrast two classes of systems for large-scale multi-document summarization (MDS): compression and full-text. Compression-ba... | {
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2502.06618 | On the Reliability of Information Retrieval From MDS Coded Data in DNA
Storage | [
"cs.IT",
"cs.ET",
"math.IT"
] | This work presents a theoretical analysis of the probability of successfully retrieving data encoded with MDS codes (e.g., Reed-Solomon codes) in DNA storage systems. We study this probability under independent and identically distributed (i.i.d.) substitution errors, focusing on a common code design strategy that comb... | {
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2502.06619 | Unleashing the Potential of Pre-Trained Diffusion Models for
Generalizable Person Re-Identification | [
"cs.CV"
] | Domain-generalizable re-identification (DG Re-ID) aims to train a model on one or more source domains and evaluate its performance on unseen target domains, a task that has attracted growing attention due to its practical relevance. While numerous methods have been proposed, most rely on discriminative or contrastive l... | {
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2502.06627 | Towards Closing the Gap between Model-Based Systems Engineering and
Automated Vehicle Assurance: Tailoring Generic Methods by Integrating Domain
Knowledge | [
"eess.SY",
"cs.SY"
] | Designing, assuring and releasing safe automated vehicles is a highly interdisciplinary process. As complex systems, automated driving systems will inevitably be subject to emergent properties, i. e., the properties of the overall system will be more than just a sum of the properties of its integrated elements. Safety ... | {
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2502.06631 | Conformal Predictions for Human Action Recognition with Vision-Language
Models | [
"cs.CV",
"cs.AI"
] | Human-In-The-Loop (HITL) frameworks are integral to many real-world computer vision systems, enabling human operators to make informed decisions with AI assistance. Conformal Predictions (CP), which provide label sets with rigorous guarantees on ground truth inclusion probabilities, have recently gained traction as a v... | {
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} |
2502.06632 | Few-Shot Classification and Anatomical Localization of Tissues in SPECT
Imaging | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Accurate classification and anatomical localization are essential for effective medical diagnostics and research, which may be efficiently performed using deep learning techniques. However, availability of limited labeled data poses a significant challenge. To address this, we adapted Prototypical Networks and the Prop... | {
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2502.06633 | Combining Large Language Models with Static Analyzers for Code Review
Generation | [
"cs.SE",
"cs.AI"
] | Code review is a crucial but often complex, subjective, and time-consuming activity in software development. Over the past decades, significant efforts have been made to automate this process. Early approaches focused on knowledge-based systems (KBS) that apply rule-based mechanisms to detect code issues, providing pre... | {
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2502.06634 | Automatic Annotation Augmentation Boosts Translation between Molecules
and Natural Language | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | Recent advancements in AI for biological research focus on integrating molecular data with natural language to accelerate drug discovery. However, the scarcity of high-quality annotations limits progress in this area. This paper introduces LA$^3$, a Language-based Automatic Annotation Augmentation framework that levera... | {
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2502.06635 | Steel-LLM:From Scratch to Open Source -- A Personal Journey in Building
a Chinese-Centric LLM | [
"cs.CL",
"cs.AI"
] | Steel-LLM is a Chinese-centric language model developed from scratch with the goal of creating a high-quality, open-source model despite limited computational resources. Launched in March 2024, the project aimed to train a 1-billion-parameter model on a large-scale dataset, prioritizing transparency and the sharing of ... | {
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2502.06636 | Enhancing healthcare infrastructure resilience through agent-based
simulation methods | [
"cs.MA",
"cs.SY",
"eess.SY"
] | Critical infrastructures face demanding challenges due to natural and human-generated threats, such as pandemics, workforce shortages or cyber-attacks, which might severely compromise service quality. To improve system resilience, decision-makers would need intelligent tools for quick and efficient resource allocation.... | {
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2502.06643 | MoETuner: Optimized Mixture of Expert Serving with Balanced Expert
Placement and Token Routing | [
"cs.LG",
"cs.DC"
] | Mixture-of-Experts (MoE) model architecture has emerged as a promising solution for scaling transformer models efficiently, offering sparse activation that reduces computational costs while increasing model capacity. However, as MoE models scale, they need to be distributed across GPU devices, thus face critical perfor... | {
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2502.06645 | Koopman-Equivariant Gaussian Processes | [
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] | Credible forecasting and representation learning of dynamical systems are of ever-increasing importance for reliable decision-making. To that end, we propose a family of Gaussian processes (GP) for dynamical systems with linear time-invariant responses, which are nonlinear only in initial conditions. This linearity all... | {
