id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.00064 | Evaluating Large Language Models in Vulnerability Detection Under
Variable Context Windows | [
"cs.CR",
"cs.LG"
] | This study examines the impact of tokenized Java code length on the accuracy and explicitness of ten major LLMs in vulnerability detection. Using chi-square tests and known ground truth, we found inconsistencies across models: some, like GPT-4, Mistral, and Mixtral, showed robustness, while others exhibited a significa... | {
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2502.00065 | Blood Glucose Level Prediction in Type 1 Diabetes Using Machine Learning | [
"q-bio.QM",
"cs.LG"
] | Type 1 Diabetes is a chronic autoimmune condition in which the immune system attacks and destroys insulin-producing beta cells in the pancreas, resulting in little to no insulin production. Insulin helps glucose in your blood enter your muscle, fat, and liver cells so they can use it for energy or store it for later us... | {
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2502.00068 | Privacy Preserving Charge Location Prediction for Electric Vehicles | [
"cs.CR",
"cs.AI"
] | By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation, grid infrastructure, and data privacy. Current research on EV routing and charge management often overlooks privacy when predicting energy... | {
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2502.00070 | Can AI Solve the Peer Review Crisis? A Large Scale Experiment on LLM's
Performance and Biases in Evaluating Economics Papers | [
"cs.CY",
"cs.AI",
"econ.GN",
"q-fin.EC"
] | We investigate whether artificial intelligence can address the peer review crisis in economics by analyzing 27,090 evaluations of 9,030 unique submissions using a large language model (LLM). The experiment systematically varies author characteristics (e.g., affiliation, reputation, gender) and publication quality (e.g.... | {
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2502.00072 | LLM Cyber Evaluations Don't Capture Real-World Risk | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.LG"
] | Large language models (LLMs) are demonstrating increasing prowess in cybersecurity applications, creating creating inherent risks alongside their potential for strengthening defenses. In this position paper, we argue that current efforts to evaluate risks posed by these capabilities are misaligned with the goal of unde... | {
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2502.00074 | SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection | [
"cs.CV",
"cs.AI",
"cs.NE"
] | Recently, 4D Radar has emerged as a crucial sensor for 3D object detection in autonomous vehicles, offering both stable perception in adverse weather and high-density point clouds for object shape recognition. However, processing such high-density data demands substantial computational resources and energy consumption.... | {
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2502.00075 | BTS: Harmonizing Specialized Experts into a Generalist LLM | [
"cs.CL",
"cs.LG"
] | We present Branch-Train-Stitch (BTS), an efficient and flexible training algorithm for combining independently trained large language model (LLM) experts into a single, capable generalist model. Following Li et al., we start with a single seed language model which is branched into domain-specific (e.g., coding or math)... | {
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2502.00076 | Influence of color correction on pathology detection in Capsule
Endoscopy | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Pathology detection in Wireless Capsule Endoscopy (WCE) using deep learning has been explored in the recent past. However, deep learning models can be influenced by the color quality of the dataset used to train them, impacting detection, segmentation and classification tasks. In this work, we evaluate the impact of co... | {
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2502.00077 | Robot localization aided by quantum algorithms | [
"cs.RO"
] | Localization is a critical aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Current probabilistic localization methods, such as the Adaptive-Monte Carlo localization (AMCL) algorithm, are computationally intensive and may struggle with large maps or high-resoluti... | {
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2502.00078 | Deep Ensembling with Multimodal Image Fusion for Efficient
Classification of Lung Cancer | [
"eess.IV",
"cs.CV"
] | This study focuses on the classification of cancerous and healthy slices from multimodal lung images. The data used in the research comprises Computed Tomography (CT) and Positron Emission Tomography (PET) images. The proposed strategy achieves the fusion of PET and CT images by utilizing Principal Component Analysis (... | {
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2502.00079 | Advanced Assessment of Stroke in Retinal Fundus Imaging with Deep
Multi-view Learning | [
"eess.IV",
"cs.CV"
] | Stroke is globally a major cause of mortality and morbidity, and hence accurate and rapid diagnosis of stroke is valuable. Retinal fundus imaging reveals the known markers of elevated stroke risk in the eyes, which are retinal venular widening, arteriolar narrowing, and increased tortuosity. In contrast to other imagin... | {
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2502.00083 | CerraData-4MM: A multimodal benchmark dataset on Cerrado for land use
and land cover classification | [
"cs.CV",
"eess.IV"
] | The Cerrado faces increasing environmental pressures, necessitating accurate land use and land cover (LULC) mapping despite challenges such as class imbalance and visually similar categories. To address this, we present CerraData-4MM, a multimodal dataset combining Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel... | {
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2502.00085 | Efficient Beam Search for Large Language Models Using Trie-Based
Decoding | [
"cs.CL"
