id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.07056 | Autonomous Deep Agent | [
"cs.AI",
"cs.LG"
] | This technical brief introduces Deep Agent, an advanced autonomous AI system designed to manage complex multi-phase tasks through a novel hierarchical task management architecture. The system's foundation is built on our Hierarchical Task DAG (HTDAG) framework, which dynamically decomposes high-level objectives into ma... | {
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2502.07057 | Tokenization Standards for Linguistic Integrity: Turkish as a Benchmark | [
"cs.CL"
] | Tokenization is a fundamental preprocessing step in NLP, directly impacting large language models' (LLMs) ability to capture syntactic, morphosyntactic, and semantic structures. This paper introduces a novel framework for systematically evaluating tokenization strategies, addressing challenges in morphologically rich a... | {
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2502.07058 | Using Contextually Aligned Online Reviews to Measure LLMs' Performance
Disparities Across Language Varieties | [
"cs.CL",
"cs.HC"
] | A language can have different varieties. These varieties can affect the performance of natural language processing (NLP) models, including large language models (LLMs), which are often trained on data from widely spoken varieties. This paper introduces a novel and cost-effective approach to benchmark model performance ... | {
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2502.07059 | Federated Continual Learning: Concepts, Challenges, and Solutions | [
"cs.LG",
"cs.AI"
] | Federated Continual Learning (FCL) has emerged as a robust solution for collaborative model training in dynamic environments, where data samples are continuously generated and distributed across multiple devices. This survey provides a comprehensive review of FCL, focusing on key challenges such as heterogeneity, model... | {
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2502.07064 | Contextual Thompson Sampling via Generation of Missing Data | [
"cs.LG",
"cs.AI",
"stat.ML"
] | We introduce a framework for Thompson sampling contextual bandit algorithms, in which the algorithm's ability to quantify uncertainty and make decisions depends on the quality of a generative model that is learned offline. Instead of viewing uncertainty in the environment as arising from unobservable latent parameters,... | {
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2502.07065 | Active Inference through Incentive Design in Markov Decision Processes | [
"eess.SY",
"cs.SY"
] | We present a method for active inference with partial observations in stochastic systems through incentive design, also known as the leader-follower game. Consider a leader agent who aims to infer a follower agent's type given a finite set of possible types. Different types of followers differ in either the dynamical m... | {
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2502.07067 | Repository-level Code Search with Neural Retrieval Methods | [
"cs.IR"
] | This paper presents a multi-stage reranking system for repository-level code search, which leverages the vastly available commit histories of large open-source repositories to aid in bug fixing. We define the task of repository-level code search as retrieving the set of files from the current state of a code repository... | {
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2502.07068 | Specializing Large Language Models to Simulate Survey Response
Distributions for Global Populations | [
"cs.CL"
] | Large-scale surveys are essential tools for informing social science research and policy, but running surveys is costly and time-intensive. If we could accurately simulate group-level survey results, this would therefore be very valuable to social science research. Prior work has explored the use of large language mode... | {
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2502.07069 | Semantics-Aware Updates from Remote IoT Devices to Interconnected LEO
Satellites | [
"cs.NI",
"cs.IT",
"math.IT"
] | Providing timely and informative data in Integrated Terrestrial and Non-Terrestrial Networks (T-NTNs) is critical as data volume continues to grow while the resources available on devices remain limited. To address this, we adopt a semantics-aware approach to optimize the Version Age of Information (VAoI) in a status u... | {
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2502.07070 | Comprehensive Analysis of Thermal Dissipation in Lithium-Ion Battery
Packs | [
"eess.SY",
"cs.SY",
"hep-ph"
] | Effective thermal management is critical for lithium-ion battery packs' safe and efficient operations, particularly in applications such as drones, where compact designs and varying airflow conditions present unique challenges. This study investigates the thermal performance of a 16-cell lithium-ion battery pack by opt... | {
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2502.07071 | TRADES: Generating Realistic Market Simulations with Diffusion Models | [
"q-fin.TR",
"cs.AI",
"cs.LG",
"q-fin.CP"
] | Financial markets are complex systems characterized by high statistical noise, nonlinearity, and constant evolution. Thus, modeling them is extremely hard. We address the task of generating realistic and responsive Limit Order Book (LOB) market simulations, which are fundamental for calibrating and testing trading stra... | {
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2502.07072 | IRepair: An Intent-Aware Approach to Repair Data-Driven Errors in Large
Language Models | [
"cs.CL",
"cs.AI",
"cs.SE"
] | Not a day goes by without hearing about the impressive feats of large language models (LLMs), and equally, not a day passes without hearing about their challenges. LLMs are notoriously vulnerable to biases in their dataset, leading to issues such as toxicity. While domain-adaptive training has been employed to mitigate... | {
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2502.07076 | On the use of neural networks for the structural characterization of
polymeric porous materials | [
"cond-mat.soft",
"cond-mat.mtrl-sci",
"cs.CV",
"eess.IV"
