id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.03119 | Comparison of the Cox proportional hazards model and Random Survival
Forest algorithm for predicting patient-specific survival probabilities in
clinical trial data | [
"stat.ML",
"cs.LG"
] | The Cox proportional hazards model is often used for model development in data from randomized controlled trials (RCT) with time-to-event outcomes. Random survival forests (RSF) is a machine-learning algorithm known for its high predictive performance. We conduct a comprehensive neutral comparison study to compare the ... | {
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2502.03120 | At the Mahakumbh, Faith Met Tragedy: Computational Analysis of Stampede
Patterns Using Machine Learning and NLP | [
"cs.LG",
"cs.AI",
"cs.CY",
"cs.SI"
] | This study employs machine learning, historical analysis, and natural language processing (NLP) to examine recurring lethal stampedes at Indias mass religious gatherings, focusing on the 2025 Mahakumbh tragedy in Prayagraj (48+ deaths) and its 1954 predecessor (700+ casualties). Through computational modeling of crowd ... | {
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2502.03122 | HiLo: Learning Whole-Body Human-like Locomotion with Motion Tracking
Controller | [
"cs.RO"
] | Deep Reinforcement Learning (RL) has emerged as a promising method to develop humanoid robot locomotion controllers. Despite the robust and stable locomotion demonstrated by previous RL controllers, their behavior often lacks the natural and agile motion patterns necessary for human-centric scenarios. In this work, we ... | {
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2502.03123 | Disentanglement in Difference: Directly Learning Semantically
Disentangled Representations by Maximizing Inter-Factor Differences | [
"cs.LG",
"cs.AI"
] | In this study, Disentanglement in Difference(DiD) is proposed to address the inherent inconsistency between the statistical independence of latent variables and the goal of semantic disentanglement in disentanglement representation learning. Conventional disentanglement methods achieve disentanglement representation by... | {
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2502.03124 | Levelised Cost of Demand Response: Estimating the Cost-Competitiveness
of Flexible Demand | [
"eess.SY",
"cs.SY"
] | To make well-informed investment decisions, energy system stakeholders require reliable cost frameworks for demand response (DR) and storage technologies. While the levelised cost of storage (LCOS) permits comprehensive cost comparisons between different storage technologies, no generic cost measure for the comparison ... | {
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2502.03125 | Double Distillation Network for Multi-Agent Reinforcement Learning | [
"cs.MA",
"cs.LG"
] | Multi-agent reinforcement learning typically employs a centralized training-decentralized execution (CTDE) framework to alleviate the non-stationarity in environment. However, the partial observability during execution may lead to cumulative gap errors gathered by agents, impairing the training of effective collaborati... | {
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2502.03128 | Metis: A Foundation Speech Generation Model with Masked Generative
Pre-training | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS",
"eess.SP"
] | We introduce Metis, a foundation model for unified speech generation. Unlike previous task-specific or multi-task models, Metis follows a pre-training and fine-tuning paradigm. It is pre-trained on large-scale unlabeled speech data using masked generative modeling and then fine-tuned to adapt to diverse speech generati... | {
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2502.03129 | Teaching Large Language Models Number-Focused Headline Generation With
Key Element Rationales | [
"cs.CL",
"cs.LG"
] | Number-focused headline generation is a summarization task requiring both high textual quality and precise numerical accuracy, which poses a unique challenge for Large Language Models (LLMs). Existing studies in the literature focus only on either textual quality or numerical reasoning and thus are inadequate to addres... | {
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2502.03132 | SPARK: A Modular Benchmark for Humanoid Robot Safety | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper introduces the Safe Protective and Assistive Robot Kit (SPARK), a comprehensive benchmark designed to ensure safety in humanoid autonomy and teleoperation. Humanoid robots pose significant safety risks due to their physical capabilities of interacting with complex environments. The physical structures of hum... | {
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2502.03134 | Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for
Intrusion Detection and Security Research | [
"cs.CR",
"cs.AI"
] | In this paper, a dataset of IoT network traffic is presented. Our dataset was generated by utilising the Gotham testbed, an emulated large-scale Internet of Things (IoT) network designed to provide a realistic and heterogeneous environment for network security research. The testbed includes 78 emulated IoT devices oper... | {
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2502.03135 | Underwater Soft Fin Flapping Motion with Deep Neural Network Based
Surrogate Model | [
"cs.RO",
"cs.LG"