] | In Transformer-based sequence-to-sequence generation, beam search has proven effective in enhancing the quality of generated sequences compared to greedy decoding. Conventional beam search methods typically adopt either a sequential or batch-based approach. The sequential approach, while memory-efficient, requires mult... | {
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2502.00088 | Re-Visiting Explainable AI Evaluation Metrics to Identify The Most
Informative Features | [
"cs.LG",
"stat.ML"
] | Functionality or proxy-based approach is one of the used approaches to evaluate the quality of explainable artificial intelligence methods. It uses statistical methods, definitions and new developed metrics for the evaluation without human intervention. Among them, Selectivity or RemOve And Retrain (ROAR), and Permutat... | {
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2502.00089 | Ensembles of Low-Rank Expert Adapters | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The training and fine-tuning of large language models (LLMs) often involve diverse textual data from multiple sources, which poses challenges due to conflicting gradient directions, hindering optimization and specialization. These challenges can undermine model generalization across tasks, resulting in reduced downstre... | {
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2502.00090 | Disambiguating Numeral Sequences to Decipher Ancient Accounting Corpora | [
"cs.CL"
] | A numeration system encodes abstract numeric quantities as concrete strings of written characters. The numeration systems used by modern scripts tend to be precise and unambiguous, but this was not so for the ancient and partially-deciphered proto-Elamite (PE) script, where written numerals can have up to four distinct... | {
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2502.00094 | AIN: The Arabic INclusive Large Multimodal Model | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.HC",
"cs.LG"
] | Amid the swift progress of large language models (LLMs) and their evolution into large multimodal models (LMMs), significant strides have been made in high-resource languages such as English and Chinese. While Arabic LLMs have seen notable progress, Arabic LMMs remain largely unexplored, often narrowly focusing on a fe... | {
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2502.00108 | Tracking Most Significant Shifts in Infinite-Armed Bandits | [
"cs.LG",
"stat.ML"
] | We study an infinite-armed bandit problem where actions' mean rewards are initially sampled from a reservoir distribution. Most prior works in this setting focused on stationary rewards (Berry et al., 1997; Wang et al., 2008; Bonald and Proutiere, 2013; Carpentier and Valko, 2015) with the more challenging adversarial/... | {
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2502.00112 | SAGRAD: A Program for Neural Network Training with Simulated Annealing
and the Conjugate Gradient Method | [
"cs.LG",
"cs.NE"
] | SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and M{\o}ller's scaled conjugate gradient algorithm, the latter a variation of the tradition... | {
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2502.00114 | Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model
Approach | [
"cs.RO",
"cs.CV"
] | Hand-drawn maps can be used to convey navigation instructions between humans and robots in a natural and efficient manner. However, these maps can often contain inaccuracies such as scale distortions and missing landmarks which present challenges for mobile robot navigation. This paper introduces a novel Hand-drawn Map... | {
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2502.00115 | A Direct Semi-Exhaustive Search Method for Robust, Partial-to-Full Point
Cloud Registration | [
"cs.RO",
"cs.CV"
] | Point cloud registration refers to the problem of finding the rigid transformation that aligns two given point clouds, and is crucial for many applications in robotics and computer vision. The main insight of this paper is that we can directly optimize the point cloud registration problem without correspondences by uti... | {
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2502.00127 | Sparse Autoencoder Insights on Voice Embeddings | [
"cs.CL"
] | Recent advances in explainable machine learning have highlighted the potential of sparse autoencoders in uncovering mono-semantic features in densely encoded embeddings. While most research has focused on Large Language Model (LLM) embeddings, the applicability of this technique to other domains remains largely unexplo... | {
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2502.00129 | ProtoSnap: Prototype Alignment for Cuneiform Signs | [
"cs.CV",
"cs.LG"
] | The cuneiform writing system served as the medium for transmitting knowledge in the ancient Near East for a period of over three thousand years. Cuneiform signs have a complex internal structure which is the subject of expert paleographic analysis, as variations in sign shapes bear witness to historical developments an... | {
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2502.00131 | Middleman Bias in Advertising: Aligning Relevance of Keyphrase
Recommendations with Search | [
"cs.IR"
] | E-commerce sellers are recommended keyphrases based on their inventory on which they advertise to increase buyer engagement (clicks/sales). Keyphrases must be pertinent to items; otherwise, it can result in seller dissatisfaction and poor targeting -- towards that end relevance filters are employed. In this work, we de... | {
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2502.00133 | Exploring Transfer Learning for Deep Learning Polyp Detection in
Colonoscopy Images Using YOLOv8 | [
"cs.CV",
"cs.AI"
] | Deep learning methods have demonstrated strong performance in objection tasks; however, their ability to learn domain-specific applications with limited training data remains a significant challenge. Transfer learning techniques address this issue by leveraging knowledge from pre-training on related datasets, enabling ... | {
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2502.00136 | A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical
Alignment of Large Language Models | [
"cs.CL",