] | The structural characterization is an essential task in the study of porous materials. To achieve reliable results, it requires to evaluate images with hundreds of pores. Current methods require large time amounts and are subjected to human errors and subjectivity. A completely automatic tool would not only speed up th... | {
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2502.07077 | Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language
Models | [
"cs.CL",
"cs.CY",
"cs.HC"
] | The tendency of users to anthropomorphise large language models (LLMs) is of growing interest to AI developers, researchers, and policy-makers. Here, we present a novel method for empirically evaluating anthropomorphic LLM behaviours in realistic and varied settings. Going beyond single-turn static benchmarks, we contr... | {
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2502.07081 | Fast Clustering of Categorical Big Data | [
"cs.LG",
"cs.DB"
] | The K-Modes algorithm, developed for clustering categorical data, is of high algorithmic simplicity but suffers from unreliable performances in clustering quality and clustering efficiency, both heavily influenced by the choice of initial cluster centers. In this paper, we investigate Bisecting K-Modes (BK-Modes), a su... | {
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2502.07082 | "Once Upon a Time..." Literary Narrative Connectedness Progresses with
Grade Level: Potential Impact on Reading Fluency and Literacy Skills | [
"cs.CL"
] | Selecting an appropriate book is crucial for fostering reading habits in children. While children exhibit varying levels of complexity when generating oral narratives, the question arises: do children's books also differ in narrative complexity? This study explores the narrative dynamics of literary texts used in schoo... | {
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2502.07085 | Real-time Optimization for Wind-to-H2 Driven Critical Infrastructures
Based on Active Constraints Identification and Integer Variables Prediction | [
"eess.SY",
"cs.SY"
] | This paper proposes a concept of wind-to-hydrogen-driven critical infrastructure (W2H-CI) as an engineering solution for decarbonizing the power generation sector where it utilizes wind power to produce hydrogen through electrolysis and combines it with the carbon captured from fossil fuel power plants. First, a convex... | {
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2502.07087 | Evaluating the Systematic Reasoning Abilities of Large Language Models
through Graph Coloring | [
"cs.LG"
] | Contemporary large language models are powerful problem-solving tools, but they exhibit weaknesses in their reasoning abilities which ongoing research seeks to mitigate. We investigate graph coloring as a means of evaluating an LLM's capacities for systematic step-by-step reasoning and possibility space exploration, as... | {
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2502.07088 | Kernels of Selfhood: GPT-4o shows humanlike patterns of cognitive
consistency moderated by free choice | [
"cs.CY",
"cs.AI",
"cs.CL",
"cs.HC",
"cs.LG"
] | Large Language Models (LLMs) show emergent patterns that mimic human cognition. We explore whether they also mirror other, less deliberative human psychological processes. Drawing upon classical theories of cognitive consistency, two preregistered studies tested whether GPT-4o changed its attitudes toward Vladimir Puti... | {
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2502.07090 | Generative Distribution Prediction: A Unified Approach to Multimodal
Learning | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Accurate prediction with multimodal data-encompassing tabular, textual, and visual inputs or outputs-is fundamental to advancing analytics in diverse application domains. Traditional approaches often struggle to integrate heterogeneous data types while maintaining high predictive accuracy. We introduce Generative Distr... | {
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2502.07096 | Lotus: Creating Short Videos From Long Videos With Abstractive and
Extractive Summarization | [
"cs.HC",
"cs.CV"
] | Short-form videos are popular on platforms like TikTok and Instagram as they quickly capture viewers' attention. Many creators repurpose their long-form videos to produce short-form videos, but creators report that planning, extracting, and arranging clips from long-form videos is challenging. Currently, creators make ... | {
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2502.07101 | SMAB: MAB based word Sensitivity Estimation Framework and its
Applications in Adversarial Text Generation | [
"cs.CL"
] | To understand the complexity of sequence classification tasks, Hahn et al. (2021) proposed sensitivity as the number of disjoint subsets of the input sequence that can each be individually changed to change the output. Though effective, calculating sensitivity at scale using this framework is costly because of exponent... | {
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2502.07102 | Optimal Steady-State Secondary Control of MT-HVdc Grids with Reduced
Communications | [
"eess.SY",
"cs.SY",
"math.OC"
] | In this paper, we propose a centralized secondary control for the real-time steady-state optimization of multi-terminal HVdc grids under voltage and current limits. First, we present the dynamic models of the grid components, including the modular multilevel converter (MMC) stations and their different control layers. ... | {
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2502.07107 | A Framework for Supervised and Unsupervised Segmentation and
Classification of Materials Microstructure Images | [
"stat.AP",
"cs.CV",
"stat.ML"
] | Microstructure of materials is often characterized through image analysis to understand processing-structure-properties linkages. We propose a largely automated framework that integrates unsupervised and supervised learning methods to classify micrographs according to microstructure phase/class and, for multiphase micr... | {
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2502.07109 | Game of Coding With an Unknown Adversary | [
"cs.IT",
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"math.IT"