] | This study presents a novel framework for precise force control of fin-actuated underwater robots by integrating a deep neural network (DNN)-based surrogate model with reinforcement learning (RL). To address the complex interactions with the underwater environment and the high experimental costs, a DNN surrogate model ... | {
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2502.03139 | Fast Sampling of Cosmological Initial Conditions with Gaussian Neural
Posterior Estimation | [
"astro-ph.CO",
"astro-ph.IM",
"cs.LG"
] | Knowledge of the primordial matter density field from which the large-scale structure of the Universe emerged over cosmic time is of fundamental importance for cosmology. However, reconstructing these cosmological initial conditions from late-time observations is a notoriously difficult task, which requires advanced co... | {
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2502.03143 | Machine Learning-Driven Student Performance Prediction for Enhancing
Tiered Instruction | [
"cs.LG",
"cs.CY"
] | Student performance prediction is one of the most important subjects in educational data mining. As a modern technology, machine learning offers powerful capabilities in feature extraction and data modeling, providing essential support for diverse application scenarios, as evidenced by recent studies confirming its eff... | {
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2502.03144 | Group Trip Planning Query Problem with Multimodal Journey | [
"cs.MA",
"cs.DB",
"cs.DS"
] | In Group Trip Planning (GTP) Query Problem, we are given a city road network where a number of Points of Interest (PoI) have been marked with their respective categories (e.g., Cafeteria, Park, Movie Theater, etc.). A group of agents want to visit one PoI from every category from their respective starting location and ... | {
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2502.03146 | Symmetry-Aware Bayesian Flow Networks for Crystal Generation | [
"cs.LG",
"cond-mat.mtrl-sci"
] | The discovery of new crystalline materials is essential to scientific and technological progress. However, traditional trial-and-error approaches are inefficient due to the vast search space. Recent advancements in machine learning have enabled generative models to predict new stable materials by incorporating structur... | {
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2502.03147 | Scalable In-Context Learning on Tabular Data via Retrieval-Augmented
Large Language Models | [
"cs.CL",
"cs.AI"
] | Recent studies have shown that large language models (LLMs), when customized with post-training on tabular data, can acquire general tabular in-context learning (TabICL) capabilities. These models are able to transfer effectively across diverse data schemas and different task domains. However, existing LLM-based TabICL... | {
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2502.03148 | Abnormal Mutations: Evolution Strategies Don't Require Gaussianity | [
"cs.NE"
] | The mutation process in evolution strategies has been interlinked with the normal distribution since its inception. Many lines of reasoning have been given for this strong dependency, ranging from maximum entropy arguments to the need for isotropy. However, some theoretical results suggest that other distributions migh... | {
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2502.03159 | PICBench: Benchmarking LLMs for Photonic Integrated Circuits Design | [
"cs.LG",
"cs.AR"
] | While large language models (LLMs) have shown remarkable potential in automating various tasks in digital chip design, the field of Photonic Integrated Circuits (PICs)-a promising solution to advanced chip designs-remains relatively unexplored in this context. The design of PICs is time-consuming and prone to errors du... | {
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2502.03162 | Low-Complexity Cram\'er-Rao Lower Bound and Sum Rate Optimization in
ISAC Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | While Cram\'er-Rao lower bound is an important metric in sensing functions in integrated sensing and communications (ISAC) designs, its optimization usually involves a computationally expensive solution such as semidefinite relaxation. In this paper, we aim to develop a low-complexity yet efficient algorithm for CRLB o... | {
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2502.03163 | Signature Reconstruction from Randomized Signatures | [
"math.CA",
"cs.LG",
"math.PR",
"stat.ML"
] | Controlled ordinary differential equations driven by continuous bounded variation curves can be considered a continuous time analogue of recurrent neural networks for the construction of expressive features of the input curves. We ask up to which extent well known signature features of such curves can be reconstructed ... | {
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2502.03183 | MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume
for Enhanced Video Understanding | [
"cs.CV",
"cs.LG"
] | Modern Video Large Language Models (VLLMs) often rely on uniform frame sampling for video understanding, but this approach frequently fails to capture critical information due to frame redundancy and variations in video content. We propose MaxInfo, a training-free method based on the maximum volume principle, which sel... | {
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2502.03188 | Euska\~nolDS: A Naturally Sourced Corpus for Basque-Spanish
Code-Switching | [
"cs.CL",
"cs.AI"