"cs.AI"
] | This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethic... | {
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2502.00138 | JustAct+: Justified and Accountable Actions in Policy-Regulated,
Multi-Domain Data Processing | [
"cs.LO",
"cs.DC",
"cs.MA",
"cs.PL"
] | Inter-organisational data exchange is regulated by norms originating from sources ranging from (inter)national laws, to processing agreements, and individual consent. Verifying norm compliance is complex because laws (e.g., GDPR) distribute responsibility and require accountability. Moreover, in some application domain... | {
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2502.00139 | Beamforming with Joint Phase and Time Array: System Design, Prototyping
and Performance | [
"cs.IT",
"eess.SP",
"math.IT"
] | Joint phase-time arrays (JPTA) is a new mmWave radio frequency front-end architecture constructed with appending time-delay elements to phase shifters for analog beamforming. JPTA allows the mmWave base station (BS) to form multiple frequency-dependent beams with a single RF chain, exploiting the extra degrees of freed... | {
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2502.00140 | Demystifying MPNNs: Message Passing as Merely Efficient Matrix
Multiplication | [
"cs.LG",
"cs.AI",
"cs.NE",
"cs.SI"
] | While Graph Neural Networks (GNNs) have achieved remarkable success, their design largely relies on empirical intuition rather than theoretical understanding. In this paper, we present a comprehensive analysis of GNN behavior through three fundamental aspects: (1) we establish that \textbf{$k$-layer} Message Passing Ne... | {
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2502.00145 | Counting and Reasoning with Plans | [
"cs.AI"
] | Classical planning asks for a sequence of operators reaching a given goal. While the most common case is to compute a plan, many scenarios require more than that. However, quantitative reasoning on the plan space remains mostly unexplored. A fundamental problem is to count plans, which relates to the conditional probab... | {
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2502.00146 | Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms
Radiologist MRI Interpretation: A Multi-Center Study | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target suspicious prostate lesions. This has led to artificial intelligence (AI) applications improving MRI-based detection of clinically significant prostate cancer (CsPCa). However, MRI-detected lesions must still be mapped to transrectal ultrasound ... | {
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2502.00151 | A Comprehensive Review: Applicability of Deep Neural Networks in
Business Decision Making and Market Prediction Investment | [
"econ.GN",
"cs.AI",
"q-fin.EC"
] | Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent appl... | {
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2502.00156 | ALBAR: Adversarial Learning approach to mitigate Biases in Action
Recognition | [
"cs.CV",
"cs.CR"
] | Bias in machine learning models can lead to unfair decision making, and while it has been well-studied in the image and text domains, it remains underexplored in action recognition. Action recognition models often suffer from background bias (i.e., inferring actions based on background cues) and foreground bias (i.e., ... | {
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2502.00158 | Resolving Editing-Unlearning Conflicts: A Knowledge Codebook Framework
for Large Language Model Updating | [
"cs.CL"
] | Large Language Models (LLMs) excel in natural language processing by encoding extensive human knowledge, but their utility relies on timely updates as knowledge evolves. Updating LLMs involves two key tasks simultaneously: unlearning to remove unwanted knowledge and editing to incorporate new information. Existing meth... | {
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2502.00160 | Improving Quality Control Of MRI Images Using Synthetic Motion Data | [
"eess.IV",
"cs.CV"
] | MRI quality control (QC) is challenging due to unbalanced and limited datasets, as well as subjective scoring, which hinder the development of reliable automated QC systems. To address these issues, we introduce an approach that pretrains a model on synthetically generated motion artifacts before applying transfer lear... | {
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2502.00162 | Physics-informed Split Koopman Operators for Data-efficient Soft Robotic
Simulation | [
"cs.RO"
] | Koopman operator theory provides a powerful data-driven technique for modeling nonlinear dynamical systems in a linear framework, in comparison to computationally expensive and highly nonlinear physics-based simulations. However, Koopman operator-based models for soft robots are very high dimensional and require consid... | {
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2502.00168 | Supervised Quadratic Feature Analysis: An Information Geometry Approach
to Dimensionality Reduction | [
"stat.ML",
"cs.LG",
"math.DG",
"math.ST",
"stat.TH"
] | Supervised dimensionality reduction aims to map labeled data to a low-dimensional feature space while maximizing class discriminability. Despite the availability of methods for learning complex non-linear features (e.g. Deep Learning), there is an enduring demand for dimensionality reduction methods that learn linear f... | {
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2502.00172 | Distribution-Specific Agnostic Conditional Classification With
Halfspaces | [
"cs.LG",
"cs.CC",
"stat.ML"
] | We study ``selective'' or ``conditional'' classification problems under an agnostic setting. Classification tasks commonly focus on modeling the relationship between features and categories that captures the vast majority of data. In contrast to common machine learning frameworks, conditional classification intends to ... | {
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2502.00173 | Lifting by Gaussians: A Simple, Fast and Flexible Method for 3D Instance
Segmentation | [
"cs.CV"
