id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.17890 | VidSole: A Multimodal Dataset for Joint Kinetics Quantification and
Disease Detection with Deep Learning | [
"cs.CV",
"eess.SP"
] | Understanding internal joint loading is critical for diagnosing gait-related diseases such as knee osteoarthritis; however, current methods of measuring joint risk factors are time-consuming, expensive, and restricted to lab settings. In this paper, we enable the large-scale, cost-effective biomechanical analysis of jo... | {
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2501.17893 | Language Modelling for Speaker Diarization in Telephonic Interviews | [
"eess.AS",
"cs.LG",
"cs.SD"
] | The aim of this paper is to investigate the benefit of combining both language and acoustic modelling for speaker diarization. Although conventional systems only use acoustic features, in some scenarios linguistic data contain high discriminative speaker information, even more reliable than the acoustic ones. In this s... | {
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2501.17894 | Progress in Artificial Intelligence and its Determinants | [
"econ.GN",
"cs.AI",
"cs.CY",
"cs.LG",
"physics.soc-ph",
"q-fin.EC"
] | We study long-run progress in artificial intelligence in a quantitative way. Many measures, including traditional ones such as patents and publications, machine learning benchmarks, and a new Aggregate State of the Art in ML (or ASOTA) Index we have constructed from these, show exponential growth at roughly constant ra... | {
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2501.17896 | Explainable Machine Learning: An Illustration of Kolmogorov-Arnold
Network Model for Airfoil Lift Prediction | [
"cs.LG"
] | Data science has emerged as fourth paradigm of scientific exploration. However many machine learning models operate as black boxes offering limited insight into the reasoning behind their predictions. This lack of transparency is one of the drawbacks to generate new knowledge from data. Recently Kolmogorov-Arnold Netwo... | {
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2501.17897 | Visualization of Organ Movements Using Automatic Region Segmentation of
Swallowing CT | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | This study presents the first report on the development of an artificial intelligence (AI) for automatic region segmentation of four-dimensional computer tomography (4D-CT) images during swallowing. The material consists of 4D-CT images taken during swallowing. Additionally, data for verifying the practicality of the A... | {
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2501.17898 | Distilling Knowledge for Designing Computational Imaging Systems | [
"eess.IV",
"cs.LG"
] | Designing the physical encoder is crucial for accurate image reconstruction in computational imaging (CI) systems. Currently, these systems are designed via end-to-end (E2E) optimization, where the encoder is modeled as a neural network layer and is jointly optimized with the decoder. However, the performance of E2E op... | {
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2501.17899 | The Right to AI | [
"cs.CY",
"cs.AI",
"cs.HC"
] | This paper proposes a Right to AI, which asserts that individuals and communities should meaningfully participate in the development and governance of the AI systems that shape their lives. Motivated by the increasing deployment of AI in critical domains and inspired by Henri Lefebvre's concept of the Right to the City... | {
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2501.17900 | Shared DIFF Transformer | [
"cs.LG"
] | DIFF Transformer improves attention allocation by enhancing focus on relevant context while suppressing noise. It introduces a differential attention mechanism that calculates the difference between two independently generated attention distributions, effectively reducing noise and promoting sparse attention patterns. ... | {
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2501.17901 | Molecular Fingerprints Are Strong Models for Peptide Function Prediction | [
"q-bio.BM",
"cs.LG"
] | We study the effectiveness of molecular fingerprints for peptide property prediction and demonstrate that domain-specific feature extraction from molecular graphs can outperform complex and computationally expensive models such as GNNs, pretrained sequence-based transformers and multimodal ensembles, even without hyper... | {
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2501.17903 | Free Agent in Agent-Based Mixture-of-Experts Generative AI Framework | [
"cs.MA",
"cs.AI"
] | Multi-agent systems commonly distribute tasks among specialized, autonomous agents, yet they often lack mechanisms to replace or reassign underperforming agents in real time. Inspired by the free-agency model of Major League Baseball, the Reinforcement Learning Free Agent (RLFA) algorithm introduces a reward-based mech... | {
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2501.17904 | A Robust Support Vector Machine Approach for Raman COVID-19 Data
Classification | [
"q-bio.QM",
"cs.LG"
] | Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological samples has been successfully applied for early-stage diagnosis. However, spectra' in... | {
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2501.17905 | DReSS: Data-driven Regularized Structured Streamlining for Large
Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large language models (LLMs) have achieved significant progress across various domains, but their increasing scale results in high computational and memory costs. Recent studies have revealed that LLMs exhibit sparsity, providing the potential to reduce model size through pruning techniques. However, existing pruning m... | {
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2501.17906 | Unsupervised Patch-GAN with Targeted Patch Ranking for Fine-Grained
Novelty Detection in Medical Imaging | [
"cs.CV",
"eess.IV"
] | Detecting novel anomalies in medical imaging is challenging due to the limited availability of labeled data for rare abnormalities, which often display high variability and subtlety. This challenge is further compounded when small abnormal regions are embedded within larger normal areas, as whole-image predictions freq... | {