] | Code-switching (CS) remains a significant challenge in Natural Language Processing (NLP), mainly due a lack of relevant data. In the context of the contact between the Basque and Spanish languages in the north of the Iberian Peninsula, CS frequently occurs in both formal and informal spontaneous interactions. However, ... | {
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2502.03198 | SimSort: A Powerful Framework for Spike Sorting by Large-Scale
Electrophysiology Simulation | [
"q-bio.NC",
"cs.LG"
] | Spike sorting is an essential process in neural recording, which identifies and separates electrical signals from individual neurons recorded by electrodes in the brain, enabling researchers to study how specific neurons communicate and process information. Although there exist a number of spike sorting methods which h... | {
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2502.03199 | Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large
Language Models | [
"cs.CL",
"cs.AI"
] | Despite their impressive capacities, Large language models (LLMs) often struggle with the hallucination issue of generating inaccurate or fabricated content even when they possess correct knowledge. In this paper, we extend the exploration of the correlation between hidden-state prediction changes and output factuality... | {
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2502.03200 | CORTEX: A Cost-Sensitive Rule and Tree Extraction Method | [
"cs.AI",
"cs.LG"
] | Tree-based and rule-based machine learning models play pivotal roles in explainable artificial intelligence (XAI) due to their unique ability to provide explanations in the form of tree or rule sets that are easily understandable and interpretable, making them essential for applications in which trust in model decision... | {
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2502.03201 | SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection
with Extremely Limited Labels | [
"cs.LG"
] | Node Anomaly Detection (NAD) has gained significant attention in the deep learning community due to its diverse applications in real-world scenarios. Existing NAD methods primarily embed graphs within a single Euclidean space, while overlooking the potential of non-Euclidean spaces. Besides, to address the prevalent is... | {
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2502.03202 | Low-cost analog signal chain for transmit-receive circuits of passive
induction-based resonators | [
"eess.SP",
"cs.SY",
"eess.SY"
] | Passive wireless sensors are crucial in modern medical and industrial settings to monitor procedures and conditions. We demonstrate a circuit to inductively excite passive resonators and to conduct their decaying signal response to a low noise amplifier. Two design variations of a generic transmit-receive signal chain ... | {
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2502.03206 | A Unified and General Humanoid Whole-Body Controller for Fine-Grained
Locomotion | [
"cs.RO",
"cs.AI"
] | Locomotion is a fundamental skill for humanoid robots. However, most existing works made locomotion a single, tedious, unextendable, and passive movement. This limits the kinematic capabilities of humanoid robots. In contrast, humans possess versatile athletic abilities-running, jumping, hopping, and finely adjusting w... | {
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2502.03207 | MotionAgent: Fine-grained Controllable Video Generation via Motion Field
Agent | [
"cs.CV",
"cs.GR"
] | We propose MotionAgent, enabling fine-grained motion control for text-guided image-to-video generation. The key technique is the motion field agent that converts motion information in text prompts into explicit motion fields, providing flexible and precise motion guidance. Specifically, the agent extracts the object mo... | {
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2502.03210 | From Kernels to Features: A Multi-Scale Adaptive Theory of Feature
Learning | [
"cond-mat.dis-nn",
"cs.LG",
"stat.ML"
] | Theoretically describing feature learning in neural networks is crucial for understanding their expressive power and inductive biases, motivating various approaches. Some approaches describe network behavior after training through a simple change in kernel scale from initialization, resulting in a generalization power ... | {
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2502.03214 | iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMs | [
"cs.CL",
"cs.AI",
"cs.CV"
] | Vision-Language Models (VLMs) are known to struggle with spatial reasoning and visual alignment. To help overcome these limitations, we introduce iVISPAR, an interactive multi-modal benchmark designed to evaluate the spatial reasoning capabilities of VLMs acting as agents. iVISPAR is based on a variant of the sliding t... | {
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2502.03218 | Data Dams: A Novel Framework for Regulating and Managing Data Flow in
Large-Scale Systems | [
"cs.IR",
"cs.DB",
"cs.DC"
] | In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congesti... | {
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2502.03220 | Mitigating Language Bias in Cross-Lingual Job Retrieval: A Recruitment
Platform Perspective | [
"cs.CL"
] | Understanding the textual components of resumes and job postings is critical for improving job-matching accuracy and optimizing job search systems in online recruitment platforms. However, existing works primarily focus on analyzing individual components within this information, requiring multiple specialized tools to ... | {