] | We introduce Lifting By Gaussians (LBG), a novel approach for open-world instance segmentation of 3D Gaussian Splatted Radiance Fields (3DGS). Recently, 3DGS Fields have emerged as a highly efficient and explicit alternative to Neural Field-based methods for high-quality Novel View Synthesis. Our 3D instance segmentati... | {
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2502.00174 | The role of positional encodings in the ARC benchmark | [
"cs.AI",
"cs.LG"
] | The Abstraction and Reasoning Corpus challenges AI systems to perform abstract reasoning with minimal training data, a task intuitive for humans but demanding for machine learning models. Using CodeT5+ as a case study, we demonstrate how limitations in positional encoding hinder reasoning and impact performance. This w... | {
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2502.00177 | Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants
Using Sighted Participants | [
"cs.LG",
"cs.CV",
"cs.HC"
] | Human-in-the-loop optimization (HILO) is a promising approach for personalizing visual prostheses by iteratively refining stimulus parameters based on user feedback. Previous work demonstrated HILO's efficacy in simulation, but its performance with human participants remains untested. Here we evaluate HILO using sighte... | {
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2502.00180 | Designing Scheduling for Diffusion Models via Spectral Analysis | [
"cs.LG",
"stat.ML"
] | Diffusion models (DMs) have emerged as powerful tools for modeling complex data distributions and generating realistic new samples. Over the years, advanced architectures and sampling methods have been developed to make these models practically usable. However, certain synthesis process decisions still rely on heuristi... | {
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2502.00182 | Understanding Federated Learning from IID to Non-IID dataset: An
Experimental Study | [
"cs.LG",
"cs.AI",
"stat.ML"
] | As privacy concerns and data regulations grow, federated learning (FL) has emerged as a promising approach for training machine learning models across decentralized data sources without sharing raw data. However, a significant challenge in FL is that client data are often non-IID (non-independent and identically distri... | {
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2502.00185 | Optimal Coupled Sensor Placement and Path-Planning in Unknown
Time-Varying Environments | [
"eess.SY",
"cs.SY"
] | We address path-planning for a mobile agent to navigate in an unknown environment with minimum exposure to a spatially and temporally varying threat field. The threat field is estimated using pointwise noisy measurements from a mobile sensor network. For this problem, we present a new information gain measure for optim... | {
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2502.00186 | Formalising Propositional Information via Implication Hypergraphs | [
"math.LO",
"cs.IT",
"math.IT"
] | This work introduces a framework for quantifying the information content of logical propositions through the use of implication hypergraphs. We posit that a proposition's informativeness is primarily determined by its relationships with other propositions -- specifically, the extent to which it implies or derives other... | {
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2502.00190 | On the Effectiveness of Random Weights in Graph Neural Networks | [
"cs.LG",
"stat.ML"
] | Graph Neural Networks (GNNs) have achieved remarkable success across diverse tasks on graph-structured data, primarily through the use of learned weights in message passing layers. In this paper, we demonstrate that random weights can be surprisingly effective, achieving performance comparable to end-to-end training co... | {
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2502.00193 | Byzantine-Resilient Zero-Order Optimization for Communication-Efficient
Heterogeneous Federated Learning | [
"cs.LG",
"cs.CR",
"cs.DC",
"stat.ML"
] | We introduce CyBeR-0, a Byzantine-resilient federated zero-order optimization method that is robust under Byzantine attacks and provides significant savings in uplink and downlink communication costs. We introduce transformed robust aggregation to give convergence guarantees for general non-convex objectives under clie... | {
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2502.00194 | Physics-Informed Neural Network based Damage Identification for Truss
Railroad Bridges | [
"cs.LG",
"cs.AI",
"physics.comp-ph"
] | Railroad bridges are a crucial component of the U.S. freight rail system, which moves over 40 percent of the nation's freight and plays a critical role in the economy. However, aging bridge infrastructure and increasing train traffic pose significant safety hazards and risk service disruptions. The U.S. rail network in... | {
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2502.00196 | DermaSynth: Rich Synthetic Image-Text Pairs Using Open Access
Dermatology Datasets | [
"cs.CV",
"cs.AI",
"cs.CL"
] | A major barrier to developing vision large language models (LLMs) in dermatology is the lack of large image--text pairs dataset. We introduce DermaSynth, a dataset comprising of 92,020 synthetic image--text pairs curated from 45,205 images (13,568 clinical and 35,561 dermatoscopic) for dermatology-related clinical task... | {
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2502.00197 | Model Successor Functions | [
"cs.LG",
"stat.ML"
] | The notion of generalization has moved away from the classical one defined in statistical learning theory towards an emphasis on out-of-domain generalization (OODG). Recently, there is a growing focus on inductive generalization, where a progression of difficulty implicitly governs the direction of domain shifts. In in... | {
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2502.00198 | Fairshare Data Pricing for Large Language Models | [
"cs.GT",
"cs.CL"
] | Training data is a pivotal resource for building large language models (LLMs), but unfair pricing in data markets poses a serious challenge for both data buyers (e.g., LLM builders) and sellers (e.g., human annotators), which discourages market participation, reducing data quantity and quality. In this paper, we propos... | {
