id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.03382 | High-Fidelity Simultaneous Speech-To-Speech Translation | [
"cs.CL",
"cs.SD",
"eess.AS"
] | We introduce Hibiki, a decoder-only model for simultaneous speech translation. Hibiki leverages a multistream language model to synchronously process source and target speech, and jointly produces text and audio tokens to perform speech-to-text and speech-to-speech translation. We furthermore address the fundamental ch... | {
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2502.03383 | Transformers and Their Roles as Time Series Foundation Models | [
"cs.LG",
"cs.AI"
] | We give a comprehensive analysis of transformers as time series foundation models, focusing on their approximation and generalization capabilities. First, we demonstrate that there exist transformers that fit an autoregressive model on input univariate time series via gradient descent. We then analyze MOIRAI, a multiva... | {
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2502.03386 | A Structured Reasoning Framework for Unbalanced Data Classification
Using Probabilistic Models | [
"cs.LG"
] | This paper studies a Markov network model for unbalanced data, aiming to solve the problems of classification bias and insufficient minority class recognition ability of traditional machine learning models in environments with uneven class distribution. By constructing joint probability distribution and conditional dep... | {
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2502.03387 | LIMO: Less is More for Reasoning | [
"cs.CL",
"cs.AI"
] | We present a fundamental discovery that challenges our understanding of how complex reasoning emerges in large language models. While conventional wisdom suggests that sophisticated reasoning tasks demand extensive training data (>100,000 examples), we demonstrate that complex mathematical reasoning abilities can be ef... | {
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2502.03391 | Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise
Sufficient Reasons | [
"cs.LG",
"cs.LO"
] | *Minimal sufficient reasons* represent a prevalent form of explanation - the smallest subset of input features which, when held constant at their corresponding values, ensure that the prediction remains unchanged. Previous *post-hoc* methods attempt to obtain such explanations but face two main limitations: (1) Obtaini... | {
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2502.03393 | CAPE: Covariate-Adjusted Pre-Training for Epidemic Time Series
Forecasting | [
"cs.LG"
] | Accurate forecasting of epidemic infection trajectories is crucial for safeguarding public health. However, limited data availability during emerging outbreaks and the complex interaction between environmental factors and disease dynamics present significant challenges for effective forecasting. In response, we introdu... | {
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2502.03395 | Benchmarking Time Series Forecasting Models: From Statistical Techniques
to Foundation Models in Real-World Applications | [
"cs.LG",
"cs.AI"
] | Time series forecasting is essential for operational intelligence in the hospitality industry, and particularly challenging in large-scale, distributed systems. This study evaluates the performance of statistical, machine learning (ML), deep learning, and foundation models in forecasting hourly sales over a 14-day hori... | {
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2502.03396 | Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS
Digital Twin | [
"cs.LG",
"cs.AI",
"cs.ET"
] | Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time and track their real-time location is crucial to the medical authorities. Despi... | {
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2502.03397 | SPRI: Aligning Large Language Models with Context-Situated Principles | [
"cs.CL",
"cs.AI"
] | Aligning Large Language Models to integrate and reflect human values, especially for tasks that demand intricate human oversight, is arduous since it is resource-intensive and time-consuming to depend on human expertise for context-specific guidance. Prior work has utilized predefined sets of rules or principles to ste... | {
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2502.03400 | DenseReviewer: A Screening Prioritisation Tool for Systematic Review
based on Dense Retrieval | [
"cs.IR"
] | Screening is a time-consuming and labour-intensive yet required task for medical systematic reviews, as tens of thousands of studies often need to be screened. Prioritising relevant studies to be screened allows downstream systematic review creation tasks to start earlier and save time. In previous work, we developed a... | {
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2502.03403 | Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin
Networks | [
"cs.CR",
"cs.AI"
] | Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and... | {
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2502.03405 | Deep Clustering via Probabilistic Ratio-Cut Optimization | [
"cs.LG",
"cs.CV"
] | We propose a novel approach for optimizing the graph ratio-cut by modeling the binary assignments as random variables. We provide an upper bound on the expected ratio-cut, as well as an unbiased estimate of its gradient, to learn the parameters of the assignment variables in an online setting. The clustering resulting ... | {
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2502.03407 | Detecting Strategic Deception Using Linear Probes | [
"cs.LG"
] | AI models might use deceptive strategies as part of scheming or misaligned behaviour. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while their internal reasoning is misaligned. We thus evaluate if linear probes can robustly detect deception by monitoring model activation... | {
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2502.03409 | Verification and Synthesis Methods for High-Order Control Barrier