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2502.03221 | Information Theoretic Analysis of PUF-Based Tamper Protection | [
"cs.IT",
"cs.CR",
"math.IT"
] | Physical Unclonable Functions (PUFs) enable physical tamper protection for high-assurance devices without needing a continuous power supply that is active over the entire lifetime of the device. Several methods for PUF-based tamper protection have been proposed together with practical quantization and error correction ... | {
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2502.03227 | Adversarial Dependence Minimization | [
"cs.LG"
] | Many machine learning techniques rely on minimizing the covariance between output feature dimensions to extract minimally redundant representations from data. However, these methods do not eliminate all dependencies/redundancies, as linearly uncorrelated variables can still exhibit nonlinear relationships. This work pr... | {
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2502.03228 | GARAD-SLAM: 3D GAussian splatting for Real-time Anti Dynamic SLAM | [
"cs.RO",
"cs.CV"
] | The 3D Gaussian Splatting (3DGS)-based SLAM system has garnered widespread attention due to its excellent performance in real-time high-fidelity rendering. However, in real-world environments with dynamic objects, existing 3DGS-based SLAM systems often face mapping errors and tracking drift issues. To address these pro... | {
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2502.03229 | A Unified Framework for Semi-Supervised Image Segmentation and
Registration | [
"cs.CV"
] | Semi-supervised learning, which leverages both annotated and unannotated data, is an efficient approach for medical image segmentation, where obtaining annotations for the whole dataset is time-consuming and costly. Traditional semi-supervised methods primarily focus on extracting features and learning data distributio... | {
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2502.03230 | Efficient Vision Language Model Fine-tuning for Text-based Person
Anomaly Search | [
"cs.CV",
"cs.MM"
] | This paper presents the HFUT-LMC team's solution to the WWW 2025 challenge on Text-based Person Anomaly Search (TPAS). The primary objective of this challenge is to accurately identify pedestrians exhibiting either normal or abnormal behavior within a large library of pedestrian images. Unlike traditional video analysi... | {
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2502.03231 | The Other Side of the Coin: Unveiling the Downsides of Model Aggregation
in Federated Learning from a Layer-peeled Perspective | [
"cs.LG",
"cs.AI"
] | In federated learning (FL), model aggregation is a critical step by which multiple clients share their knowledge with one another. However, it is also widely recognized that the aggregated model, when sent back to each client, performs poorly on local data until after several rounds of local training. This temporary pe... | {
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2502.03232 | JAMMit! Monolithic 3D-Printing of a Bead Jamming Soft Pneumatic Arm | [
"cs.RO"
] | 3D-printed bellow soft pneumatic arms are widely adopted for their flexible design, ease of fabrication, and large deformation capabilities. However, their low stiffness limits their real-world applications. Although several methods exist to enhance the stiffness of soft actuators, many involve complex manufacturing pr... | {
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2502.03236 | Pioneer: Physics-informed Riemannian Graph ODE for Entropy-increasing
Dynamics | [
"cs.LG"
] | Dynamic interacting system modeling is important for understanding and simulating real world systems. The system is typically described as a graph, where multiple objects dynamically interact with each other and evolve over time. In recent years, graph Ordinary Differential Equations (ODE) receive increasing research a... | {
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2502.03238 | Long-tailed Medical Diagnosis with Relation-aware Representation
Learning and Iterative Classifier Calibration | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] | Recently computer-aided diagnosis has demonstrated promising performance, effectively alleviating the workload of clinicians. However, the inherent sample imbalance among different diseases leads algorithms biased to the majority categories, leading to poor performance for rare categories. Existing works formulated thi... | {
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2502.03244 | Analysis of Value Iteration Through Absolute Probability Sequences | [
"cs.LG"
] | Value Iteration is a widely used algorithm for solving Markov Decision Processes (MDPs). While previous studies have extensively analyzed its convergence properties, they primarily focus on convergence with respect to the infinity norm. In this work, we use absolute probability sequences to develop a new line of analys... | {
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2502.03245 | Calibrated Unsupervised Anomaly Detection in Multivariate Time-series
using Reinforcement Learning | [
"cs.LG",
"cs.SY",
"eess.SP",
"eess.SY"
] | This paper investigates unsupervised anomaly detection in multivariate time-series data using reinforcement learning (RL) in the latent space of an autoencoder. A significant challenge is the limited availability of anomalous data, often leading to misclassifying anomalies as normal events, thus raising false negatives... | {