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2502.00199 | Optimal Construction of Data Injection Attacks on Process Systems | [
"eess.SY",
"cs.SY"
] | An information-theoretic framework for constructing data injection attacks on process systems, from the attacker's standpoint, is studied. The attack construction aims to distract the stationary distributions of the process variables and stay stealthy, simultaneously. The problem is formulated as designing a multivaria... | {
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2502.00201 | Year-over-Year Developments in Financial Fraud Detection via Deep
Learning: A Systematic Literature Review | [
"cs.LG",
"cs.AI",
"q-fin.ST"
] | This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of variou... | {
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2502.00203 | Reward-aware Preference Optimization: A Unified Mathematical Framework
for Model Alignment | [
"cs.LG",
"cs.CL"
] | The rapid development of large language model (LLM) alignment algorithms has resulted in a complex and fragmented landscape, with limited clarity on the effectiveness of different methods and their inter-connections. This paper introduces Reward-Aware Preference Optimization (RPO), a mathematical framework that unifies... | {
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2502.00204 | Nearly-Optimal Bandit Learning in Stackelberg Games with Side
Information | [
"cs.LG",
"cs.GT"
] | We study the problem of online learning in Stackelberg games with side information between a leader and a sequence of followers. In every round the leader observes contextual information and commits to a mixed strategy, after which the follower best-responds. We provide learning algorithms for the leader which achieve ... | {
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2502.00205 | EcoWeedNet: A Lightweight and Automated Weed Detection Method for
Sustainable Next-Generation Agricultural Consumer Electronics | [
"cs.CV",
"cs.AI"
] | Sustainable agriculture plays a crucial role in ensuring world food security for consumers. A critical challenge faced by sustainable precision agriculture is weed growth, as weeds share essential resources with the crops, such as water, soil nutrients, and sunlight, which notably affect crop yields. The traditional me... | {
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2502.00206 | BICompFL: Stochastic Federated Learning with Bi-Directional Compression | [
"cs.LG",
"cs.DC",
"cs.IT",
"math.IT",
"stat.ML"
] | We address the prominent communication bottleneck in federated learning (FL). We specifically consider stochastic FL, in which models or compressed model updates are specified by distributions rather than deterministic parameters. Stochastic FL offers a principled approach to compression, and has been shown to reduce t... | {
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2502.00208 | Discovering Dataset Nature through Algorithmic Clustering based on
String Compression | [
"cs.IT",
"math.IT"
] | Text datasets can be represented using models that do not preserve text structure, or using models that preserve text structure. Our hypothesis is that depending on the dataset nature, there can be advantages using a model that preserves text structure over one that does not, and viceversa. The key is to determine the ... | {
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2502.00212 | STP: Self-play LLM Theorem Provers with Iterative Conjecturing and
Proving | [
"cs.LG",
"cs.AI",
"cs.LO"
] | A fundamental challenge in formal theorem proving by LLMs is the lack of high-quality training data. Although reinforcement learning or expert iteration partially mitigates this issue by alternating between LLM generating proofs and finetuning them on correctly generated ones, performance quickly plateaus due to the sc... | {
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2502.00213 | Understanding Why Adam Outperforms SGD: Gradient Heterogeneity in
Transformers | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Transformer models are challenging to optimize with SGD and typically require adaptive optimizers such as Adam. However, the reasons behind the superior performance of Adam over SGD remain unclear. In this study, we investigate the optimization of transformer models by focusing on \emph{gradient heterogeneity}, defined... | {
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2502.00215 | Impulsive Relative Motion Control with Continuous-Time Constraint
Satisfaction for Cislunar Space Missions | [
"eess.SY",
"cs.SY"
] | Recent investments in cislunar applications open new frontiers for space missions within highly nonlinear dynamical regimes. In this paper, we propose a method based on Sequential Convex Programming (SCP) to loiter around a given target with impulsive actuation while satisfying path constraints continuously over the fi... | {
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2502.00217 | Fantastic Multi-Task Gradient Updates and How to Find Them In a Cone | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Balancing competing objectives remains a fundamental challenge in multi-task learning (MTL), primarily due to conflicting gradients across individual tasks. A common solution relies on computing a dynamic gradient update vector that balances competing tasks as optimization progresses. Building on this idea, we propose ... | {
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2502.00219 | Team Size and Its Negative Impact on the Disruption Index | [
"cs.SI"
] | As science transitions from the age of lone geniuses to an era of collaborative teams, the question of whether large teams can sustain the creativity of individuals and continue driving innovation has become increasingly important. Our previous research first revealed a negative relationship between team size and the D... | {
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2502.00220 | Algorithmic Clustering based on String Compression to Extract P300
Structure in EEG Signals | [