] | Motivated by emerging decentralized applications, the \emph{game of coding} framework has been recently introduced to address scenarios where the adversary's control over coded symbols surpasses the fundamental limits of traditional coding theory. Still, the reward mechanism available in decentralized systems, motivate... | {
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2502.07111 | Likelihood-Free Estimation for Spatiotemporal Hawkes processes with
missing data and application to predictive policing | [
"cs.LG",
"stat.AP",
"stat.ME"
] | With the growing use of AI technology, many police departments use forecasting software to predict probable crime hotspots and allocate patrolling resources effectively for crime prevention. The clustered nature of crime data makes self-exciting Hawkes processes a popular modeling choice. However, one significant chall... | {
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2502.07114 | Online Covariance Matrix Estimation in Sketched Newton Methods | [
"stat.ML",
"cs.LG",
"cs.NA",
"math.NA",
"math.OC",
"stat.CO"
] | Given the ubiquity of streaming data, online algorithms have been widely used for parameter estimation, with second-order methods particularly standing out for their efficiency and robustness. In this paper, we study an online sketched Newton method that leverages a randomized sketching technique to perform an approxim... | {
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2502.07115 | Online Scheduling for LLM Inference with KV Cache Constraints | [
"cs.LG",
"cs.AI",
"math.OC"
] | Large Language Model (LLM) inference, where a trained model generates text one word at a time in response to user prompts, is a computationally intensive process requiring efficient scheduling to optimize latency and resource utilization. A key challenge in LLM inference is the management of the Key-Value (KV) cache, w... | {
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2502.07117 | Choroidal image analysis for OCT image sequences with applications in
systemic health | [
"eess.IV",
"cs.CV",
"cs.LG",
"cs.MS"
] | The choroid, a highly vascular layer behind the retina, is an extension of the central nervous system and has parallels with the renal cortex, with blood flow far exceeding that of the brain and kidney. Thus, there has been growing interest of choroidal blood flow reflecting physiological status of systemic disease. Op... | {
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2502.07119 | SAFE: Self-Supervised Anomaly Detection Framework for Intrusion
Detection | [
"cs.CR",
"cs.LG"
] | The proliferation of IoT devices has significantly increased network vulnerabilities, creating an urgent need for effective Intrusion Detection Systems (IDS). Machine Learning-based IDS (ML-IDS) offer advanced detection capabilities but rely on labeled attack data, which limits their ability to identify unknown threats... | {
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2502.07120 | Is Long Range Sequential Modeling Necessary For Colorectal Tumor
Segmentation? | [
"cs.CV"
] | Segmentation of colorectal cancer (CRC) tumors in 3D medical imaging is both complex and clinically critical, providing vital support for effective radiation therapy planning and survival outcome assessment. Recently, 3D volumetric segmentation architectures incorporating long-range sequence modeling mechanisms, such a... | {
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2502.07124 | Structural Reformation of Large Language Model Neuron Encapsulation for
Divergent Information Aggregation | [
"cs.CL"
] | Structured neuron encapsulation introduces a modular framework that enables more effective aggregation and specialization of information within deep learning architectures. A model modified through this framework demonstrated improved perplexity scores, greater lexical variability, and enhanced consistency in logical r... | {
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2502.07128 | Cardiverse: Harnessing LLMs for Novel Card Game Prototyping | [
"cs.CL",
"cs.AI",
"cs.MM"
] | The prototyping of computer games, particularly card games, requires extensive human effort in creative ideation and gameplay evaluation. Recent advances in Large Language Models (LLMs) offer opportunities to automate and streamline these processes. However, it remains challenging for LLMs to design novel game mechanic... | {
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2502.07129 | Fourier-enhanced Neural Networks For Systems Biology Applications | [
"cs.LG",
"q-bio.QM"
] | In the field of systems biology, differential equations are commonly used to model biological systems, but solving them for large-scale and complex systems can be computationally expensive. Recently, the integration of machine learning and mathematical modeling has offered new opportunities for scientific discoveries i... | {
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2502.07130 | Unconstrained Body Recognition at Altitude and Range: Comparing Four
Approaches | [
"cs.CV",
"cs.AI",
"cs.LG"
] | This study presents an investigation of four distinct approaches to long-term person identification using body shape. Unlike short-term re-identification systems that rely on temporary features (e.g., clothing), we focus on learning persistent body shape characteristics that remain stable over time. We introduce a body... | {
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2502.07131 | TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model
Bring? -- A Case Study on Korea Financial Texts | [
"cs.CL",
"q-fin.CP"
] | Domain specificity of embedding models is critical for effective performance. However, existing benchmarks, such as FinMTEB, are primarily designed for high-resource languages, leaving low-resource settings, such as Korean, under-explored. Directly translating established English benchmarks often fails to capture the l... | {
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2502.07132 | Interactive Data Harmonization with LLM Agents | [
"cs.AI",
"cs.DB"
] | Data harmonization is an essential task that entails integrating datasets from diverse sources. Despite years of research in this area, it remains a time-consuming and challenging task due to schema mismatches, varying terminologies, and differences in data collection methodologies. This paper presents the case for age... | {