Functions | [
"eess.SY",
"cs.SY"
] | High-order control barrier functions (HOCBFs) can be used to provide autonomous systems with safety, though computational methods to verify and synthesize these functions remain lacking. In this work, we address this need by formulating SOS programs that verify and synthesize HOCBFs, such that continued safety is alway... | {
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2502.03411 | Cryptocurrency Network Analysis | [
"cs.SI",
"cs.CY",
"cs.NI"
] | Cryptocurrency network analysis consists of applying the tools and methods of social network analysis to transactional data issued from cryptocurrencies. The main difference with most online social networks is that users do not exchange textual content but instead value -- in systems designed mainly as cryptocurrency, ... | {
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2502.03412 | Deep Reinforcement Learning-Based Optimization of Second-Life Battery
Utilization in Electric Vehicles Charging Stations | [
"eess.SY",
"cs.LG",
"cs.SY"
] | The rapid rise in electric vehicle (EV) adoption presents significant challenges in managing the vast number of retired EV batteries. Research indicates that second-life batteries (SLBs) from EVs typically retain considerable residual capacity, offering extended utility. These batteries can be effectively repurposed fo... | {
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2502.03417 | From Features to Transformers: Redefining Ranking for Scalable Impact | [
"cs.LG"
] | We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modified transformer architecture that incorporates learned normalization and simultaneous set-wise attention to user history and ranked items. Thi... | {
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2502.03418 | Think or Step-by-Step? UnZIPping the Black Box in Zero-Shot Prompts | [
"cs.CL"
] | Zero-shot prompting techniques have significantly improved the performance of Large Language Models (LLMs). However, we lack a clear understanding of why zero-shot prompts are so effective. For example, in the prompt "Let's think step-by-step," is "think" or "step-by-step" more crucial to its success? Existing interpre... | {
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2502.03420 | Can Text-to-Image Generative Models Accurately Depict Age? A Comparative
Study on Synthetic Portrait Generation and Age Estimation | [
"cs.CV"
] | Text-to-image generative models have shown remarkable progress in producing diverse and photorealistic outputs. In this paper, we present a comprehensive analysis of their effectiveness in creating synthetic portraits that accurately represent various demographic attributes, with a special focus on age, nationality, an... | {
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2502.03421 | Investigating Corporate Social Responsibility Initiatives: Examining the
case of corporate Covid-19 response | [
"cs.IR"
] | In todays age of freely available information, policy makers have to take into account a huge amount of information while making decisions affecting relevant stakeholders. While increase in the amount of information sources and documents increases credibility of decisions based on the corpus of available text, it is ch... | {
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2502.03422 | Concept Based Explanations and Class Contrasting | [
"cs.CV"
] | Explaining deep neural networks is challenging, due to their large size and non-linearity. In this paper, we introduce a concept-based explanation method, in order to explain the prediction for an individual class, as well as contrasting any two classes, i.e. explain why the model predicts one class over the other. We ... | {
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2502.03424 | Prediction of the Most Fire-Sensitive Point in Building Structures with
Differentiable Agents for Thermal Simulators | [
"cs.LG"
] | Fire safety is a critical area of research in civil and mechanical engineering, particularly in ensuring the structural stability of buildings during fire events. The Most Fire-Sensitive Point (MFSP) in a structure is the location where a fire would cause the greatest impact on structural stability. Accurate prediction... | {
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2502.03426 | TruePose: Human-Parsing-guided Attention Diffusion for Full-ID
Preserving Pose Transfer | [
"cs.CV",
"cs.AI"
] | Pose-Guided Person Image Synthesis (PGPIS) generates images that maintain a subject's identity from a source image while adopting a specified target pose (e.g., skeleton). While diffusion-based PGPIS methods effectively preserve facial features during pose transformation, they often struggle to accurately maintain clot... | {
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2502.03429 | On Fairness of Unified Multimodal Large Language Model for Image
Generation | [
"cs.CL",
"cs.AI"
] | Unified multimodal large language models (U-MLLMs) have demonstrated impressive performance in visual understanding and generation in an end-to-end pipeline. Compared with generation-only models (e.g., Stable Diffusion), U-MLLMs may raise new questions about bias in their outputs, which can be affected by their unified... | {
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2502.03430 | A Temporal Convolutional Network-Based Approach and a Benchmark Dataset
for Colonoscopy Video Temporal Segmentation | [
"cs.CV",
"eess.IV"
] | Following recent advancements in computer-aided detection and diagnosis systems for colonoscopy, the automated reporting of colonoscopy procedures is set to further revolutionize clinical practice. A crucial yet underexplored aspect in the development of these systems is the creation of computer vision models capable o... | {
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2502.03433 | Analyzing Political Discourse on Discord during the 2024 U.S.