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2502.06648 | The 2021 Tokyo Olympics Multilingual News Article Dataset | [
"cs.IR",
"cs.AI",
"cs.CL"
] | In this paper, we introduce a dataset of multilingual news articles covering the 2021 Tokyo Olympics. A total of 10,940 news articles were gathered from 1,918 different publishers, covering 1,350 sub-events of the 2021 Olympics, and published between July 1, 2021, and August 14, 2021. These articles are written in nine... | {
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2502.06649 | Estimation of Food Intake Quantity Using Inertial Signals from
Smartwatches | [
"eess.SP",
"cs.LG"
] | Accurate monitoring of eating behavior is crucial for managing obesity and eating disorders such as bulimia nervosa. At the same time, existing methods rely on multiple and/or specialized sensors, greatly harming adherence and ultimately, the quality and continuity of data. This paper introduces a novel approach for es... | {
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2502.06650 | Prototype Contrastive Consistency Learning for Semi-Supervised Medical
Image Segmentation | [
"cs.CV"
] | Medical image segmentation is a crucial task in medical image analysis, but it can be very challenging especially when there are less labeled data but with large unlabeled data. Contrastive learning has proven to be effective for medical image segmentation in semi-supervised learning by constructing contrastive samples... | {
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2502.06652 | Transparent NLP: Using RAG and LLM Alignment for Privacy Q&A | [
"cs.CL"
] | The transparency principle of the General Data Protection Regulation (GDPR) requires data processing information to be clear, precise, and accessible. While language models show promise in this context, their probabilistic nature complicates truthfulness and comprehensibility. This paper examines state-of-the-art Ret... | {
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2502.06653 | In-Context Learning (and Unlearning) of Length Biases | [
"cs.CL"
] | Large language models have demonstrated strong capabilities to learn in-context, where exemplar input-output pairings are appended to the prompt for demonstration. However, existing work has demonstrated the ability of models to learn lexical and label biases in-context, which negatively impacts both performance and ro... | {
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2502.06655 | Unbiased Evaluation of Large Language Models from a Causal Perspective | [
"cs.AI"
] | Benchmark contamination has become a significant concern in the LLM evaluation community. Previous Agents-as-an-Evaluator address this issue by involving agents in the generation of questions. Despite their success, the biases in Agents-as-an-Evaluator methods remain largely unexplored. In this paper, we present a theo... | {
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2502.06656 | A Frontier AI Risk Management Framework: Bridging the Gap Between
Current AI Practices and Established Risk Management | [
"cs.AI"
] | The recent development of powerful AI systems has highlighted the need for robust risk management frameworks in the AI industry. Although companies have begun to implement safety frameworks, current approaches often lack the systematic rigor found in other high-risk industries. This paper presents a comprehensive risk ... | {
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2502.06658 | Generating Samples to Question Trained Models | [
"cs.LG"
] | There is a growing need for investigating how machine learning models operate. With this work, we aim to understand trained machine learning models by questioning their data preferences. We propose a mathematical framework that allows us to probe trained models and identify their preferred samples in various scenarios ... | {
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2502.06659 | Who Taught You That? Tracing Teachers in Model Distillation | [
"cs.CL"
] | Model distillation -- using outputs from a large teacher model to teach a small student model -- is a practical means of creating efficient models for a particular task. We ask: Can we identify a students' teacher based on its outputs? Such "footprints" left by teacher LLMs would be interesting artifacts. Beyond this, ... | {
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2502.06661 | iLOCO: Distribution-Free Inference for Feature Interactions | [
"stat.ML",
"cs.LG"
] | Feature importance measures are widely studied and are essential for understanding model behavior, guiding feature selection, and enhancing interpretability. However, many machine learning fitted models involve complex, higher-order interactions between features. Existing feature importance metrics fail to capture thes... | {
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2502.06663 | EfficientLLM: Scalable Pruning-Aware Pretraining for
Architecture-Agnostic Edge Language Models | [
"cs.LG"
] | Modern large language models (LLMs) driven by scaling laws, achieve intelligence emergency in large model sizes. Recently, the increasing concerns about cloud costs, latency, and privacy make it an urgent requirement to develop compact edge language models. Distinguished from direct pretraining that bounded by the scal... | {
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2502.06664 | Evaluation of Deep Audio Representations for Hearables | [
"cs.SD",
"cs.AI",
"cs.LG"