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2502.07133 | Cross-platform Learning-based Fault Tolerant Surfacing Controller for
Underwater Robots | [
"cs.RO"
] | In this paper, we propose a novel cross-platform fault-tolerant surfacing controller for underwater robots, based on reinforcement learning (RL). Unlike conventional approaches, which require explicit identification of malfunctioning actuators, our method allows the robot to surface using only the remaining operational... | {
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2502.07135 | One-Shot Learning for k-SAT | [
"cs.DS",
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | Consider a $k$-SAT formula $\Phi$ where every variable appears at most $d$ times, and let $\sigma$ be a satisfying assignment of $\Phi$ sampled proportionally to $e^{\beta m(\sigma)}$ where $m(\sigma)$ is the number of variables set to true and $\beta$ is a real parameter. Given $\Phi$ and $\sigma$, can we learn the va... | {
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2502.07136 | A Safe Hybrid Control Framework for Car-like Robot with Guaranteed
Global Path-Invariance using a Control Barrier Function | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This work proposes a hybrid framework for car-like robots with obstacle avoidance, global convergence, and safety, where safety is interpreted as path invariance, namely, once the robot converges to the path, it never leaves the path. Given a priori obstacle-free feasible path where obstacles can be around the path, th... | {
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2502.07138 | Towards a Robust Framework for Multimodal Hate Detection: A Study on
Video vs. Image-based Content | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Social media platforms enable the propagation of hateful content across different modalities such as textual, auditory, and visual, necessitating effective detection methods. While recent approaches have shown promise in handling individual modalities, their effectiveness across different modality combinations remains ... | {
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2502.07139 | Language-TPP: Integrating Temporal Point Processes with Language Models
for Event Analysis | [
"cs.CL",
"cs.LG"
] | Temporal Point Processes (TPPs) have been widely used for event sequence modeling, but they often struggle to incorporate rich textual event descriptions effectively. Conversely, while Large Language Models (LLMs) have been shown remarkable capabilities in processing textual data, they lack mechanisms for handling temp... | {
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2502.07140 | Few-Shot Multi-Human Neural Rendering Using Geometry Constraints | [
"cs.CV",
"cs.AI",
"cs.GR"
] | We present a method for recovering the shape and radiance of a scene consisting of multiple people given solely a few images. Multi-human scenes are complex due to additional occlusion and clutter. For single-human settings, existing approaches using implicit neural representations have achieved impressive results that... | {
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2502.07141 | Small steps no more: Global convergence of stochastic gradient bandits
for arbitrary learning rates | [
"cs.LG"
] | We provide a new understanding of the stochastic gradient bandit algorithm by showing that it converges to a globally optimal policy almost surely using \emph{any} constant learning rate. This result demonstrates that the stochastic gradient algorithm continues to balance exploration and exploitation appropriately even... | {
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2502.07143 | Ask Patients with Patience: Enabling LLMs for Human-Centric Medical
Dialogue with Grounded Reasoning | [
"cs.CL"
] | Accurate and efficient diagnosis in online medical consultations remains a challenge for current large language models. These models often rely on single-turn interactions and lack the ability to refine their predictions through follow-up questions. Additionally, their responses frequently contain complex medical termi... | {
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2502.07145 | Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of
Statistical Shape Model of Anatomies from Surface Meshes | [
"cs.CV"
] | Anatomy evaluation is crucial for understanding the physiological state, diagnosing abnormalities, and guiding medical interventions. Statistical shape modeling (SSM) is vital in this process. By enabling the extraction of quantitative morphological shape descriptors from MRI and CT scans, SSM provides comprehensive de... | {
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2502.07148 | Expressing entropy and cross-entropy in expansions of common meadows | [
"cs.IT",
"math.IT"
] | A common meadow is an enrichment of a field with a partial division operation that is made total by assuming that division by zero takes the a default value, a special element $\bot$ adjoined to the field. To a common meadow of real numbers we add a binary logarithm $\log_2(-)$, which we also assume to be total with $\... | {
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2502.07151 | Conditional Distribution Quantization in Machine Learning | [
"cs.LG"
] | Conditional expectation \mathbb{E}(Y \mid X) often fails to capture the complexity of multimodal conditional distributions \mathcal{L}(Y \mid X). To address this, we propose using n-point conditional quantizations--functional mappings of X that are learnable via gradient descent--to approximate \mathcal{L}(Y \mid X). T... | {
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2502.07153 | Feature Importance Depends on Properties of the Data: Towards Choosing
the Correct Explanations for Your Data and Decision Trees based Models | [
"cs.LG",
"cs.AI"
] | In order to ensure the reliability of the explanations of machine learning models, it is crucial to establish their advantages and limits and in which case each of these methods outperform. However, the current understanding of when and how each method of explanation can be used is insufficient. To fill this gap, we pe... | {
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2502.07154 | Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting
Confidence Improves Mathematical Reasoning | [
"cs.LG",
"cs.AI"
] | Recent progress in large language models (LLMs) highlights the power of scaling test-time compute to achieve strong performance on complex tasks, such as mathematical reasoning and code generation. This raises a critical question: how should model training be modified to optimize performance under a subsequent test-tim... | {
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2502.07156 | Explaining 3D Computed Tomography Classifiers with Counterfactuals | [
"cs.CV",
"cs.AI"
] | Counterfactual explanations in medical imaging are critical for understanding the predictions made by deep learning models. We extend the Latent Shift counterfactual generation method from 2D applications to 3D computed tomography (CT) scans. We address the challenges associated with 3D data, such as limited training s... | {
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2502.07158 | Early Risk Prediction of Pediatric Cardiac Arrest from Electronic Health
Records via Multimodal Fused Transformer | [
"cs.LG",
"cs.AI"
] | Early prediction of pediatric cardiac arrest (CA) is critical for timely intervention in high-risk intensive care settings. We introduce PedCA-FT, a novel transformer-based framework that fuses tabular view of EHR with the derived textual view of EHR to fully unleash the interactions of high-dimensional risk factors an... | {
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2502.07160 | HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates | [
"cs.CV",
"cs.MM"
] | Image compression under ultra-low bitrates remains challenging for both conventional learned image compression (LIC) and generative vector-quantized (VQ) modeling. Conventional LIC suffers from severe artifacts due to heavy quantization, while generative VQ modeling gives poor fidelity due to the mismatch between learn... | {
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2502.07161 | A Survey on Mamba Architecture for Vision Applications | [
"cs.CV",
"cs.AI"
] | Transformers have become foundational for visual tasks such as object detection, semantic segmentation, and video understanding, but their quadratic complexity in attention mechanisms presents scalability challenges. To address these limitations, the Mamba architecture utilizes state-space models (SSMs) for linear scal... | {
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2502.07164 | Does Training on Synthetic Data Make Models Less Robust? | [
"cs.CL",
"cs.AI",
"cs.LG"
] | An increasingly common practice is to train large language models (LLMs) using synthetic data. Often this synthetic data is produced by the same or similar LLMs as those it is being used to train. This raises the question of whether the synthetic data might in fact exacerbate certain "blindspots" by reinforcing heurist... | {
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2502.07165 | Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent
Prompting Strategy for Text Classification | [
"cs.CL",
"cs.AI"
] | We present PRINCIPLE-BASED PROMPTING, a simple but effective multi-agent prompting strategy for text classification. It first asks multiple LLM agents to independently generate candidate principles based on analysis of demonstration samples with or without labels, consolidates them into final principles via a finalizer... | {
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2502.07166 | Bayesian Optimization for Building Social-Influence-Free Consensus | [
"cs.MA",
"cs.GT",
"cs.LG",
"stat.ML"
] | We introduce Social Bayesian Optimization (SBO), a vote-efficient algorithm for consensus-building in collective decision-making. In contrast to single-agent scenarios, collective decision-making encompasses group dynamics that may distort agents' preference feedback, thereby impeding their capacity to achieve a social... | {
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2502.07169 | Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow
Technology | [
"physics.comp-ph",
"cs.LG",
"physics.geo-ph"
] | Geological Carbon Storage (GCS) is a key technology for achieving global climate goals by capturing and storing CO2 in deep geological formations. Its effectiveness and safety rely on accurate monitoring of subsurface CO2 migration using advanced time-lapse seismic imaging. A Digital Shadow framework integrates field d... | {
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2502.07170 | Effcient classical error correction for parity encoded spin systems | [
"quant-ph",
"cs.IT",
"math.IT"
] | Fast solvers for combinatorial optimization problems (COPs) have attracted engineering interest in various industrial and social applications. Quantum annealing (QA) has emerged as a promising candidate and significant efforts have been dedicated to its development. Since COP is encoded in the Ising interaction between... | {
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2502.07171 | Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With
Augmented Rock Physics Modeling | [
"physics.comp-ph",
"cs.LG",
"physics.geo-ph"
] | To meet climate targets, the IPCC underscores the necessity of technologies capable of removing gigatonnes of CO2 annually, with Geological Carbon Storage (GCS) playing a central role. GCS involves capturing CO2 and injecting it into deep geological formations for long-term storage, requiring precise monitoring to ensu... | {
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2502.07172 | SemiHMER: Semi-supervised Handwritten Mathematical Expression
Recognition using pseudo-labels | [
"cs.CV",
"cs.AI"
] | In this paper, we study semi-supervised Handwritten Mathematical Expression Recognition (HMER) via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization framework, termed SemiHMER, which introduces dual-branch semi-supervised learning. Specifically, we enforce consistency b... | {
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2502.07175 | Foreign-Object Detection in High-Voltage Transmission Line Based on
Improved YOLOv8m | [
"cs.CV",
"cs.AI"
] | The safe operation of high-voltage transmission lines ensures the power grid's security. Various foreign objects attached to the transmission lines, such as balloons, kites and nesting birds, can significantly affect the safe and stable operation of high-voltage transmission lines. With the advancement of computer visi... | {