Presidential Election | [
"cs.SI"
] | Social media networks have amplified the reach of social and political movements, but most research focuses on mainstream platforms such as X, Reddit, and Facebook, overlooking Discord. As a rapidly growing, community-driven platform with optional decentralized moderation, Discord offers unique opportunities to study p... | {
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2502.03435 | Taking a Big Step: Large Learning Rates in Denoising Score Matching
Prevent Memorization | [
"stat.ML",
"cs.LG"
] | Denoising score matching plays a pivotal role in the performance of diffusion-based generative models. However, the empirical optimal score--the exact solution to the denoising score matching--leads to memorization, where generated samples replicate the training data. Yet, in practice, only a moderate degree of memoriz... | {
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2502.03438 | BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic
Theorem Proving | [
"cs.AI"
] | Recent advancements in large language models (LLMs) have spurred growing interest in automatic theorem proving using Lean4, where effective tree search methods are crucial for navigating proof search spaces. While the existing approaches primarily rely on value functions and Monte Carlo Tree Search (MCTS), the potentia... | {
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2502.03439 | Linearized Optimal Transport pyLOT Library: A Toolkit for Machine
Learning on Point Clouds | [
"stat.ML",
"cs.LG",
"cs.MS",
"stat.CO"
] | The pyLOT library offers a Python implementation of linearized optimal transport (LOT) techniques and methods to use in downstream tasks. The pipeline embeds probability distributions into a Hilbert space via the Optimal Transport maps from a fixed reference distribution, and this linearization allows downstream tasks ... | {
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2502.03444 | Masked Autoencoders Are Effective Tokenizers for Diffusion Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advances in latent diffusion models have demonstrated their effectiveness for high-resolution image synthesis. However, the properties of the latent space from tokenizer for better learning and generation of diffusion models remain under-explored. Theoretically and empirically, we find that improved generation q... | {
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2502.03449 | Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion
Prior and Differentiable Physics | [
"cs.CV"
] | Recent advances in large models have significantly advanced image-to-3D reconstruction. However, the generated models are often fused into a single piece, limiting their applicability in downstream tasks. This paper focuses on 3D garment generation, a key area for applications like virtual try-on with dynamic garment a... | {
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2502.03450 | A Schema-Guided Reason-while-Retrieve framework for Reasoning on Scene
Graphs with Large-Language-Models (LLMs) | [
"cs.LG",
"cs.AI",
"cs.MA",
"cs.RO"
] | Scene graphs have emerged as a structured and serializable environment representation for grounded spatial reasoning with Large Language Models (LLMs). In this work, we propose SG-RwR, a Schema-Guided Retrieve-while-Reason framework for reasoning and planning with scene graphs. Our approach employs two cooperative, cod... | {
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2502.03454 | Kineto-Dynamical Planning and Accurate Execution of Minimum-Time
Maneuvers on Three-Dimensional Circuits | [
"cs.RO"
] | Online planning and execution of minimum-time maneuvers on three-dimensional (3D) circuits is an open challenge in autonomous vehicle racing. In this paper, we present an artificial race driver (ARD) to learn the vehicle dynamics, plan and execute minimum-time maneuvers on a 3D track. ARD integrates a novel kineto-dyna... | {
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2502.03459 | SKI Models: Skeleton Induced Vision-Language Embeddings for
Understanding Activities of Daily Living | [
"cs.CV"
] | The introduction of vision-language models like CLIP has enabled the development of foundational video models capable of generalizing to unseen videos and human actions. However, these models are typically trained on web videos, which often fail to capture the challenges present in Activities of Daily Living (ADL) vide... | {
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2502.03460 | Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language
Model Training | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Small language models (SLMs) have attracted considerable attention from both academia and industry due to their broad range of applications in edge devices. To obtain SLMs with strong performance, conventional approaches either pre-train the models from scratch, which incurs substantial computational costs, or compress... | {
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2502.03461 | Do Large Language Model Benchmarks Test Reliability? | [
"cs.LG",
"cs.CL"
] | When deploying large language models (LLMs), it is important to ensure that these models are not only capable, but also reliable. Many benchmarks have been created to track LLMs' growing capabilities, however there has been no similar focus on measuring their reliability. To understand the potential ramifications of th... | {
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2502.03465 | Seeing World Dynamics in a Nutshell | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.MM"
] | We consider the problem of efficiently representing casually captured monocular videos in a spatially- and temporally-coherent manner. While existing approaches predominantly rely on 2D/2.5D techniques treating videos as collections of spatiotemporal pixels, they struggle with complex motions, occlusions, and geometric... | {
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2502.03467 | Where AI Assurance Might Go Wrong: Initial lessons from engineering of
critical systems | [
"cs.CY",
"cs.AI",
"cs.SE"