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2501.17917 | Deep Ensembles Secretly Perform Empirical Bayes | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Quantifying uncertainty in neural networks is a highly relevant problem which is essential to many applications. The two predominant paradigms to tackle this task are Bayesian neural networks (BNNs) and deep ensembles. Despite some similarities between these two approaches, they are typically surmised to lack a formal ... | {
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2501.17951 | An iterative spectral algorithm for digraph clustering | [
"physics.soc-ph",
"cs.SI"
] | Graph clustering is a fundamental technique in data analysis with applications in many different fields. While there is a large body of work on clustering undirected graphs, the problem of clustering directed graphs is much less understood. The analysis is more complex in the directed graph case for two reasons: the cl... | {
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2501.17962 | Agricultural Industry Initiatives on Autonomy: How collaborative
initiatives of VDMA and AEF can facilitate complexity in domain crossing
harmonization needs | [
"cs.CY",
"cs.RO",
"cs.SY",
"eess.SY"
] | The agricultural industry is undergoing a significant transformation with the increasing adoption of autonomous technologies. Addressing complex challenges related to safety and security, components and validation procedures, and liability distribution is essential to facilitate the adoption of autonomous technologies.... | {
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2501.17963 | Physics-Grounded Differentiable Simulation for Soft Growing Robots | [
"cs.RO"
] | Soft-growing robots (i.e., vine robots) are a promising class of soft robots that allow for navigation and growth in tightly confined environments. However, these robots remain challenging to model and control due to the complex interplay of the inflated structure and inextensible materials, which leads to obstacles fo... | {
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2501.17965 | Variational Combinatorial Sequential Monte Carlo for Bayesian
Phylogenetics in Hyperbolic Space | [
"cs.LG",
"stat.ML"
] | Hyperbolic space naturally encodes hierarchical structures such as phylogenies (binary trees), where inward-bending geodesics reflect paths through least common ancestors, and the exponential growth of neighborhoods mirrors the super-exponential scaling of topologies. This scaling challenge limits the efficiency of Euc... | {
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2501.17968 | Online Trajectory Replanner for Dynamically Grasping Irregular Objects | [
"cs.RO"
] | This paper presents a new trajectory replanner for grasping irregular objects. Unlike conventional grasping tasks where the object's geometry is assumed simple, we aim to achieve a "dynamic grasp" of the irregular objects, which requires continuous adjustment during the grasping process. To effectively handle irregular... | {
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2501.17969 | LLMs can be Fooled into Labelling a Document as Relevant (best caf\'e
near me; this paper is perfectly relevant) | [
"cs.IR"
] | LLMs are increasingly being used to assess the relevance of information objects. This work reports on experiments to study the labelling of short texts (i.e., passages) for relevance, using multiple open-source and proprietary LLMs. While the overall agreement of some LLMs with human judgements is comparable to human-t... | {
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2501.17974 | Think Smarter not Harder: Adaptive Reasoning with Inference Aware
Optimization | [
"cs.AI"
] | Solving mathematics problems has been an intriguing capability of large language models, and many efforts have been made to improve reasoning by extending reasoning length, such as through self-correction and extensive long chain-of-thoughts. While promising in problem-solving, advanced long reasoning chain models exhi... | {
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2501.17976 | KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated
Recurrent Units | [
"cs.LG"
] | Anomaly detection in real-world time-series data is a challenging task due to the complex and nonlinear temporal dynamics involved. This paper introduces KoopAGRU, a new deep learning model designed to tackle this problem by combining Fast Fourier Transform (FFT), Deep Dynamic Mode Decomposition (DeepDMD), and Koopman ... | {
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2501.17977 | TransRAD: Retentive Vision Transformer for Enhanced Radar Object
Detection | [
"cs.CV",
"cs.SY",
"eess.SY"
] | Despite significant advancements in environment perception capabilities for autonomous driving and intelligent robotics, cameras and LiDARs remain notoriously unreliable in low-light conditions and adverse weather, which limits their effectiveness. Radar serves as a reliable and low-cost sensor that can effectively com... | {
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2501.17978 | VoD-3DGS: View-opacity-Dependent 3D Gaussian Splatting | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Reconstructing a 3D scene from images is challenging due to the different ways light interacts with surfaces depending on the viewer's position and the surface's material. In classical computer graphics, materials can be classified as diffuse or specular, interacting with light differently. The standard 3D Gaussian Spl... | {
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2501.17980 | Limits to AI Growth: The Ecological and Social Consequences of Scaling | [
"cs.CY",
"cs.AI"
] | The accelerating development and deployment of AI technologies depend on the continued ability to scale their infrastructure. This has implied increasing amounts of monetary investment and natural resources. Frontier AI applications have thus resulted in rising financial, environmental, and social costs. While the fact... | {
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2501.17981 | Can Generative LLMs Create Query Variants for Test Collections? An