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2502.07176 | MatrixKAN: Parallelized Kolmogorov-Arnold Network | [
"cs.LG"
] | Kolmogorov-Arnold Networks (KAN) are a new class of neural network architecture representing a promising alternative to the Multilayer Perceptron (MLP), demonstrating improved expressiveness and interpretability. However, KANs suffer from slow training and inference speeds relative to MLPs due in part to the recursive ... | {
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2502.07178 | Online Aggregation of Trajectory Predictors | [
"cs.RO"
] | Trajectory prediction, the task of forecasting future agent behavior from past data, is central to safe and efficient autonomous driving. A diverse set of methods (e.g., rule-based or learned with different architectures and datasets) have been proposed, yet it is often the case that the performance of these methods is... | {
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2502.07179 | Improved YOLOv7 model for insulator defect detection | [
"cs.CV",
"cs.AI"
] | Insulators are crucial insulation components and structural supports in power grids, playing a vital role in the transmission lines. Due to temperature fluctuations, internal stress, or damage from hail, insulators are prone to injury. Automatic detection of damaged insulators faces challenges such as diverse types, sm... | {
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2502.07181 | Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using
Deep Learning with Visual Representations | [
"cs.LG",
"cs.CV"
] | This research addresses the challenge of limited data in tabular data classification, particularly prevalent in domains with constraints like healthcare. We propose Tab2Visual, a novel approach that transforms heterogeneous tabular data into visual representations, enabling the application of powerful deep learning mod... | {
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2502.07182 | Morphing Wing Designs in Commercial Aviation | [
"eess.SY",
"cs.SY"
] | With increasing demands for fuel efficiency and operational adaptability in commercial aviation}, this paper provides a systematic review and classification of morphing wing technologies, analyzing their aerodynamic performance characteristics and atmospheric condition adaptability. We first develop a comprehensive cla... | {
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2502.07183 | Space-Aware Instruction Tuning: Dataset and Benchmark for Guide Dog
Robots Assisting the Visually Impaired | [
"cs.RO",
"cs.CV"
] | Guide dog robots offer promising solutions to enhance mobility and safety for visually impaired individuals, addressing the limitations of traditional guide dogs, particularly in perceptual intelligence and communication. With the emergence of Vision-Language Models (VLMs), robots are now capable of generating natural ... | {
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2502.07184 | Refine Knowledge of Large Language Models via Adaptive Contrastive
Learning | [
"cs.CL",
"cs.AI"
] | How to alleviate the hallucinations of Large Language Models (LLMs) has always been the fundamental goal pursued by the LLMs research community. Looking through numerous hallucination-related studies, a mainstream category of methods is to reduce hallucinations by optimizing the knowledge representation of LLMs to chan... | {
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2502.07186 | Perceived Confidence Scoring for Data Annotation with Zero-Shot LLMs | [
"cs.CL",
"cs.LG"
] | Zero-shot LLMs are now also used for textual classification tasks, e.g., sentiment/emotion detection of a given input as a sentence/article. However, their performance can be suboptimal in such data annotation tasks. We introduce a novel technique Perceived Confidence Scoring (PCS) that evaluates LLM's confidence for i... | {
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2502.07187 | Local Regularizers Are Not Transductive Learners | [
"cs.LG",
"stat.ML"
] | We partly resolve an open question raised by Asilis et al. (COLT 2024): whether the algorithmic template of local regularization -- an intriguing generalization of explicit regularization, a.k.a. structural risk minimization -- suffices to learn all learnable multiclass problems. Specifically, we provide a negative ans... | {
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2502.07188 | A Large-Scale Benchmark for Vietnamese Sentence Paraphrases | [
"cs.CL"
] | This paper presents ViSP, a high-quality Vietnamese dataset for sentence paraphrasing, consisting of 1.2M original-paraphrase pairs collected from various domains. The dataset was constructed using a hybrid approach that combines automatic paraphrase generation with manual evaluation to ensure high quality. We conducte... | {
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2502.07189 | Exploring Neural Network Pruning with Screening Methods | [
"cs.LG",
"stat.ML"
] | Deep neural networks (DNNs) such as convolutional neural networks (CNNs) for visual tasks, recurrent neural networks (RNNs) for sequence data, and transformer models for rich linguistic or multimodal tasks, achieved unprecedented performance on a wide range of tasks. The impressive performance of modern DNNs is partial... | {
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2502.07190 | Understanding LLMs' Fluid Intelligence Deficiency: An Analysis of the
ARC Task | [
"cs.AI"
] | While LLMs have exhibited strong performance on various NLP tasks, it is noteworthy that most of these tasks rely on utilizing the vast amount of knowledge encoded in LLMs' parameters, rather than solving new problems without prior knowledge. In cognitive research, the latter ability is referred to as fluid intelligenc... | {
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2502.07191 | Bag of Tricks for Inference-time Computation of LLM Reasoning | [
"cs.AI"
] | With the advancement of large language models (LLMs), solving complex reasoning tasks has gained increasing attention. Inference-time computation methods (e.g., Best-of-N, beam search, et al.) are particularly valuable as they can enhance reasoning performance without modifying model parameters or requiring additional ... | {