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2502.03251 | RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry | [
"cs.LG"
] | The foundation model has heralded a new era in artificial intelligence, pretraining a single model to offer cross-domain transferability on different datasets. Graph neural networks excel at learning graph data, the omnipresent non-Euclidean structure, but often lack the generalization capacity. Hence, graph foundation... | {
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2502.03252 | A scale of conceptual orality and literacy: Automatic text
categorization in the tradition of "N\"ahe und Distanz" | [
"cs.CL"
] | Koch and Oesterreicher's model of "N\"ahe und Distanz" (N\"ahe = immediacy, conceptual orality; Distanz = distance, conceptual literacy) is constantly used in German linguistics. However, there is no statistical foundation for use in corpus linguistic analyzes, while it is increasingly moving into empirical corpus ling... | {
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2502.03253 | How do Humans and Language Models Reason About Creativity? A Comparative
Analysis | [
"cs.CL"
] | Creativity assessment in science and engineering is increasingly based on both human and AI judgment, but the cognitive processes and biases behind these evaluations remain poorly understood. We conducted two experiments examining how including example solutions with ratings impact creativity evaluation, using a finegr... | {
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2502.03257 | Efficient extraction of medication information from clinical notes: an
evaluation in two languages | [
"cs.CL",
"cs.IR"
] | Objective: To evaluate the accuracy, computational cost and portability of a new Natural Language Processing (NLP) method for extracting medication information from clinical narratives. Materials and Methods: We propose an original transformer-based architecture for the extraction of entities and their relations pertai... | {
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2502.03261 | CARROT: A Cost Aware Rate Optimal Router | [
"stat.ML",
"cs.LG",
"cs.NI",
"math.ST",
"stat.TH"
] | With the rapid growth in the number of Large Language Models (LLMs), there has been a recent interest in LLM routing, or directing queries to the cheapest LLM that can deliver a suitable response. Following this line of work, we introduce CARROT, a Cost AwaRe Rate Optimal rouTer that can select models based on any desi... | {
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2502.03263 | Model Reference-Based Control with Guaranteed Predefined Performance for
Uncertain Strict-Feedback Systems | [
"eess.SY",
"cs.SY"
] | To address the complexities posed by time- and state-varying uncertainties and the computation of analytic derivatives in strict-feedback form (SFF) systems, this study introduces a novel model reference-based control (MRBC) framework which applies locally to each subsystem (SS), to ensure output tracking performance w... | {
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2502.03264 | General Time-series Model for Universal Knowledge Representation of
Multivariate Time-Series data | [
"cs.LG"
] | Universal knowledge representation is a central problem for multivariate time series(MTS) foundation models and yet remains open. This paper investigates this problem from the first principle and it makes four folds of contributions. First, a new empirical finding is revealed: time series with different time granularit... | {
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2502.03266 | ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic
Environments with Vision Foundation Models | [
"cs.CV",
"cs.RO"
] | Service robots operating in unstructured environments must effectively recognize and segment unknown objects to enhance their functionality. Traditional supervised learningbased segmentation techniques require extensive annotated datasets, which are impractical for the diversity of objects encountered in real-world sce... | {
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2502.03270 | When Pre-trained Visual Representations Fall Short: Limitations in
Visuo-Motor Robot Learning | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | The integration of pre-trained visual representations (PVRs) into visuo-motor robot learning has emerged as a promising alternative to training visual encoders from scratch. However, PVRs face critical challenges in the context of policy learning, including temporal entanglement and an inability to generalise even in t... | {
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} |
2502.03272 | Deep Learning Pipeline for Fully Automated Myocardial Infarct
Segmentation from Clinical Cardiac MR Scans | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Purpose: To develop and evaluate a deep learning-based method that allows to perform myocardial infarct segmentation in a fully-automated way. Materials and Methods: For this retrospective study, a cascaded framework of two and three-dimensional convolutional neural networks (CNNs), specialized on identifying ischemi... | {
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2502.03274 | A Scalable Approach to Probabilistic Neuro-Symbolic Verification | [
"cs.AI"