] | We draw on our experience working on system and software assurance and evaluation for systems important to society to summarise how safety engineering is performed in traditional critical systems, such as aircraft flight control. We analyse how this critical systems perspective might support the development and impleme... | {
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2502.03469 | A Capability Approach to AI Ethics | [
"cs.CY",
"cs.AI"
] | We propose a conceptualization and implementation of AI ethics via the capability approach. We aim to show that conceptualizing AI ethics through the capability approach has two main advantages for AI ethics as a discipline. First, it helps clarify the ethical dimension of AI tools. Second, it provides guidance to impl... | {
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2502.03478 | From In Silico to In Vitro: A Comprehensive Guide to Validating
Bioinformatics Findings | [
"q-bio.GN",
"cs.CE"
] | The integration of bioinformatics predictions and experimental validation plays a pivotal role in advancing biological research, from understanding molecular mechanisms to developing therapeutic strategies. Bioinformatics tools and methods offer powerful means for predicting gene functions, protein interactions, and re... | {
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2502.03480 | Foundation for unbiased cross-validation of spatio-temporal models for
species distribution modeling | [
"stat.AP",
"cs.LG"
] | Species Distribution Models (SDMs) often suffer from spatial autocorrelation (SAC), leading to biased performance estimates. We tested cross-validation (CV) strategies - random splits, spatial blocking with varied distances, environmental (ENV) clustering, and a novel spatio-temporal method - under two proposed trainin... | {
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2502.03482 | Can Domain Experts Rely on AI Appropriately? A Case Study on AI-Assisted
Prostate Cancer MRI Diagnosis | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.CY",
"cs.HC",
"cs.LG"
] | Despite the growing interest in human-AI decision making, experimental studies with domain experts remain rare, largely due to the complexity of working with domain experts and the challenges in setting up realistic experiments. In this work, we conduct an in-depth collaboration with radiologists in prostate cancer dia... | {
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2502.03484 | Dementia Classification Using Acoustic Speech and Feature Selection | [
"eess.AS",
"cs.LG",
"cs.SD"
] | Dementia is a general term for a group of syndromes that affect cognitive functions such as memory, thinking, reasoning, and the ability to perform daily tasks. The number of dementia patients is increasing as the population ages, and it is estimated that over 10 million people develop dementia each year. Dementia prog... | {
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2502.03487 | Artificial Intelligence and Legal Analysis: Implications for Legal
Education and the Profession | [
"cs.CY",
"cs.AI"
] | This article reports the results of a study examining the ability of legal and non-legal Large Language Models to perform legal analysis using the Issue-Rule-Application-Conclusion framework. LLMs were tested on legal reasoning tasks involving rule analysis and analogical reasoning. The results show that LLMs can condu... | {
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2502.03490 | Examining Two Hop Reasoning Through Information Content Scaling | [
"cs.AI",
"cs.LG"
] | Prior work has found that transformers have an inconsistent ability to learn to answer latent two-hop questions -- questions of the form "Who is Bob's mother's boss?" We study why this is the case by examining how transformers' capacity to learn datasets of two-hop questions and answers (two-hop QA) scales with their s... | {
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2502.03492 | Teaching Language Models to Critique via Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Teaching large language models (LLMs) to critique and refine their outputs is crucial for building systems that can iteratively improve, yet it is fundamentally limited by the ability to provide accurate judgments and actionable suggestions. In this work, we study LLM critics for code generation and propose $\texttt{CT... | {
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2502.03493 | MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from
Monocular Endoscopic Images | [
"eess.IV",
"cs.CV"
] | Depth estimation from monocular endoscopic images presents significant challenges due to the complexity of endoscopic surgery, such as irregular shapes of human soft tissues, as well as variations in lighting conditions. Existing methods primarily estimate the depth information from RGB images directly, and often surff... | {
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2502.03499 | Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and
Multi-Task Learning | [
"q-bio.GN",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) demonstrate remarkable generalizability across diverse tasks, yet genomic foundation models (GFMs) still require separate finetuning for each downstream application, creating significant overhead as model sizes grow. Moreover, existing GFMs are constrained by rigid output formats, limiting ... | {
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2502.03500 | Efficient Image Restoration via Latent Consistency Flow Matching | [
"eess.IV",
"cs.AI",
"stat.AP"
] | Recent advances in generative image restoration (IR) have demonstrated impressive results. However, these methods are hindered by their substantial size and computational demands, rendering them unsuitable for deployment on edge devices. This work introduces ELIR, an Efficient Latent Image Restoration method. ELIR oper... | {
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2502.03501 | Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for
Medical Segmentation | [
"eess.IV",
"cs.LG"