Exploratory Study | [
"cs.IR"
] | This paper explores the utility of a Large Language Model (LLM) to automatically generate queries and query variants from a description of an information need. Given a set of information needs described as backstories, we explore how similar the queries generated by the LLM are to those generated by humans. We quantify... | {
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2501.17982 | Belief Roadmaps with Uncertain Landmark Evanescence | [
"cs.RO",
"cs.AI"
] | We would like a robot to navigate to a goal location while minimizing state uncertainty. To aid the robot in this endeavor, maps provide a prior belief over the location of objects and regions of interest. To localize itself within the map, a robot identifies mapped landmarks using its sensors. However, as the time bet... | {
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2501.17983 | Efficient Feature Fusion for UAV Object Detection | [
"cs.CV"
] | Object detection in unmanned aerial vehicle (UAV) remote sensing images poses significant challenges due to unstable image quality, small object sizes, complex backgrounds, and environmental occlusions. Small objects, in particular, occupy small portions of images, making their accurate detection highly difficult. Exis... | {
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2501.17987 | Pressure Field Reconstruction with SIREN: A Mesh-Free Approach for Image
Velocimetry in Complex Noisy Environments | [
"cs.CV",
"physics.flu-dyn"
] | This work presents a novel approach for pressure field reconstruction from image velocimetry data using SIREN (Sinusoidal Representation Network), emphasizing its effectiveness as an implicit neural representation in noisy environments and its mesh-free nature. While we briefly assess two recently proposed methods - on... | {
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2501.17991 | Investigating the Monte-Carlo Tree Search Approach for the Job Shop
Scheduling Problem | [
"cs.AI",
"math.OC"
] | The Job Shop Scheduling Problem (JSSP) is a well-known optimization problem in manufacturing, where the goal is to determine the optimal sequence of jobs across different machines to minimize a given objective. In this work, we focus on minimising the weighted sum of job completion times. We explore the potential of Mo... | {
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2501.17992 | Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of
Market Information | [
"q-fin.PM",
"cs.LG"
] | We develop a portfolio allocation framework that leverages deep learning techniques to address challenges arising from high-dimensional, non-stationary, and low-signal-to-noise market information. Our approach includes a dynamic embedding method that reduces the non-stationary, high-dimensional state space into a lower... | {
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2501.17994 | InnerThoughts: Disentangling Representations and Predictions in Large
Language Models | [
"cs.CL",
"cs.LG"
] | Large language models (LLMs) contain substantial factual knowledge which is commonly elicited by multiple-choice question-answering prompts. Internally, such models process the prompt through multiple transformer layers, building varying representations of the problem within its hidden states. Ultimately, however, only... | {
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2501.18005 | Fault Localization via Fine-tuning Large Language Models with Mutation
Generated Stack Traces | [
"cs.SE",
"cs.LG"
] | Abrupt and unexpected terminations of software are termed as software crashes. They can be challenging to analyze. Finding the root cause requires extensive manual effort and expertise to connect information sources like stack traces, source code, and logs. Typical approaches to fault localization require either test f... | {
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2501.18006 | Topological Signatures of Adversaries in Multimodal Alignments | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Multimodal Machine Learning systems, particularly those aligning text and image data like CLIP/BLIP models, have become increasingly prevalent, yet remain susceptible to adversarial attacks. While substantial research has addressed adversarial robustness in unimodal contexts, defense strategies for multimodal systems a... | {
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2501.18009 | Large Language Models Think Too Fast To Explore Effectively | [
"cs.AI",
"q-bio.NC"
] | Large Language Models have emerged many intellectual capacities. While numerous benchmarks assess their intelligence, limited attention has been given to their ability to explore, an essential capacity for discovering new information and adapting to novel environments in both natural and artificial systems. The extent ... | {
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2501.18011 | Anatomy Might Be All You Need: Forecasting What to Do During Surgery | [
"cs.CV",
"cs.AI"
] | Surgical guidance can be delivered in various ways. In neurosurgery, spatial guidance and orientation are predominantly achieved through neuronavigation systems that reference pre-operative MRI scans. Recently, there has been growing interest in providing live guidance by analyzing video feeds from tools such as endosc... | {
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2501.18012 | When less is more: evolving large neural networks from small ones | [
"cs.LG",
"cond-mat.dis-nn"
] | In contrast to conventional artificial neural networks, which are large and structurally static, we study feed-forward neural networks that are small and dynamic, whose nodes can be added (or subtracted) during training. A single neuronal weight in the network controls the network's size, while the weight itself is opt... | {
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2501.18015 | A Proximal Operator for Inducing 2:4-Sparsity | [
"cs.LG"
] | Recent hardware advancements in AI Accelerators and GPUs allow to efficiently compute sparse matrix multiplications, especially when 2 out of 4 consecutive weights are set to zero. However, this so-called 2:4 sparsity usually comes at a decreased accuracy of the model. We derive a regularizer that exploits the local co... | {