"cs.LG",
"cs.IT",
"eess.SP",
"math.IT"
] | P300 is an Event-Related Potential widely used in Brain-Computer Interfaces, but its detection is challenging due to inter-subject and temporal variability. This work introduces a clustering methodology based on Normalized Compression Distance (NCD) to extract the P300 structure, ensuring robustness against variability... | {
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2502.00221 | Social Robots as Social Proxies for Fostering Connection and Empathy
Towards Humanity | [
"cs.HC",
"cs.RO"
] | Despite living in an increasingly connected world, social isolation is a prevalent issue today. While social robots have been explored as tools to enhance social connection through companionship, their potential as asynchronous social platforms for fostering connection towards humanity has received less attention. In t... | {
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2502.00222 | The Free Termination Property of Queries Over Time | [
"cs.DB",
"cs.DC",
"cs.PL"
] | Building on prior work on distributed databases and the CALM Theorem, we define and study the question of free termination: in the absence of distributed coordination, what query properties allow nodes in a distributed (database) system to unilaterally terminate execution even though they may receive additional data or... | {
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2502.00225 | Should You Use Your Large Language Model to Explore or Exploit? | [
"cs.LG",
"cs.AI",
"cs.CL"
] | We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploitation tradeoff. We use LLMs to explore and exploit in silos in various (contextual) bandit tasks. We find that while the current LLMs often struggle to exploit, in-context mitig... | {
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2502.00226 | HackerRank-ASTRA: Evaluating Correctness & Consistency of Large Language
Models on cross-domain multi-file project problems | [
"cs.LG",
"cs.SE"
] | Evaluating the real-world applicability of large language models (LLMs) provides valuable insights for their development and use in software development tasks. Existing benchmarks often focus on standalone coding problems or specific libraries, overlooking multi-file, project-based scenarios and lacking a rigorous eval... | {
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2502.00227 | AK-SLRL: Adaptive Krylov Subspace Exploration Using Single-Life
Reinforcement Learning for Sparse Linear System | [
"cs.CE"
] | This paper presents a single-life reinforcement learning (SLRL) approach to adaptively select the dimension of the Krylov subspace during the generalized minimal residual (GMRES) iteration. GMRES is an iterative algorithm for solving large and sparse linear systems of equations in the form of \(Ax = b\) which are mainl... | {
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2502.00232 | A Hybrid Random Forest and CNN Framework for Tile-Wise Oil-Water
Classification in Hyperspectral Images | [
"cs.CV",
"cs.AI"
] | A novel hybrid Random Forest and Convolutional Neural Network (CNN) framework is presented for oil-water classification in hyperspectral images (HSI). To address the challenge of preserving spatial context, the images were divided into smaller, non-overlapping tiles, which served as the basis for training, validation, ... | {
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2502.00233 | Vision-Based Fuzzy Control System for Smart Walkers: Enhancing Usability
for Stroke Survivors with Unilateral Upper Limb Impairments | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Mobility impairments, particularly those caused by stroke-induced hemiparesis, significantly impact independence and quality of life. Current smart walker controllers operate by using input forces from the user to control linear motion and input torques to dictate rotational movement; however, because they predominantl... | {
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2502.00234 | Fast Solvers for Discrete Diffusion Models: Theory and Applications of
High-Order Algorithms | [
"cs.LG",
"cs.CV",
"cs.NA",
"math.NA",
"physics.comp-ph",
"stat.ML"
] | Discrete diffusion models have emerged as a powerful generative modeling framework for discrete data with successful applications spanning from text generation to image synthesis. However, their deployment faces challenges due to the high dimensionality of the state space, necessitating the development of efficient inf... | {
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2502.00240 | Learning Difference-of-Convex Regularizers for Inverse Problems: A
Flexible Framework with Theoretical Guarantees | [
"stat.ML",
"cs.LG",
"eess.IV",
"math.OC"
] | Learning effective regularization is crucial for solving ill-posed inverse problems, which arise in a wide range of scientific and engineering applications. While data-driven methods that parameterize regularizers using deep neural networks have demonstrated strong empirical performance, they often result in highly non... | {
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2502.00241 | Mordal: Automated Pretrained Model Selection for Vision Language Models | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | Incorporating multiple modalities into large language models (LLMs) is a powerful way to enhance their understanding of non-textual data, enabling them to perform multimodal tasks. Vision language models (VLMs) form the fastest growing category of multimodal models because of their many practical use cases, including i... | {
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2502.00242 | Digital-Twin assisted Network Energy Optimization during Low Traffic
Hours | [
"cs.NI",
"cs.SY",
"eess.SY"
] | As wireless network technology advances towards the sixth generation (6G), increasing network energy consumption has become a critical concern due to the growing demand for diverse services, radio deployments at various frequencies, larger bandwidths, and more antennas. Network operators must manage energy usage not on... | {