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2502.07192 | OscNet: Machine Learning on CMOS Oscillator Networks | [
"cs.CV"
] | Machine learning and AI have achieved remarkable advancements but at the cost of significant computational resources and energy consumption. This has created an urgent need for a novel, energy-efficient computational fabric to replace the current computing pipeline. Recently, a promising approach has emerged by mimicki... | {
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2502.07193 | Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits | [
"cs.LG",
"stat.ML"
] | Reinforcement Learning from Human Feedback (RLHF) is a widely used approach for aligning Large Language Models (LLMs) with human preferences. While recent advancements have provided valuable insights into various stages and settings of RLHF, a comprehensive theoretical understanding of the entire RLHF pipeline remains ... | {
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2502.07194 | Dense Object Detection Based on De-homogenized Queries | [
"cs.CV",
"cs.AI"
] | Dense object detection is widely used in automatic driving, video surveillance, and other fields. This paper focuses on the challenging task of dense object detection. Currently, detection methods based on greedy algorithms, such as non-maximum suppression (NMS), often produce many repetitive predictions or missed dete... | {
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2502.07196 | Parameter Optimization of Optical Six-Axis Force/Torque Sensor for
Legged Robots | [
"cs.RO"
] | This paper introduces a novel six-axis force/torque sensor tailored for compact and lightweight legged robots. Unlike traditional strain gauge-based sensors, the proposed non-contact design employs photocouplers, enhancing resistance to physical impacts and reducing damage risk. This approach simplifies manufacturing, ... | {
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2502.07199 | Fixed-Confidence Best Arm Identification with Decreasing Variance | [
"cs.LG",
"cs.IT",
"math.IT",
"math.ST",
"stat.ML",
"stat.TH"
] | We focus on the problem of best-arm identification in a stochastic multi-arm bandit with temporally decreasing variances for the arms' rewards. We model arm rewards as Gaussian random variables with fixed means and variances that decrease with time. The cost incurred by the learner is modeled as a weighted sum of the t... | {
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2502.07200 | Color-Quality Invariance for Robust Medical Image Segmentation | [
"eess.IV",
"cs.CV"
] | Single-source domain generalization (SDG) in medical image segmentation remains a significant challenge, particularly for images with varying color distributions and qualities. Previous approaches often struggle when models trained on high-quality images fail to generalize to low-quality test images due to these color ... | {
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2502.07202 | Monte Carlo Tree Diffusion for System 2 Planning | [
"cs.AI",
"cs.LG"
] | Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)-whose performance naturally improves with additional test-time computation (TTC), standard diffusion-based planners offer only limited avenues for TTC scalability. In this paper, we introduce Monte Car... | {
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2502.07203 | Playmate: Flexible Control of Portrait Animation via 3D-Implicit Space
Guided Diffusion | [
"cs.CV"
] | Recent diffusion-based talking face generation models have demonstrated impressive potential in synthesizing videos that accurately match a speech audio clip with a given reference identity. However, existing approaches still encounter significant challenges due to uncontrollable factors, such as inaccurate lip-sync, i... | {
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2502.07205 | VINP: Variational Bayesian Inference with Neural Speech Prior for Joint
ASR-Effective Speech Dereverberation and Blind RIR Identification | [
"eess.AS",
"cs.AI",
"cs.LG"
] | Reverberant speech, denoting the speech signal degraded by the process of reverberation, contains crucial knowledge of both anechoic source speech and room impulse response (RIR). This work proposes a variational Bayesian inference (VBI) framework with neural speech prior (VINP) for joint speech dereverberation and bli... | {
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2502.07207 | A Study on the Importance of Features in Detecting Advanced Persistent
Threats Using Machine Learning | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Advanced Persistent Threats (APTs) pose a significant security risk to organizations and industries. These attacks often lead to severe data breaches and compromise the system for a long time. Mitigating these sophisticated attacks is highly challenging due to the stealthy and persistent nature of APTs. Machine learnin... | {
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2502.07209 | Enhancing Physics-Informed Neural Networks Through Feature Engineering | [
"cs.LG"
] | Physics-Informed Neural Networks (PINNs) seek to solve partial differential equations (PDEs) with deep learning. Mainstream approaches that deploy fully-connected multi-layer deep learning architectures require prolonged training to achieve even moderate accuracy, while recent work on feature engineering allows higher ... | {
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2502.07211 | Improve the Training Efficiency of DRL for Wireless Communication
Resource Allocation: The Role of Generative Diffusion Models | [
"cs.LG"
] | Dynamic resource allocation in mobile wireless networks involves complex, time-varying optimization problems, motivating the adoption of deep reinforcement learning (DRL). However, most existing works rely on pre-trained policies, overlooking dynamic environmental changes that rapidly invalidate the policies. Periodic ... | {
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2502.07213 | Evaluation for Regression Analyses on Evolving Data Streams | [
"cs.LG",
"cs.AI"