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2501.18016 | Digital Twin-Enabled Real-Time Control in Robotic Additive Manufacturing
via Soft Actor-Critic Reinforcement Learning | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | Smart manufacturing systems increasingly rely on adaptive control mechanisms to optimize complex processes. This research presents a novel approach integrating Soft Actor-Critic (SAC) reinforcement learning with digital twin technology to enable real-time process control in robotic additive manufacturing. We demonstrat... | {
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2501.18018 | Perforated Backpropagation: A Neuroscience Inspired Extension to
Artificial Neural Networks | [
"cs.NE",
"cs.LG",
"q-bio.NC"
] | The neurons of artificial neural networks were originally invented when much less was known about biological neurons than is known today. Our work explores a modification to the core neuron unit to make it more parallel to a biological neuron. The modification is made with the knowledge that biological dendrites are no... | {
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2501.18028 | KNN and K-means in Gini Prametric Spaces | [
"cs.LG"
] | This paper introduces innovative enhancements to the K-means and K-nearest neighbors (KNN) algorithms based on the concept of Gini prametric spaces. Unlike traditional distance metrics, Gini-based measures incorporate both value-based and rank-based information, improving robustness to noise and outliers. The main cont... | {
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2501.18033 | Generative AI for Vision: A Comprehensive Study of Frameworks and
Applications | [
"cs.CV"
] | Generative AI is transforming image synthesis, enabling the creation of high-quality, diverse, and photorealistic visuals across industries like design, media, healthcare, and autonomous systems. Advances in techniques such as image-to-image translation, text-to-image generation, domain transfer, and multimodal alignme... | {
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2501.18039 | Online Nonstochastic Control with Convex Safety Constraints | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper considers the online nonstochastic control problem of a linear time-invariant system under convex state and input constraints that need to be satisfied at all times. We propose an algorithm called Online Gradient Descent with Buffer Zone for Convex Constraints (OGD-BZC), designed to handle scenarios where th... | {
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2501.18045 | From tools to thieves: Measuring and understanding public perceptions of
AI through crowdsourced metaphors | [
"cs.CY",
"cs.AI",
"cs.CL",
"cs.HC"
] | How has the public responded to the increasing prevalence of artificial intelligence (AI)-based technologies? We investigate public perceptions of AI by collecting over 12,000 responses over 12 months from a nationally representative U.S. sample. Participants provided open-ended metaphors reflecting their mental models... | {
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2501.18049 | Joint Pricing and Resource Allocation: An Optimal Online-Learning
Approach | [
"cs.LG",
"math.OC",
"stat.ML"
] | We study an online learning problem on dynamic pricing and resource allocation, where we make joint pricing and inventory decisions to maximize the overall net profit. We consider the stochastic dependence of demands on the price, which complicates the resource allocation process and introduces significant non-convexit... | {
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2501.18052 | SAeUron: Interpretable Concept Unlearning in Diffusion Models with
Sparse Autoencoders | [
"cs.LG",
"cs.AI"
] | Diffusion models, while powerful, can inadvertently generate harmful or undesirable content, raising significant ethical and safety concerns. Recent machine unlearning approaches offer potential solutions but often lack transparency, making it difficult to understand the changes they introduce to the base model. In thi... | {
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2501.18055 | Current Pathology Foundation Models are unrobust to Medical Center
Differences | [
"cs.LG",
"cs.AI"
] | Pathology Foundation Models (FMs) hold great promise for healthcare. Before they can be used in clinical practice, it is essential to ensure they are robust to variations between medical centers. We measure whether pathology FMs focus on biological features like tissue and cancer type, or on the well known confounding ... | {
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2501.18056 | RL-based Query Rewriting with Distilled LLM for online E-Commerce
Systems | [
"cs.IR"
] | Query rewriting (QR) is a critical technique in e-commerce search, addressing the lexical gap between user queries and product descriptions to enhance search performance. Existing QR approaches typically fall into two categories: discriminative models and generative methods leveraging large language models (LLMs). Disc... | {
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2501.18058 | Power-Efficient Over-the-Air Aggregation with Receive Beamforming for
Federated Learning | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper studies power-efficient uplink transmission design for federated learning (FL) that employs over-the-air analog aggregation and multi-antenna beamforming at the server. We jointly optimize device transmit weights and receive beamforming at each FL communication round to minimize the total device transmit pow... | {
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2501.18059 | Learning the Optimal Stopping for Early Classification within Finite
Horizons via Sequential Probability Ratio Test | [
"cs.LG",
"cs.AI"
] | Time-sensitive machine learning benefits from Sequential Probability Ratio Test (SPRT), which provides an optimal stopping time for early classification of time series. However, in finite horizon scenarios, where input lengths are finite, determining the optimal stopping rule becomes computationally intensive due to th... | {