] | Neuro-Symbolic Artificial Intelligence (NeSy AI) has emerged as a promising direction for integrating neural learning with symbolic reasoning. In the probabilistic variant of such systems, a neural network first extracts a set of symbols from sub-symbolic input, which are then used by a symbolic component to reason in ... | {
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2502.03275 | Token Assorted: Mixing Latent and Text Tokens for Improved Language
Model Reasoning | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.LO"
] | Large Language Models (LLMs) excel at reasoning and planning when trained on chainof-thought (CoT) data, where the step-by-step thought process is explicitly outlined by text tokens. However, this results in lengthy inputs where many words support textual coherence rather than core reasoning information, and processing... | {
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2502.03278 | Fault-Tolerant Control for System Availability and Continuous Operation
in Heavy-Duty Wheeled Mobile Robots | [
"eess.SY",
"cs.SY"
] | When the control system in a heavy-duty wheeled mobile robot (HD-WMR) malfunctions, deviations from ideal motion occur, significantly heightening the risks of off-road instability and costly damage. To meet the demands for safety, reliability, and controllability in HD-WMRs, the control system must tolerate faults to a... | {
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2502.03283 | SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex
Reasoning over Knowledge Graphs | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Recent advancements have highlighted that Large Language Models (LLMs) are prone to hallucinations when solving complex reasoning problems, leading to erroneous results. To tackle this issue, researchers incorporate Knowledge Graphs (KGs) to improve the reasoning ability of LLMs. However, existing methods face two limi... | {
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2502.03285 | Deep Learning-based Event Data Coding: A Joint Spatiotemporal and
Polarity Solution | [
"cs.CV",
"eess.IV"
] | Neuromorphic vision sensors, commonly referred to as event cameras, have recently gained relevance for applications requiring high-speed, high dynamic range and low-latency data acquisition. Unlike traditional frame-based cameras that capture 2D images, event cameras generate a massive number of pixel-level events, com... | {
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2502.03286 | Conditional Prediction by Simulation for Automated Driving | [
"cs.RO",
"cs.CV"
] | Modular automated driving systems commonly handle prediction and planning as sequential, separate tasks, thereby prohibiting cooperative maneuvers. To enable cooperative planning, this work introduces a prediction model that models the conditional dependencies between trajectories. For this, predictions are generated b... | {
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2502.03287 | STEMS: Spatial-Temporal Mapping Tool For Spiking Neural Networks | [
"cs.NE",
"cs.AI",
"cs.AR",
"cs.DC"
] | Spiking Neural Networks (SNNs) are promising bio-inspired third-generation neural networks. Recent research has trained deep SNN models with accuracy on par with Artificial Neural Networks (ANNs). Although the event-driven and sparse nature of SNNs show potential for more energy efficient computation than ANNs, SNN neu... | {
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2502.03292 | ALPET: Active Few-shot Learning for Citation Worthiness Detection in
Low-Resource Wikipedia Languages | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Citation Worthiness Detection (CWD) consists in determining which sentences, within an article or collection, should be backed up with a citation to validate the information it provides. This study, introduces ALPET, a framework combining Active Learning (AL) and Pattern-Exploiting Training (PET), to enhance CWD for la... | {
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2502.03297 | IRIS: An Immersive Robot Interaction System | [
"cs.RO",
"cs.LG"
] | This paper introduces IRIS, an immersive Robot Interaction System leveraging Extended Reality (XR), designed for robot data collection and interaction across multiple simulators, benchmarks, and real-world scenarios. While existing XR-based data collection systems provide efficient and intuitive solutions for large-sca... | {
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2502.03298 | MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge
Letters | [
"cs.CL",
"cs.AI",
"cs.LG"
] | While increasing patients' access to medical documents improves medical care, this benefit is limited by varying health literacy levels and complex medical terminology. Large language models (LLMs) offer solutions by simplifying medical information. However, evaluating LLMs for safe and patient-friendly text generation... | {
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2502.03302 | MAP Image Recovery with Guarantees using Locally Convex Multi-Scale
Energy (LC-MUSE) Model | [
"cs.LG",
"cs.CV",
"eess.IV"
] | We propose a multi-scale deep energy model that is strongly convex in the local neighbourhood around the data manifold to represent its probability density, with application in inverse problems. In particular, we represent the negative log-prior as a multi-scale energy model parameterized by a Convolutional Neural Netw... | {