] | The paper explores the challenges of regression analysis in evolving data streams, an area that remains relatively underexplored compared to classification. We propose a standardized evaluation process for regression and prediction interval tasks in streaming contexts. Additionally, we introduce an innovative drift sim... | {
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2502.07214 | Pareto Optimal Algorithmic Recourse in Multi-cost Function | [
"cs.LG",
"cs.AI",
"cs.DS"
] | In decision-making systems, algorithmic recourse aims to identify minimal-cost actions to alter an individual features, thereby obtaining a desired outcome. This empowers individuals to understand, question, or alter decisions that negatively affect them. However, due to the variety and sensitivity of system environmen... | {
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2502.07215 | PDV: Prompt Directional Vectors for Zero-shot Composed Image Retrieval | [
"cs.CV"
] | Zero-shot composed image retrieval (ZS-CIR) enables image search using a reference image and text prompt without requiring specialized text-image composition networks trained on large-scale paired data. However, current ZS-CIR approaches face three critical limitations in their reliance on composed text embeddings: sta... | {
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2502.07216 | SparseFormer: Detecting Objects in HRW Shots via Sparse Vision
Transformer | [
"cs.CV",
"cs.AI"
] | Recent years have seen an increase in the use of gigapixel-level image and video capture systems and benchmarks with high-resolution wide (HRW) shots. However, unlike close-up shots in the MS COCO dataset, the higher resolution and wider field of view raise unique challenges, such as extreme sparsity and huge scale cha... | {
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2502.07218 | LUNAR: LLM Unlearning via Neural Activation Redirection | [
"cs.LG",
"cs.AI"
] | Large Language Models (LLMs) benefit from training on ever larger amounts of textual data, but as a result, they increasingly incur the risk of leaking private information. The ability to selectively remove knowledge from LLMs is, therefore, a highly desirable capability. In this paper, we propose LUNAR, a novel unlear... | {
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} |
2502.07219 | DOGR: Leveraging Document-Oriented Contrastive Learning in Generative
Retrieval | [
"cs.IR"
] | Generative retrieval constitutes an innovative approach in information retrieval, leveraging generative language models (LM) to generate a ranked list of document identifiers (docid) for a given query. It simplifies the retrieval pipeline by replacing the large external index with model parameters. However, existing wo... | {
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} |
2502.07221 | MLLM4PUE: Toward Universal Embeddings in Computational Pathology through
Multimodal LLMs | [
"cs.CV"
] | Pathology plays a critical role in diagnosing a wide range of diseases, yet existing approaches often rely heavily on task-specific models trained on extensive, well-labeled datasets. These methods face sustainability challenges due to the diversity of pathologies and the labor-intensive nature of data collection. To a... | {
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} |
2502.07222 | A Memory Efficient Randomized Subspace Optimization Method for Training
Large Language Models | [
"cs.LG"
] | The memory challenges associated with training Large Language Models (LLMs) have become a critical concern, particularly when using the Adam optimizer. To address this issue, numerous memory-efficient techniques have been proposed, with GaLore standing out as a notable example designed to reduce the memory footprint of... | {
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} |
2502.07223 | Graph RAG-Tool Fusion | [
"cs.CL"
] | Recent developments in retrieval-augmented generation (RAG) for selecting relevant tools from a tool knowledge base enable LLM agents to scale their complex tool calling capabilities to hundreds or thousands of external tools, APIs, or agents-as-tools. However, traditional RAG-based tool retrieval fails to capture stru... | {
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} |
2502.07225 | CAT: Contrastive Adversarial Training for Evaluating the Robustness of
Protective Perturbations in Latent Diffusion Models | [
"cs.CV"
] | Latent diffusion models have recently demonstrated superior capabilities in many downstream image synthesis tasks. However, customization of latent diffusion models using unauthorized data can severely compromise the privacy and intellectual property rights of data owners. Adversarial examples as protective perturbatio... | {
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} |
2502.07226 | Non-Iterative Coordination of Interconnected Power Grids via
Dimension-Decomposition-Based Flexibility Aggregation | [
"eess.SY",
"cs.SY"
] | The bulk power grid is divided into regional grids interconnected with multiple tie-lines for efficient operation. Since interconnected power grids are operated by different control centers, it is a challenging task to realize coordinated dispatch of multiple regional grids. A viable solution is to compute a flexibilit... | {
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} |
2502.07230 | Physics-Informed Recurrent Network for Gas Pipeline Network Parameters
Identification | [
"eess.SY",
"cs.SY"
] | As a part of the integrated energy system (IES), gas pipeline networks can provide additional flexibility to power systems through coordinated optimal dispatch. An accurate pipeline network model is critical for the optimal operation and control of IESs. However, inaccuracies or unavailability of accurate pipeline para... | {
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} |
2502.07232 | Simplifying Adversarially Robust PAC Learning with Tolerance | [
"cs.LG"
] | Adversarially robust PAC learning has proved to be challenging, with the currently best known learners [Montasser et al., 2021a] relying on improper methods based on intricate compression schemes, resulting in sample complexity exponential in the VC-dimension. A series of follow up work considered a slightly relaxed ve... | {
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} |
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