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2501.18060 | Noise-Adaptive Conformal Classification with Marginal Coverage | [
"stat.ME",
"cs.LG",
"stat.ML"
] | Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated prediction sets with precise coverage guarantees for any classification model. However, its reliance on the idealized assumption of perfect data exchangeability limits its effective... | {
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2501.18062 | FinanceQA: A Benchmark for Evaluating Financial Analysis Capabilities of
Large Language Models | [
"cs.LG",
"cs.CL"
] | FinanceQA is a testing suite that evaluates LLMs' performance on complex numerical financial analysis tasks that mirror real-world investment work. Despite recent advances, current LLMs fail to meet the strict accuracy requirements of financial institutions, with models failing approximately 60% of realistic tasks that... | {
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2501.18063 | Impedance Trajectory Analysis during Power Swing for Grid-Forming
Inverter with Different Current Limiters | [
"eess.SY",
"cs.SY"
] | Grid-forming (GFM) inverter-based resources (IBRs) are capable of emulating the external characteristics of synchronous generators (SGs) through the careful design of the control loops. However, the current limiter in the control loops of the GFM IBR poses challenges to the effectiveness of power swing detection functi... | {
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2501.18064 | Learning Metal Microstructural Heterogeneity through Spatial Mapping of
Diffraction Latent Space Features | [
"cond-mat.mtrl-sci",
"cs.AI"
] | To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors. This need is particularly relevant for metallic... | {
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2501.18071 | Towards Transparent and Accurate Diabetes Prediction Using Machine
Learning and Explainable Artificial Intelligence | [
"cs.LG",
"cs.AI",
"cs.SE"
] | Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a framework for diabetes prediction using Machine Learning (ML) models, complemented with eXplainable Artificial Intelligence (XAI) tools, to investigate both the predictive ... | {
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2501.18075 | Synthesizing Grasps and Regrasps for Complex Manipulation Tasks | [
"cs.RO"
] | In complex manipulation tasks, e.g., manipulation by pivoting, the motion of the object being manipulated has to satisfy path constraints that can change during the motion. Therefore, a single grasp may not be sufficient for the entire path, and the object may need to be regrasped. Additionally, geometric data for obje... | {
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2501.18078 | Statistical Design of Thermal Protection System Using Physics-Informed
Neural Network | [
"cs.CE"
] | Thermal protection systems (TPS) of space vehicles are designed computationally rather than experimentally. They are validated using ground experiments, but all aspects of the flight cannot be replicated on ground. This ground-to-flight mapping introduces uncertainties which need to be accounted for while designing any... | {
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2501.18080 | PAC Codes Meet CRC-Polar Codes | [
"cs.IT",
"math.IT"
] | CRC-Polar codes under SC list decoding are well-regarded for their competitive error performance. This paper examines these codes by focusing on minimum weight codewords, breaking them down into the rows of the polar transform. Inspired by the significant impact of parity check bits and their positions, we apply a shif... | {
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2501.18081 | Normative Evaluation of Large Language Models with Everyday Moral
Dilemmas | [
"cs.AI",
"cs.CY"
] | The rapid adoption of large language models (LLMs) has spurred extensive research into their encoded moral norms and decision-making processes. Much of this research relies on prompting LLMs with survey-style questions to assess how well models are aligned with certain demographic groups, moral beliefs, or political id... | {
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2501.18084 | U-aggregation: Unsupervised Aggregation of Multiple Learning Algorithms | [
"stat.ML",
"cs.LG"
] | Across various domains, the growing advocacy for open science and open-source machine learning has made an increasing number of models publicly available. These models allow practitioners to integrate them into their own contexts, reducing the need for extensive data labeling, training, and calibration. However, select... | {
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2501.18086 | DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints
for Safety-Critical Systems | [
"cs.LG",
"cs.AI",
"cs.RO",
"cs.SY",
"eess.SY"
] | Safe reinforcement learning has traditionally relied on predefined constraint functions to ensure safety in complex real-world tasks, such as autonomous driving. However, defining these functions accurately for varied tasks is a persistent challenge. Recent research highlights the potential of leveraging pre-acquired t... | {
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2501.18089 | ISAM-MTL: Cross-subject multi-task learning model with identifiable
spikes and associative memory networks | [
"cs.NE",
"cs.LG",
"q-bio.NC"
] | Cross-subject variability in EEG degrades performance of current deep learning models, limiting the development of brain-computer interface (BCI). This paper proposes ISAM-MTL, which is a multi-task learning (MTL) EEG classification model based on identifiable spiking (IS) representations and associative memory (AM) ne... | {
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2501.18092 | Learning Provably Improves the Convergence of Gradient Descent | [
"cs.LG",
"math.OC"
] | As a specialized branch of deep learning, Learning to Optimize (L2O) tackles optimization problems by training DNN-based solvers. Despite achieving significant success in various scenarios, such as faster convergence in solving convex optimizations and improved optimality in addressing non-convex cases, there remains a... | {