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2502.03304 | Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient
Zeroth-order LLM Fine-tuning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large language models (LLMs) excel across various tasks, but standard first-order (FO) fine-tuning demands considerable memory, significantly limiting real-world deployment. Recently, zeroth-order (ZO) optimization stood out as a promising memory-efficient training paradigm, avoiding backward passes and relying solely ... | {
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2502.03307 | Intent Alignment between Interaction and Language Spaces for
Recommendation | [
"cs.IR"
] | Intent-based recommender systems have garnered significant attention for uncovering latent fine-grained preferences. Intents, as underlying factors of interactions, are crucial for improving recommendation interpretability. Most methods define intents as learnable parameters updated alongside interactions. However, exi... | {
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2502.03317 | Contact-Aware Motion Planning Among Movable Objects | [
"cs.RO"
] | Most existing methods for motion planning of mobile robots involve generating collision-free trajectories. However, these methods focusing solely on contact avoidance may limit the robots' locomotion and can not be applied to tasks where contact is inevitable or intentional. To address these issues, we propose a novel ... | {
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2502.03321 | Simplifying Formal Proof-Generating Models with ChatGPT and Basic
Searching Techniques | [
"cs.LO",
"cs.AI"
] | The challenge of formal proof generation has a rich history, but with modern techniques, we may finally be at the stage of making actual progress in real-life mathematical problems. This paper explores the integration of ChatGPT and basic searching techniques to simplify generating formal proofs, with a particular focu... | {
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2502.03322 | An efficient end-to-end computational framework for the generation of
ECG calibrated volumetric models of human atrial electrophysiology | [
"math.NA",
"cs.CE",
"cs.NA",
"q-bio.TO"
] | Computational models of atrial electrophysiology (EP) are increasingly utilized for applications such as the development of advanced mapping systems, personalized clinical therapy planning, and the generation of virtual cohorts and digital twins. These models have the potential to establish robust causal links between ... | {
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2502.03323 | Out-of-Distribution Detection using Synthetic Data Generation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Distinguishing in- and out-of-distribution (OOD) inputs is crucial for reliable deployment of classification systems. However, OOD data is typically unavailable or difficult to collect, posing a significant challenge for accurate OOD detection. In this work, we present a method that harnesses the generative capabilitie... | {
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2502.03325 | ECM: A Unified Electronic Circuit Model for Explaining the Emergence of
In-Context Learning and Chain-of-Thought in Large Language Model | [
"cs.CL",
"cs.AI"
] | Recent advancements in large language models (LLMs) have led to significant successes across various applications, where the most noticeable is to a series of emerging capabilities, particularly in the areas of In-Context Learning (ICL) and Chain-of-Thought (CoT). To better understand and control model performance, man... | {
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2502.03327 | Is In-Context Universality Enough? MLPs are Also Universal In-Context | [
"stat.ML",
"cs.LG",
"cs.NA",
"cs.NE",
"math.NA",
"math.PR"
] | The success of transformers is often linked to their ability to perform in-context learning. Recent work shows that transformers are universal in context, capable of approximating any real-valued continuous function of a context (a probability measure over $\mathcal{X}\subseteq \mathbb{R}^d$) and a query $x\in \mathcal... | {
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2502.03330 | Controllable GUI Exploration | [
"cs.HC",
"cs.AI",
"cs.CV",
"cs.GR"
] | During the early stages of interface design, designers need to produce multiple sketches to explore a design space. Design tools often fail to support this critical stage, because they insist on specifying more details than necessary. Although recent advances in generative AI have raised hopes of solving this issue, in... | {
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2502.03332 | A Mixture-Based Framework for Guiding Diffusion Models | [
"stat.ML",
"cs.LG"
] | Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems. Recent approaches use pre-trained diffusion models as priors to solve a wide range of such problems, only leveraging inference-time compute and thereby eliminating the need to retrain task-specific models on the same ... | {
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2502.03333 | RadVLM: A Multitask Conversational Vision-Language Model for Radiology | [
"cs.CV",
"cs.AI"
] | The widespread use of chest X-rays (CXRs), coupled with a shortage of radiologists, has driven growing interest in automated CXR analysis and AI-assisted reporting. While existing vision-language models (VLMs) show promise in specific tasks such as report generation or abnormality detection, they often lack support for... | {