] | In this paper, we aim to address the unmet demand for automated prompting and enhanced human-model interactions of SAM and SAM2 for the sake of promoting their widespread clinical adoption. Specifically, we propose Proxy Prompt (PP), auto-generated by leveraging non-target data with a pre-annotated mask. We devise a no... | {
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2502.03502 | DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with
Video Diffusion Prior | [
"eess.IV",
"cs.AI",
"cs.GR"
] | Video super-resolution (VSR) aims to reconstruct a high-resolution (HR) video from a low-resolution (LR) counterpart. Achieving successful VSR requires producing realistic HR details and ensuring both spatial and temporal consistency. To restore realistic details, diffusion-based VSR approaches have recently been propo... | {
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2502.03503 | Two in context learning tasks with complex functions | [
"stat.ML",
"cs.AI",
"cs.LG"
] | We examine two in context learning (ICL) tasks with mathematical functions in several train and test settings for transformer models. Our study generalizes work on linear functions by showing that small transformers, even models with attention layers only, can approximate arbitrary polynomial functions and hence contin... | {
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2502.03504 | Immersion for AI: Immersive Learning with Artificial Intelligence | [
"q-bio.NC",
"cs.AI",
"cs.HC"
] | This work reflects upon what Immersion can mean from the perspective of an Artificial Intelligence (AI). Applying the lens of immersive learning theory, it seeks to understand whether this new perspective supports ways for AI participation in cognitive ecologies. By treating AI as a participant rather than a tool, it e... | {
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2502.03505 | Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using
Deep Learning | [
"eess.IV",
"cs.AI",
"cs.LG"
] | This study introduces a motion-based learning network with a global-local self-attention module (MoGLo-Net) to enhance 3D reconstruction in handheld photoacoustic and ultrasound (PAUS) imaging. Standard PAUS imaging is often limited by a narrow field of view and the inability to effectively visualize complex 3D structu... | {
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2502.03506 | Optimistic {\epsilon}-Greedy Exploration for Cooperative Multi-Agent
Reinforcement Learning | [
"cs.MA",
"cs.LG"
] | The Centralized Training with Decentralized Execution (CTDE) paradigm is widely used in cooperative multi-agent reinforcement learning. However, due to the representational limitations of traditional monotonic value decomposition methods, algorithms can underestimate optimal actions, leading policies to suboptimal solu... | {
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2502.03508 | Elucidation of the Concept of Consciousness from the Theory of Non-Human
Communication Agents | [
"q-bio.NC",
"cs.AI",
"cs.HC"
] | This article focuses on elucidating the concept of consciousness from a relational and post-phenomenological theory of non-human communication agents (ANHC). Specifically, we explore the contributions of Thomas Metzinger s Self Model Theory, Katherine Hayles conceptualizations of non-conscious cognitive processes cente... | {
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2502.03510 | Mapping and Localization Using LiDAR Fiducial Markers | [
"cs.CV"
] | LiDAR sensors are essential for autonomous systems, yet LiDAR fiducial markers (LFMs) lag behind visual fiducial markers (VFMs) in adoption and utility. Bridging this gap is vital for robotics and computer vision but challenging due to the sparse, unstructured nature of 3D LiDAR data and 2D-focused fiducial marker desi... | {
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2502.03511 | An Empirical Exploration of ChatGPT's Ability to Support Problem
Formulation Tasks for Mission Engineering and a Documentation of its
Performance Variability | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercise... | {
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2502.03512 | YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing
Multi-Objective Optimization based DPO for Text-to-Image Alignment | [
"cs.AI"
] | Precise alignment in Text-to-Image (T2I) systems is crucial to ensure that generated visuals not only accurately encapsulate user intents but also conform to stringent ethical and aesthetic benchmarks. Incidents like the Google Gemini fiasco, where misaligned outputs triggered significant public backlash, underscore th... | {
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2502.03540 | Path Planning for Masked Diffusion Model Sampling | [
"cs.LG",
"cs.AI"
] | In this paper, we explore how token unmasking order influences generative quality in masked diffusion models (MDMs). We derive an expanded evidence lower bound (ELBO) that introduces a planner to select which tokens to unmask at each step. Our analysis reveals that alternative unmasking strategies can enhance generatio... | {
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2502.03544 | Gold-medalist Performance in Solving Olympiad Geometry with
AlphaGeometry2 | [
"cs.AI",
"cs.LG"
] | We present AlphaGeometry2, a significantly improved version of AlphaGeometry introduced in Trinh et al. (2024), which has now surpassed an average gold medalist in solving Olympiad geometry problems. To achieve this, we first extend the original AlphaGeometry language to tackle harder problems involving movements of ob... | {
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2502.03545 | Proportional Selection in Networks | [
"cs.GT",
"cs.AI",
"cs.MA",
"cs.SI"
] | We address the problem of selecting $k$ representative nodes from a network, aiming to achieve two objectives: identifying the most influential nodes and ensuring the selection proportionally reflects the network's diversity. We propose two approaches to accomplish this, analyze them theoretically, and demonstrate thei... | {
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2502.03549 | Kronecker Mask and Interpretive Prompts are Language-Action Video
Learners | [
"cs.CV"