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2501.18093 | Reward Prediction Error Prioritisation in Experience Replay: The RPE-PER
Method | [
"cs.LG",
"cs.RO"
] | Reinforcement Learning algorithms aim to learn optimal control strategies through iterative interactions with an environment. A critical element in this process is the experience replay buffer, which stores past experiences, allowing the algorithm to learn from a diverse range of interactions rather than just the most ... | {
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2501.18094 | AlphaAdam:Asynchronous Masked Optimization with Dynamic Alpha for
Selective Updates | [
"cs.LG"
] | In the training of large language models (LLMs), updating parameters more efficiently and stably has always been an important challenge. To achieve efficient parameter updates, existing methods usually achieve performance comparable to full parameter updates through methods such as low-dimensional decomposition or laye... | {
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2501.18096 | LLMs can see and hear without any training | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | We present MILS: Multimodal Iterative LLM Solver, a surprisingly simple, training-free approach, to imbue multimodal capabilities into your favorite LLM. Leveraging their innate ability to perform multi-step reasoning, MILS prompts the LLM to generate candidate outputs, each of which are scored and fed back iteratively... | {
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2501.18098 | Disentangling Safe and Unsafe Corruptions via Anisotropy and Locality | [
"cs.CV",
"cs.LG"
] | State-of-the-art machine learning systems are vulnerable to small perturbations to their input, where ``small'' is defined according to a threat model that assigns a positive threat to each perturbation. Most prior works define a task-agnostic, isotropic, and global threat, like the $\ell_p$ norm, where the magnitude o... | {
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2501.18099 | Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge | [
"cs.AI",
"cs.CL"
] | LLM-as-a-Judge models generate chain-of-thought (CoT) sequences intended to capture the step-bystep reasoning process that underlies the final evaluation of a response. However, due to the lack of human annotated CoTs for evaluation, the required components and structure of effective reasoning traces remain understudie... | {
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2501.18100 | Panacea: Mitigating Harmful Fine-tuning for Large Language Models via
Post-fine-tuning Perturbation | [
"cs.CL",
"cs.AI"
] | Harmful fine-tuning attack introduces significant security risks to the fine-tuning services. Mainstream defenses aim to vaccinate the model such that the later harmful fine-tuning attack is less effective. However, our evaluation results show that such defenses are fragile -- with a few fine-tuning steps, the model st... | {
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2501.18101 | Diverse Preference Optimization | [
"cs.CL"
] | Post-training of language models, either through reinforcement learning, preference optimization or supervised finetuning, tends to sharpen the output probability distribution and reduce the diversity of generated responses. This is particularly a problem for creative generative tasks where varied responses are desired... | {
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2501.18103 | Beyond Turn-taking: Introducing Text-based Overlap into Human-LLM
Interactions | [
"cs.HC",
"cs.CL"
] | Traditional text-based human-AI interactions often adhere to a strict turn-taking approach. In this research, we propose a novel approach that incorporates overlapping messages, mirroring natural human conversations. Through a formative study, we observed that even in text-based contexts, users instinctively engage in ... | {
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2501.18107 | Scaling Inference-Efficient Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Scaling laws are powerful tools to predict the performance of large language models. However, current scaling laws fall short of accounting for inference costs. In this work, we first show that model architecture affects inference latency, where models of the same size can have up to 3.5x difference in latency. To tack... | {
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2501.18108 | Investigating an Intelligent System to Monitor \& Explain Abnormal
Activity Patterns of Older Adults | [
"cs.HC",
"cs.AI",
"cs.LG"
] | Despite the growing potential of older adult care technologies, the adoption of these technologies remains challenging. In this work, we conducted a focus-group session with family caregivers to scope designs of the older adult care technology. We then developed a high-fidelity prototype and conducted its qualitative s... | {
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2501.18109 | Influence of High-Performance Image-to-Image Translation Networks on
Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to
MRI Translation in Prostate Cancer | [
"eess.IV",
"cs.CV",
"physics.bio-ph"
] | Purpose: This study examines the core traits of image-to-image translation (I2I) networks, focusing on their effectiveness and adaptability in everyday clinical settings. Methods: We have analyzed data from 794 patients diagnosed with prostate cancer (PCa), using ten prominent 2D/3D I2I networks to convert ultrasound (... | {
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2501.18110 | Lifelong 3D Mapping Framework for Hand-held & Robot-mounted LiDAR
Mapping Systems | [
"cs.RO",
"cs.CV"
] | We propose a lifelong 3D mapping framework that is modular, cloud-native by design and more importantly, works for both hand-held and robot-mounted 3D LiDAR mapping systems. Our proposed framework comprises of dynamic point removal, multi-session map alignment, map change detection and map version control. First, our s... | {
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2501.18112 | ACTGNN: Assessment of Clustering Tendency with Synthetically-Trained
Graph Neural Networks | [
"cs.LG"