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2502.00245 | Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion | [
"cs.LG"
] | Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that resemble high-quality private data while ensuring Differential Privacy (DP), a formal privacy guarantee, promises scalability and practicality.... | {
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2502.00246 | Context-Preserving Tensorial Reconfiguration in Large Language Model
Training | [
"cs.CL"
] | Handling long-range dependencies in neural architectures has remained a persistent challenge due to computational limitations and inefficient contextual retention mechanisms. Tensorial operations have provided a foundation for restructuring model representations, yet conventional architectures have struggled to incorpo... | {
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2502.00248 | Provably-Stable Neural Network-Based Control of Nonlinear Systems | [
"math.OC",
"cs.LG",
"cs.SY",
"eess.SY"
] | In recent years, Neural Networks (NNs) have been employed to control nonlinear systems due to their potential capability in dealing with situations that might be difficult for conventional nonlinear control schemes. However, to the best of our knowledge, the current literature on NN-based control lacks theoretical guar... | {
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2502.00250 | Transformer-Based Vector Font Classification Using Different Font
Formats: TrueType versus PostScript | [
"cs.CV"
] | Modern fonts adopt vector-based formats, which ensure scalability without loss of quality. While many deep learning studies on fonts focus on bitmap formats, deep learning for vector fonts remains underexplored. In studies involving deep learning for vector fonts, the choice of font representation has often been made c... | {
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2502.00253 | Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT
Image Denoising | [
"eess.IV",
"cs.CV"
] | Image denoising of low-dose computed tomography (LDCT) is an important problem for clinical diagnosis with reduced radiation exposure. Previous methods are mostly trained with pairs of synthetic or misaligned LDCT and normal-dose CT (NDCT) images. However, trained with synthetic noise or misaligned LDCT/NDCT image pair... | {
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2502.00258 | ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for
Pretrained LLMs | [
"cs.LG",
"cs.CL"
] | Large Language Models (LLMs) have demonstrated exceptional performance in natural language processing tasks, yet their massive size makes serving them inefficient and costly. Semi-structured pruning has emerged as an effective method for model acceleration, but existing approaches are suboptimal because they focus on l... | {
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2502.00262 | INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language
Models on Context-Aware Hazard Detection and Edge Case Evaluation | [
"cs.CV",
"cs.AI"
] | Autonomous driving systems face significant challenges in handling unpredictable edge-case scenarios, such as adversarial pedestrian movements, dangerous vehicle maneuvers, and sudden environmental changes. Current end-to-end driving models struggle with generalization to these rare events due to limitations in traditi... | {
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2502.00264 | Beyond the Permutation Symmetry of Transformers: The Role of Rotation
for Model Fusion | [
"cs.LG",
"cs.CV"
] | Symmetry in the parameter space of deep neural networks (DNNs) has proven beneficial for various deep learning applications. A well-known example is the permutation symmetry in Multi-Layer Perceptrons (MLPs), where permuting the rows of weight matrices in one layer and applying the inverse permutation to adjacent layer... | {
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2502.00265 | RADx Data Hub: A Cloud Platform for FAIR, Harmonized COVID-19 Data | [
"cs.DB"
] | The COVID-19 pandemic highlighted the urgent need for robust systems to enable rapid data collection, integration, and analysis for public health responses. Existing approaches often relied on disparate, non-interoperable systems, creating bottlenecks in comprehensive analyses and timely decision-making. To address the... | {
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2502.00266 | MCM: Multi-layer Concept Map for Efficient Concept Learning from Masked
Images | [
"cs.CV",
"cs.LG"
] | Masking strategies commonly employed in natural language processing are still underexplored in vision tasks such as concept learning, where conventional methods typically rely on full images. However, using masked images diversifies perceptual inputs, potentially offering significant advantages in concept learning with... | {
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2502.00270 | DUET: Optimizing Training Data Mixtures via Feedback from Unseen
Evaluation Tasks | [
"cs.LG",
"cs.AI",
"stat.ML"
] | The performance of a machine learning (ML) model depends heavily on the relevance of its training data to the domain of the downstream evaluation task. However, in practice, the data involved in an unseen evaluation task is often not known to us (e.g., conversations between an LLM and a user are end-to-end encrypted). ... | {
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2502.00271 | Scaling Flaws of Verifier-Guided Search in Mathematical Reasoning | [
"cs.CL"
] | Large language models (LLMs) struggle with multi-step reasoning, where inference-time scaling has emerged as a promising strategy for performance improvement. Verifier-guided search outperforms repeated sampling when sample size is limited by selecting and prioritizing valid reasoning paths. However, we identify a crit... | {
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2502.00274 | AoI in M/G/1/1 Queues with Probabilistic Preemption | [
"cs.IT",
"math.IT"
] | We consider a status update system consisting of one source, one server, and one sink. The source generates packets according to a Poisson process and the packets are served according to a generally distributed service time. We consider a system with a capacity of one packet, i.e., there is no waiting buffer in the sys... | {