] | Effectively steering hearable devices requires understanding the acoustic environment around the user. In the computational analysis of sound scenes, foundation models have emerged as the state of the art to produce high-performance, robust, multi-purpose audio representations. We introduce and release Deep Evaluation ... | {
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2502.06666 | Automatic Evaluation of Healthcare LLMs Beyond Question-Answering | [
"cs.CL",
"cs.AI"
] | Current Large Language Models (LLMs) benchmarks are often based on open-ended or close-ended QA evaluations, avoiding the requirement of human labor. Close-ended measurements evaluate the factuality of responses but lack expressiveness. Open-ended capture the model's capacity to produce discourse responses but are hard... | {
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2502.06669 | Boosting Self-Efficacy and Performance of Large Language Models via
Verbal Efficacy Stimulations | [
"cs.CL",
"cs.AI"
] | Significant improvements have been observed in the zero-shot capabilities of the Large Language Models (LLMs). Due to their high sensitivity to input, research has increasingly focused on enhancing LLMs' performance via direct and simple prompt engineering rather than intricate domain adaptation. Studies suggest that L... | {
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2502.06673 | Selecting Optimal Sampling Rate for Stable Super-Resolution | [
"math.NA",
"cs.IT",
"cs.NA",
"math.IT"
] | We investigate the recovery of nodes and amplitudes from noisy frequency samples in spike train signals, also known as the super-resolution (SR) problem. When the node separation falls below the Rayleigh limit, the problem becomes ill-conditioned. Admissible sampling rates, or decimation parameters, improve the conditi... | {
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2502.06674 | RAILS: Risk-Aware Iterated Local Search for Joint SLA Decomposition and
Service Provider Management in Multi-Domain Networks | [
"cs.NI",
"cs.LG"
] | The emergence of the fifth generation (5G) technology has transformed mobile networks into multi-service environments, necessitating efficient network slicing to meet diverse Service Level Agreements (SLAs). SLA decomposition across multiple network domains, each potentially managed by different service providers, pose... | {
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2502.06676 | Discovery of skill switching criteria for learning agile quadruped
locomotion | [
"cs.RO"
] | This paper develops a hierarchical learning and optimization framework that can learn and achieve well-coordinated multi-skill locomotion. The learned multi-skill policy can switch between skills automatically and naturally in tracking arbitrarily positioned goals and recover from failures promptly. The proposed framew... | {
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2502.06678 | Quantile Multi-Armed Bandits with 1-bit Feedback | [
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT"
] | In this paper, we study a variant of best-arm identification involving elements of risk sensitivity and communication constraints. Specifically, the goal of the learner is to identify the arm with the highest quantile reward, while the communication from an agent (who observes rewards) and the learner (who chooses acti... | {
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2502.06681 | CHIRLA: Comprehensive High-resolution Identification and
Re-identification for Large-scale Analysis | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Person re-identification (Re-ID) is a key challenge in computer vision, requiring the matching of individuals across different cameras, locations, and time periods. While most research focuses on short-term scenarios with minimal appearance changes, real-world applications demand robust Re-ID systems capable of handlin... | {
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2502.06682 | Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint
in a Driving Scene | [
"cs.CV"
] | Self-driving cars relying solely on ego-centric perception face limitations in sensing, often failing to detect occluded, faraway objects. Collaborative autonomous driving (CAV) seems like a promising direction, but collecting data for development is non-trivial. It requires placing multiple sensor-equipped agents in a... | {
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2502.06683 | Solving Optimal Power Flow on a Data-Budget: Feature Selection on Smart
Meter Data | [
"eess.SY",
"cs.SY"
] | How much data is needed to optimally schedule distributed energy resources (DERs)? Does the distribution system operator (DSO) have to precisely know load demands and solar injections at each bus of the feeder to solve an optimal power flow (OPF)? This work exploits redundancies in OPF's structure and data to avoid com... | {
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2502.06684 | EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks | [
"cs.LG",
"cs.AI"
] | Recent foundational models for tabular data, such as TabPFN, have demonstrated remarkable effectiveness in adapting to new tasks through in-context learning. However, these models overlook a crucial equivariance property: the arbitrary ordering of target dimensions should not influence model predictions. In this study,... | {
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2502.06685 | No Trick, No Treat: Pursuits and Challenges Towards Simulation-free
Training of Neural Samplers | [
"cs.LG",
"stat.ML"
] | We consider the sampling problem, where the aim is to draw samples from a distribution whose density is known only up to a normalization constant. Recent breakthroughs in generative modeling to approximate a high-dimensional data distribution have sparked significant interest in developing neural network-based methods ... | {