] | Determining clustering tendency in datasets is a fundamental but challenging task, especially in noisy or high-dimensional settings where traditional methods, such as the Hopkins Statistic and Visual Assessment of Tendency (VAT), often struggle to produce reliable results. In this paper, we propose ACTGNN, a graph-base... | {
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2501.18114 | DCatalyst: A Unified Accelerated Framework for Decentralized
Optimization | [
"math.OC",
"cs.LG"
] | We study decentralized optimization over a network of agents, modeled as graphs, with no central server. The goal is to minimize $f+r$, where $f$ represents a (strongly) convex function averaging the local agents' losses, and $r$ is a convex, extended-value function. We introduce DCatalyst, a unified black-box framew... | {
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2501.18115 | A spectral clustering-type algorithm for the consistent estimation of
the Hurst distribution in moderately high dimensions | [
"stat.ME",
"cs.LG",
"stat.ML"
] | Scale invariance (fractality) is a prominent feature of the large-scale behavior of many stochastic systems. In this work, we construct an algorithm for the statistical identification of the Hurst distribution (in particular, the scaling exponents) undergirding a high-dimensional fractal system. The algorithm is based ... | {
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2501.18116 | DeepFRC: An End-to-End Deep Learning Model for Functional Registration
and Classification | [
"cs.CV",
"cs.LG",
"stat.ML"
] | Functional data analysis (FDA) is essential for analyzing continuous, high-dimensional data, yet existing methods often decouple functional registration and classification, limiting their efficiency and performance. We present DeepFRC, an end-to-end deep learning framework that unifies these tasks within a single model... | {
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2501.18117 | Improving Minimax Group Fairness in Sequential Recommendation | [
"cs.IR"
] | Training sequential recommenders such as SASRec with uniform sample weights achieves good overall performance but can fall short on specific user groups. One such example is popularity bias, where mainstream users receive better recommendations than niche content viewers. To improve recommendation quality across divers... | {
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2501.18119 | Self-supervised Quantized Representation for Seamlessly Integrating
Knowledge Graphs with Large Language Models | [
"cs.CL",
"cs.AI"
] | Due to the presence of the natural gap between Knowledge Graph (KG) structures and the natural language, the effective integration of holistic structural information of KGs with Large Language Models (LLMs) has emerged as a significant question. To this end, we propose a two-stage framework to learn and apply quantized... | {
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2501.18121 | Optimal Survey Design for Private Mean Estimation | [
"stat.ML",
"cs.CR",
"cs.LG",
"math.ST",
"stat.TH"
] | This work identifies the first privacy-aware stratified sampling scheme that minimizes the variance for general private mean estimation under the Laplace, Discrete Laplace (DLap) and Truncated-Uniform-Laplace (TuLap) mechanisms within the framework of differential privacy (DP). We view stratified sampling as a subsampl... | {
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2501.18122 | VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical
Constraints | [
"cs.LG",
"cs.AI"
] | Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issues in operational forecasting. Regrettably, they exhibit subpar long-term forecasting capabilities. W... | {
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2501.18123 | Battery State of Health Estimation Using LLM Framework | [
"cs.LG",
"eess.SP"
] | Battery health monitoring is critical for the efficient and reliable operation of electric vehicles (EVs). This study introduces a transformer-based framework for estimating the State of Health (SoH) and predicting the Remaining Useful Life (RUL) of lithium titanate (LTO) battery cells by utilizing both cycle-based and... | {
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2501.18124 | REMOTE: Real-time Ego-motion Tracking for Various Endoscopes via
Multimodal Visual Feature Learning | [
"cs.CV",
"cs.AI"
] | Real-time ego-motion tracking for endoscope is a significant task for efficient navigation and robotic automation of endoscopy. In this paper, a novel framework is proposed to perform real-time ego-motion tracking for endoscope. Firstly, a multi-modal visual feature learning network is proposed to perform relative pose... | {
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2501.18126 | HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation
with Hourly Feedback | [
"cs.IR",
"cs.LG"
] | Modern recommendation systems can be broadly divided into two key stages: the ranking stage, where the system predicts various user engagements (e.g., click-through rate, like rate, follow rate, watch time), and the value model stage, which aggregates these predictive scores through a function (e.g., a linear combinati... | {
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2501.18128 | Unraveling the Capabilities of Language Models in News Summarization | [
"cs.CL",
"cs.AI"
] | Given the recent introduction of multiple language models and the ongoing demand for improved Natural Language Processing tasks, particularly summarization, this work provides a comprehensive benchmarking of 20 recent language models, focusing on smaller ones for the news summarization task. In this work, we systematic... | {
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2501.18129 | Revisiting gender bias research in bibliometrics: Standardizing
methodological variability using Scholarly Data Analysis (SoDA) Cards | [
"cs.DL",
"cs.AI",
"cs.SI"
] | Gender biases in scholarly metrics remain a persistent concern, despite numerous bibliometric studies exploring their presence and absence across productivity, impact, acknowledgment, and self-citations. However, methodological inconsistencies, particularly in author name disambiguation and gender identification, limit... | {