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2502.00275 | Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from
Forearm Ultrasound Images | [
"cs.RO",
"cs.CV",
"cs.ET",
"cs.HC"
] | Accurate estimation of human hand configuration and the forces they exert is critical for effective teleoperation and skill transfer in robotic manipulation. A deeper understanding of human interactions with objects can further enhance teleoperation performance. To address this need, researchers have explored methods t... | {
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"cs.SY": 0
} |
2502.00277 | Regularized Langevin Dynamics for Combinatorial Optimization | [
"cs.LG",
"stat.ML"
] | This work proposes a simple yet effective sampling framework for combinatorial optimization (CO). Our method builds on discrete Langevin dynamics (LD), an efficient gradient-guided generative algorithm. However, we observed that directly applying LD often leads to limited exploration. To overcome this limitation, we pr... | {
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} |
2502.00279 | Improving realistic semi-supervised learning with doubly robust
estimation | [
"cs.LG",
"stat.ML"
] | A major challenge in Semi-Supervised Learning (SSL) is the limited information available about the class distribution in the unlabeled data. In many real-world applications this arises from the prevalence of long-tailed distributions, where the standard pseudo-label approach to SSL is biased towards the labeled class d... | {
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} |
2502.00280 | On the study of frequency control and spectral bias in Wavelet-Based
Kolmogorov Arnold networks: A path to physics-informed KANs | [
"cs.LG",
"cs.NA",
"math.NA"
] | Spectral bias, the tendency of neural networks to prioritize learning low-frequency components of functions during the initial training stages, poses a significant challenge when approximating solutions with high-frequency details. This issue is particularly pronounced in physics-informed neural networks (PINNs), widel... | {
"Other": 1,
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} |
2502.00281 | Sigmoid Self-Attention is Better than Softmax Self-Attention: A
Mixture-of-Experts Perspective | [
"cs.LG",
"cs.AI"
] | At the core of the popular Transformer architecture is the self-attention mechanism, which dynamically assigns softmax weights to each input token so that the model can focus on the most salient information. However, the softmax structure slows down the attention computation due to its row-wise nature, and inherently i... | {
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} |
2502.00282 | GraphMinNet: Learning Dependencies in Graphs with Light Complexity
Minimal Architecture | [
"cs.LG"
] | Graph Neural Networks (GNNs) have demonstrated remarkable success in various applications, yet they often struggle to capture long-range dependencies (LRD) effectively. This paper introduces GraphMinNet, a novel GNN architecture that generalizes the idea of minimal Gated Recurrent Units to graph-structured data. Our ap... | {
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} |
2502.00284 | Bounded-Confidence Models of Multi-Dimensional Opinions with
Topic-Weighted Discordance | [
"physics.soc-ph",
"cs.SI",
"math.DS"
] | People's opinions on a wide range of topics often evolve over time through their interactions with others. Models of opinion dynamics primarily focus on one-dimensional opinions which represent opinions on one topic. However, opinions on various topics are rarely isolated; instead, they can be interdependent and exhibi... | {
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} |
2502.00285 | K Nearest Neighbor-Guided Trajectory Similarity Learning | [
"cs.LG",
"cs.CV",
"cs.DB"
] | Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once trained. Although efficient inference has been reported, challenges remain in similarit... | {
"Other": 0,
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} |
2502.00288 | Learning from Suboptimal Data in Continuous Control via Auto-Regressive
Soft Q-Network | [
"cs.LG",
"cs.RO"
] | Reinforcement learning (RL) for continuous control often requires large amounts of online interaction data. Value-based RL methods can mitigate this burden by offering relatively high sample efficiency. Some studies further enhance sample efficiency by incorporating offline demonstration data to "kick-start" training, ... | {
"Other": 0,
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} |
2502.00290 | Estimating LLM Uncertainty with Logits | [
"cs.CL",
"cs.AI"
] | In recent years, Large Language Models (LLMs) have seen remarkable advancements and have been extensively integrated across various fields. Despite their progress, LLMs are prone to hallucinations, producing responses that may not be dependable if the models lack sufficient grounding knowledge. To mitigate this issue, ... | {
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} |
2502.00294 | On the Source Model Key Agreement Problem | [
"cs.IT",
"math.IT"
] | We consider the source model key agreement problem involving two legitimate parties and an eavesdropper who observe n i.i.d. samples of X and Y and Z respectively. The best-known upper bound on the key capacity is characterized by an inf-max optimization problem that generally lacks a closed-form solution. In this pape... | {
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} |
2502.00298 | The Price of Linear Time: Error Analysis of Structured Kernel
Interpolation | [
"cs.LG",
"stat.ML"
] | Structured Kernel Interpolation (SKI) (Wilson et al. 2015) helps scale Gaussian Processes (GPs) by approximating the kernel matrix via interpolation at inducing points, achieving linear computational complexity. However, it lacks rigorous theoretical error analysis. This paper bridges the gap: we prove error bounds for... | {
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} |
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