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2502.06689 | Neumann eigenmaps for landmark embedding | [
"math.ST",
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML",
"stat.TH"
] | We present Neumann eigenmaps (NeuMaps), a novel approach for enhancing the standard diffusion map embedding using landmarks, i.e distinguished samples within the dataset. By interpreting these landmarks as a subgraph of the larger data graph, NeuMaps are obtained via the eigendecomposition of a renormalized Neumann Lap... | {
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2502.06692 | Multi-label Scandinavian Language Identification (SLIDE) | [
"cs.CL",
"cs.AI"
] | Identifying closely related languages at sentence level is difficult, in particular because it is often impossible to assign a sentence to a single language. In this paper, we focus on multi-label sentence-level Scandinavian language identification (LID) for Danish, Norwegian Bokm\r{a}l, Norwegian Nynorsk, and Swedish.... | {
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} |
2502.06693 | Recent Advances, Applications and Open Challenges in Machine Learning
for Health: Reflections from Research Roundtables at ML4H 2024 Symposium | [
"cs.LG",
"cs.AI",
"cs.CY"
] | The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam, Squamish, and Tsleil-Waututh Nations in Vancouver, British Columbia, Canada. The symposium included research roundtable sessions to foster d... | {
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} |
2502.06695 | FairDropout: Using Example-Tied Dropout to Enhance Generalization of
Minority Groups | [
"cs.LG"
] | Deep learning models frequently exploit spurious features in training data to achieve low training error, often resulting in poor generalization when faced with shifted testing distributions. To address this issue, various methods from imbalanced learning, representation learning, and classifier recalibration have been... | {
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} |
2502.06698 | Heisenberg-limited calibration of entangling gates with robust phase
estimation | [
"quant-ph",
"cs.SY",
"eess.SY"
] | The calibration of high-quality two-qubit entangling gates is an essential component in engineering large-scale, fault-tolerant quantum computers. However, many standard calibration techniques are based on randomized circuits that are only quadratically sensitive to calibration errors. As a result, these approaches are... | {
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} |
2502.06703 | Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time
Scaling | [
"cs.CL"
] | Test-Time Scaling (TTS) is an important method for improving the performance of Large Language Models (LLMs) by using additional computation during the inference phase. However, current studies do not systematically analyze how policy models, Process Reward Models (PRMs), and problem difficulty influence TTS. This lack... | {
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} |
2502.06705 | RSAttAE: An Information-Aware Attention-based Autoencoder Recommender
System | [
"cs.LG",
"cs.IR"
] | Recommender systems play a crucial role in modern life, including information retrieval, the pharmaceutical industry, retail, and entertainment. The entertainment sector, in particular, attracts significant attention and generates substantial profits. This work proposes a new method for predicting unknown user-movie ra... | {
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} |
2502.06707 | FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock
Movement Prediction | [
"cs.CE"
] | Recently, combining stock features with inter-stock correlations has become a common and effective approach for stock movement prediction. However, financial data presents significant challenges due to its low signal-to-noise ratio and the dynamic complexity of the market, which give rise to two key limitations in exis... | {
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} |
2502.06708 | TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic
Videos using Surgical Timeline Segmentation | [
"cs.CV"
] | Indexing endoscopic surgical videos is vital in surgical data science, forming the basis for systematic retrospective analysis and clinical performance evaluation. Despite its significance, current video analytics rely on manual indexing, a time-consuming process. Advances in computer vision, particularly deep learning... | {
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} |
2502.06710 | Learning Musical Representations for Music Performance Question
Answering | [
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | Music performances are representative scenarios for audio-visual modeling. Unlike common scenarios with sparse audio, music performances continuously involve dense audio signals throughout. While existing multimodal learning methods on the audio-video QA demonstrate impressive capabilities in general scenarios, they ar... | {
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} |
2502.06715 | HoneyComb: A Parallel Worst-Case Optimal Join on Multicores | [
"cs.DB"
] | To achieve true scalability on massive datasets, a modern query engine needs to be able to take advantage of large, shared-memory, multicore systems. Binary joins are conceptually easy to parallelize on a multicore system; however, several applications require a different approach to query evaluation, using a Worst-Cas... | {
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} |
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