] | Contrastive language-image pretraining (CLIP) has significantly advanced image-based vision learning. A pressing topic subsequently arises: how can we effectively adapt CLIP to the video domain? Recent studies have focused on adjusting either the textual or visual branch of CLIP for action recognition. However, we argu... | {
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2502.03550 | TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy
Constraint | [
"cs.LG",
"cs.RO"
] | Model-based reinforcement learning algorithms that combine model-based planning and learned value/policy prior have gained significant recognition for their high data efficiency and superior performance in continuous control. However, we discover that existing methods that rely on standard SAC-style policy iteration fo... | {
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2502.03551 | Online Learning Algorithms in Hilbert Spaces with $\beta-$ and
$\phi-$Mixing Sequences | [
"stat.ML",
"cs.LG",
"math.FA"
] | In this paper, we study an online algorithm in a reproducing kernel Hilbert spaces (RKHS) based on a class of dependent processes, called the mixing process. For such a process, the degree of dependence is measured by various mixing coefficients. As a representative example, we analyze a strictly stationary Markov chai... | {
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2502.03552 | Can Cross Encoders Produce Useful Sentence Embeddings? | [
"cs.CL",
"cs.IR"
] | Cross encoders (CEs) are trained with sentence pairs to detect relatedness. As CEs require sentence pairs at inference, the prevailing view is that they can only be used as re-rankers in information retrieval pipelines. Dual encoders (DEs) are instead used to embed sentences, where sentence pairs are encoded by two sep... | {
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2502.03553 | Efficient Global Neural Architecture Search | [
"cs.CV"
] | Neural architecture search (NAS) has shown promise towards automating neural network design for a given task, but it is computationally demanding due to training costs associated with evaluating a large number of architectures to find the optimal one. To speed up NAS, recent works limit the search to network building b... | {
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2502.03566 | CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally | [
"cs.CV",
"cs.LG"
] | CLIP (Contrastive Language-Image Pretraining) has become a popular choice for various downstream tasks. However, recent studies have questioned its ability to represent compositional concepts effectively. These works suggest that CLIP often acts like a bag-of-words (BoW) model, interpreting images and text as sets of i... | {
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2502.03568 | Code Simulation as a Proxy for High-order Tasks in Large Language Models | [
"cs.LG",
"cs.AI"
] | Many reasoning, planning, and problem-solving tasks share an intrinsic algorithmic nature: correctly simulating each step is a sufficient condition to solve them correctly. We collect pairs of naturalistic and synthetic reasoning tasks to assess the capabilities of Large Language Models (LLM). While naturalistic tasks ... | {
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2502.03569 | Controllable Sequence Editing for Counterfactual Generation | [
"cs.LG",
"q-bio.GN",
"q-bio.PE"
] | Sequence models generate counterfactuals by modifying parts of a sequence based on a given condition, enabling reasoning about "what if" scenarios. While these models excel at conditional generation, they lack fine-grained control over when and where edits occur. Existing approaches either focus on univariate sequences... | {
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2502.03571 | A Multi-Task Learning Approach to Linear Multivariate Forecasting | [
"cs.LG",
"cs.AI"
] | Accurate forecasting of multivariate time series data is important in many engineering and scientific applications. Recent state-of-the-art works ignore the inter-relations between variates, using their model on each variate independently. This raises several research questions related to proper modeling of multivariat... | {
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2502.03576 | Clone-Resistant Weights in Metric Spaces: A Framework for Handling
Redundancy Bias | [
"cs.LG",
"cs.GT"
] | We are given a set of elements in a metric space. The distribution of the elements is arbitrary, possibly adversarial. Can we weigh the elements in a way that is resistant to such (adversarial) manipulations? This problem arises in various contexts. For instance, the elements could represent data points, requiring robu... | {
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2502.03587 | Stein Discrepancy for Unsupervised Domain Adaptation | [
"cs.LG",
"stat.ML"
] | Unsupervised domain adaptation (UDA) leverages information from a labeled source dataset to improve accuracy on a related but unlabeled target dataset. A common approach to UDA is aligning representations from the source and target domains by minimizing the distance between their data distributions. Previous methods ha... | {
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2502.03589 | HACK: Homomorphic Acceleration via Compression of the Key-Value Cache
for Disaggregated LLM Inference | [
"cs.DC",
"cs.LG"
] | Disaggregated Large Language Model (LLM) inference has gained popularity as it separates the computation-intensive prefill stage from the memory-intensive decode stage, avoiding the prefill-decode interference and improving resource utilization. However, transmitting Key-Value (KV) data between the two stages can be a ... | {
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2502.03591 | Clinically-Inspired Hierarchical Multi-Label Classification of Chest
X-rays with a Penalty-Based Loss Function | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In this work, we present a novel approach to multi-label chest X-ray (CXR) image classification that enhances clinical interpretability while maintaining a streamlined, single-model, single-run training pipeline. Leveraging the CheXpert dataset and VisualCheXbert-derived labels, we incorporate hierarchical label groupi... | {