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2501.18130 | Waste Animal Bone-derived Calcium Phosphate Particles with High Solar
Reflectance | [
"eess.SY",
"cs.SY"
] | Highly reflective Calcium Phosphate (CAP) nanoparticles have been obtained from waste chicken and porcine bones. Chicken and pork bones have been processed and calcined at temperatures between 600{\deg}C and 1200{\deg}C to remove organic material and resulting in CAP bio-ceramic compounds with high reflectance. The ref... | {
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2501.18131 | Entropy-Synchronized Neural Hashing for Unsupervised Ransomware
Detection | [
"cs.CR",
"cs.AI"
] | Entropy-based detection methodologies have gained significant attention due to their ability to analyze structural irregularities within executable files, particularly in the identification of malicious software employing advanced obfuscation techniques. The Entropy-Synchronized Neural Hashing (ESNH) framework introduc... | {
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} |
2501.18137 | Tensor Completion for Surrogate Modeling of Material Property Prediction | [
"cs.LG",
"cond-mat.mtrl-sci",
"cs.AI"
] | When designing materials to optimize certain properties, there are often many possible configurations of designs that need to be explored. For example, the materials' composition of elements will affect properties such as strength or conductivity, which are necessary to know when developing new materials. Exploring all... | {
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} |
2501.18138 | B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning | [
"cs.LG"
] | Overestimation arising from selecting unseen actions during policy evaluation is a major challenge in offline reinforcement learning (RL). A minimalist approach in the single-agent setting -- adding behavior cloning (BC) regularization to existing online RL algorithms -- has been shown to be effective; however, this ap... | {
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} |
2501.18143 | Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut
Clustering | [
"cs.LG"
] | Min cut is an important graph partitioning method. However, current solutions to the min cut problem suffer from slow speeds, difficulty in solving, and often converge to simple solutions. To address these issues, we relax the min cut problem into a dual-bounded constraint and, for the first time, treat the min cut pro... | {
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} |
2501.18154 | Mixed-Precision Graph Neural Quantization for Low Bit Large Language
Models | [
"cs.CL"
] | Post-Training Quantization (PTQ) is pivotal for deploying large language models (LLMs) within resource-limited settings by significantly reducing resource demands. However, existing PTQ strategies underperform at low bit levels < 3 bits due to the significant difference between the quantized and original weights. To en... | {
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} |
2501.18157 | Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training
and Unimodal Deployment | [
"cs.SD",
"cs.CV",
"cs.MM",
"eess.AS"
] | Building reliable speech systems often requires combining multiple modalities, like audio and visual cues. While such multimodal solutions frequently lead to improvements in performance and may even be critical in certain cases, they come with several constraints such as increased sensory requirements, computational co... | {
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} |
2501.18158 | Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin
Case Study | [
"cs.CR",
"cs.LG"
] | Cryptocurrencies are widely used, yet current methods for analyzing transactions heavily rely on opaque, black-box models. These lack interpretability and adaptability, failing to effectively capture behavioral patterns. Many researchers, including us, believe that Large Language Models (LLMs) could bridge this gap due... | {
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} |
2501.18161 | Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing
Interpretability and Transparency in Deep Learning Models for Skin Cancer
Classification | [
"eess.IV",
"cs.CV"
] | With over 2 million new cases identified annually, skin cancer is the most prevalent type of cancer globally and the second most common in Bangladesh, following breast cancer. Early detection and treatment are crucial for enhancing patient outcomes; however, Bangladesh faces a shortage of dermatologists and qualified m... | {
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} |
2501.18162 | IROAM: Improving Roadside Monocular 3D Object Detection Learning from
Autonomous Vehicle Data Domain | [
"cs.CV",
"cs.RO"
] | In autonomous driving, The perception capabilities of the ego-vehicle can be improved with roadside sensors, which can provide a holistic view of the environment. However, existing monocular detection methods designed for vehicle cameras are not suitable for roadside cameras due to viewpoint domain gaps. To bridge this... | {
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} |
2501.18164 | Faster Convergence of Riemannian Stochastic Gradient Descent with
Increasing Batch Size | [
"cs.LG",
"math.OC",
"stat.ML"
] | Many models used in machine learning have become so large that even computer computation of the full gradient of the loss function is impractical. This has made it necessary to efficiently train models using limited available information, such as batch size and learning rate. We have theoretically analyzed the use of R... | {
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} |
2501.18167 | Scattering approach to diffusion quantifies axonal damage in brain
injury | [
"physics.med-ph",
"cs.CV",
"physics.bio-ph"
] | Early diagnosis and noninvasive monitoring of neurological disorders require sensitivity to elusive cellular-level alterations that occur much earlier than volumetric changes observable with the millimeter-resolution of medical imaging modalities. Morphological changes in axons, such as axonal varicosities or beadings,... | {
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} |
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