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2502.03592 | Solar Panel Mapping via Oriented Object Detection | [
"cs.CV"
] | Maintaining the integrity of solar power plants is a vital component in dealing with the current climate crisis. This process begins with analysts creating a detailed map of a plant with the coordinates of every solar panel, making it possible to quickly locate and mitigate potential faulty solar panels. However, this ... | {
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2502.03603 | Dynamical Landauer principle: Thermodynamic criteria of transmitting
classical information | [
"quant-ph",
"cs.IT",
"math-ph",
"math.IT",
"math.MP"
] | Transmitting energy and information are two essential aspects of nature. Recent findings suggest they are closely related, while a quantitative equivalence between them is still unknown. This thus motivates us to ask: Can information transmission tasks equal certain energy transmission tasks? We answer this question po... | {
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2502.03604 | Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques
for Efficient LLM Fine-Tuning and Meta-Training | [
"cs.LG"
] | Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks using First-Order (FO) optimizers presents significant computational challenges. Parameter-Efficient Fine-Tuning(PEFT) methods have been proposed to address these challenges by freezing most model parameters and training only a small subset. Whil... | {
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2502.03607 | Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Recent advances in diffusion models hold significant potential in robotics, enabling the generation of diverse and smooth trajectories directly from raw representations of the environment. Despite this promise, applying diffusion models to motion planning remains challenging due to their difficulty in enforcing critica... | {
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2502.03608 | (GG) MoE vs. MLP on Tabular Data | [
"cs.LG",
"cs.AI"
] | In recent years, significant efforts have been directed toward adapting modern neural network architectures for tabular data. However, despite their larger number of parameters and longer training and inference times, these models often fail to consistently outperform vanilla multilayer perceptron (MLP) neural networks... | {
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2502.03609 | Multivariate Conformal Prediction using Optimal Transport | [
"stat.ML",
"cs.LG"
] | Conformal prediction (CP) quantifies the uncertainty of machine learning models by constructing sets of plausible outputs. These sets are constructed by leveraging a so-called conformity score, a quantity computed using the input point of interest, a prediction model, and past observations. CP sets are then obtained by... | {
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2502.03614 | A Novel Zero-Touch, Zero-Trust, AI/ML Enablement Framework for IoT
Network Security | [
"cs.LG",
"cs.AI",
"cs.CR"
] | The IoT facilitates a connected, intelligent, and sustainable society; therefore, it is imperative to protect the IoT ecosystem. The IoT-based 5G and 6G will leverage the use of machine learning and artificial intelligence (ML/AI) more to pave the way for autonomous and collaborative secure IoT networks. Zero-touch, ze... | {
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2502.03616 | Noncooperative Equilibrium Selection via a Trading-based Auction | [
"cs.GT",
"cs.MA"
] | Noncooperative multi-agent systems often face coordination challenges due to conflicting preferences among agents. In particular, agents acting in their own self-interest can settle on different equilibria, leading to suboptimal outcomes or even safety concerns. We propose an algorithm named trading auction for consens... | {
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2502.03618 | The Logical Implication Steering Method for Conditional Interventions on
Transformer Generation | [
"cs.LG"
] | The field of mechanistic interpretability in pre-trained transformer models has demonstrated substantial evidence supporting the ''linear representation hypothesis'', which is the idea that high level concepts are encoded as vectors in the space of activations of a model. Studies also show that model generation behavio... | {
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2502.03619 | Swarm Characteristic Classification using Robust Neural Networks with
Optimized Controllable Inputs | [
"cs.LG"
] | Having the ability to infer characteristics of autonomous agents would profoundly revolutionize defense, security, and civil applications. Our previous work was the first to demonstrate that supervised neural network time series classification (NN TSC) could rapidly predict the tactics of swarming autonomous agents in ... | {
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2502.03620 | Efficient Optimal PAC Learning | [
"cs.LG"
] | Recent advances in the binary classification setting by Hanneke [2016b] and Larsen [2023] have resulted in optimal PAC learners. These learners leverage, respectively, a clever deterministic subsampling scheme and the classic heuristic of bagging Breiman [1996]. Both optimal PAC learners use, as a subroutine, the natur... | {
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2502.03621 | DynVFX: Augmenting Real Videos with Dynamic Content | [
"cs.CV"
] | We present a method for augmenting real-world videos with newly generated dynamic content. Given an input video and a simple user-provided text instruction describing the desired content, our method synthesizes dynamic objects or complex scene effects that naturally interact with the existing scene over time. The posit... | {
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2502.03622 | AdaPhish: AI-Powered Adaptive Defense and Education Resource Against
Deceptive Emails | [
"cs.CR",
"cs.AI"
] | Phishing attacks remain a significant threat in the digital age, yet organizations lack effective methods to tackle phishing attacks without leaking sensitive information. Phish bowl initiatives are a vital part of cybersecurity efforts against these attacks. However, traditional phish bowls require manual anonymizatio... | {
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2502.03623 | Large Teams Overshadow Individual Recognition | [
"cs.SI"
] | In an ideal world, every scientist's contribution would be fully recognized, driving collective scientific progress. In reality, however, only a few scientists are recognized and remembered. Sociologist Robert Merton first described this disparity between contribution and recognition as the Matthew Effect, where citati... | {
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2502.03627 | Sorting the Babble in Babel: Assessing the Performance of Language
Detection Algorithms on the OpenAlex Database | [
"cs.CL"
] | This project aims to compare various language classification procedures, procedures combining various Python language detection algorithms and metadata-based corpora extracted from manually-annotated articles sampled from the OpenAlex database. Following an analysis of precision and recall performance for each algorith... | {
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} |
2502.03628 | The Hidden Life of Tokens: Reducing Hallucination of Large
Vision-Language Models via Visual Information Steering | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded contents. In this paper, we investigate the internal dynamics of hallucination by examining the tokens logits rankings throughout the generation pr... | {
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} |
2502.03629 | REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image
Transformations | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Existing image editing models struggle to meet real-world demands. Despite excelling in academic benchmarks, they have yet to be widely adopted for real user needs. Datasets that power these models use artificial edits, lacking the scale and ecological validity necessary to address the true diversity of user requests. ... | {
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} |
2502.03638 | SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models | [
"cond-mat.mtrl-sci",
"cs.LG"
] | Generating novel crystalline materials has potential to lead to advancements in fields such as electronics, energy storage, and catalysis. The defining characteristic of crystals is their symmetry, which plays a central role in determining their physical properties. However, existing crystal generation methods either f... | {
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} |
2502.03639 | Towards Physical Understanding in Video Generation: A 3D Point
Regularization Approach | [
"cs.CV"
] | We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D point trajectories and align them in pixel space. The resulting 3D-aware video dataset, PointVid, is then used to fine-tune a latent diffusion model, enabling it to t... | {
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} |
2502.03640 | Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal
Control | [
"cs.RO",
"cs.LG",
"cs.MA",
"math.OC"
] | Control policies that can achieve high task performance and satisfy safety constraints are desirable for any system, including multi-agent systems (MAS). One promising technique for ensuring the safety of MAS is distributed control barrier functions (CBF). However, it is difficult to design distributed CBF-based polici... | {
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} |
2502.03643 | Context-Preserving Gradient Modulation for Large Language Models: A
Novel Approach to Semantic Consistency in Long-Form Text Generation | [
"cs.CL"
] | Maintaining semantic consistency over extended text sequences remains a fundamental challenge in long-form text generation, where conventional training methodologies often struggle to prevent contextual drift and coherence degradation. A novel gradient modulation approach is introduced, designed to adjust parameter upd... | {
"Other": 0,
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} |
2502.03647 | Looking for the Inner Music: Probing LLMs' Understanding of Literary
Style | [
"cs.CL",
"cs.LG"
] | Recent work has demonstrated that language models can be trained to identify the author of much shorter literary passages than has been thought feasible for traditional stylometry. We replicate these results for authorship and extend them to a new dataset measuring novel genre. We find that LLMs are able to distinguish... | {
"Other": 0,
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} |
2502.03649 | All-in-One Image Compression and Restoration | [
"cs.CV"
] | Visual images corrupted by various types and levels of degradations are commonly encountered in practical image compression. However, most existing image compression methods are tailored for clean images, therefore struggling to achieve satisfying results on these images. Joint compression and restoration methods typic... | {
"Other": 0,
"cs.AI": 0,
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} |
2502.03650 | Rule-based Evolving Fuzzy System for Time Series Forecasting: New
Perspectives Based on Type-2 Fuzzy Sets Measures Approach | [
"stat.ML",
"cs.LG"
] | Real-world data contain uncertainty and variations that can be correlated to external variables, known as randomness. An alternative cause of randomness is chaos, which can be an important component of chaotic time series. One of the existing methods to deal with this type of data is the use of the evolving Fuzzy Syste... | {
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} |
2502.03652 | The Cost of Shuffling in Private Gradient Based Optimization | [
"cs.LG"
] | We consider the problem of differentially private (DP) convex empirical risk minimization (ERM). While the standard DP-SGD algorithm is theoretically well-established, practical implementations often rely on shuffled gradient methods that traverse the training data sequentially rather than sampling with replacement in ... | {
"Other": 0,
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} |
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