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2502.03335 | Actions Speak Louder Than Words: Rate-Reward Trade-off in Markov
Decision Processes | [
"cs.IT",
"math.IT"
] | The impact of communication on decision-making systems has been extensively studied under the assumption of dedicated communication channels. We instead consider communicating through actions, where the message is embedded into the actions of an agent which interacts with the environment in a Markov decision process (M... | {
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2502.03338 | Optimal PMU Placement for Kalman Filtering of DAE Power System Models | [
"eess.SY",
"cs.SY"
] | Optimal sensor placement is essential for minimizing costs and ensuring accurate state estimation in power systems. This paper introduces a novel method for optimal sensor placement for dynamic state estimation of power systems modeled by differential-algebraic equations. The method identifies optimal sensor locations ... | {
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2502.03340 | Interaction-Aware Gaussian Weighting for Clustered Federated Learning | [
"cs.LG"
] | Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy. However, conventional FL struggles with data heterogeneity and class imbalance, which degrade model performance. Clustered FL balances personalization and decentralized training by grouping clients with analogous data ... | {
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2502.03341 | Adaptive Variational Inference in Probabilistic Graphical Models: Beyond
Bethe, Tree-Reweighted, and Convex Free Energies | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Variational inference in probabilistic graphical models aims to approximate fundamental quantities such as marginal distributions and the partition function. Popular approaches are the Bethe approximation, tree-reweighted, and other types of convex free energies. These approximations are efficient but can fail if the m... | {
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2502.03346 | Implicit Communication in Human-Robot Collaborative Transport | [
"cs.RO"
] | We focus on human-robot collaborative transport, in which a robot and a user collaboratively move an object to a goal pose. In the absence of explicit communication, this problem is challenging because it demands tight implicit coordination between two heterogeneous agents, who have very different sensing, actuation, a... | {
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2502.03347 | DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday
Life Behavior Modeling | [
"cs.CY",
"cs.SI"
] | Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experience in mobile apps to enabling appropriate interventions in digital health apps. Towards this goal, previous studies have relied on datasets ... | {
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2502.03349 | Robust Autonomy Emerges from Self-Play | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Self-play has powered breakthroughs in two-player and multi-player games. Here we show that self-play is a surprisingly effective strategy in another domain. We show that robust and naturalistic driving emerges entirely from self-play in simulation at unprecedented scale -- 1.6~billion~km of driving. This is enabled by... | {
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2502.03350 | Optimal Task Order for Continual Learning of Multiple Tasks | [
"stat.ML",
"cs.LG"
] | Continual learning of multiple tasks remains a major challenge for neural networks. Here, we investigate how task order influences continual learning and propose a strategy for optimizing it. Leveraging a linear teacher-student model with latent factors, we derive an analytical expression relating task similarity and o... | {
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2502.03356 | Inverse Mixed Strategy Games with Generative Trajectory Models | [
"cs.RO"
] | Game-theoretic models are effective tools for modeling multi-agent interactions, especially when robots need to coordinate with humans. However, applying these models requires inferring their specifications from observed behaviors -- a challenging task known as the inverse game problem. Existing inverse game approaches... | {
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2502.03358 | Minerva: A Programmable Memory Test Benchmark for Language Models | [
"cs.CL"
] | How effectively can LLM-based AI assistants utilize their memory (context) to perform various tasks? Traditional data benchmarks, which are often manually crafted, suffer from several limitations: they are static, susceptible to overfitting, difficult to interpret, and lack actionable insights--failing to pinpoint the ... | {
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2502.03359 | GHOST: Gaussian Hypothesis Open-Set Technique | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Evaluations of large-scale recognition methods typically focus on overall performance. While this approach is common, it often fails to provide insights into performance across individual classes, which can lead to fairness issues and misrepresentation. Addressing these gaps is crucial for accurately assessing how well... | {
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2502.03360 | A Beam's Eye View to Fluence Maps 3D Network for Ultra Fast VMAT
Radiotherapy Planning | [
"eess.IV",
"cs.AI",
"physics.med-ph"
] | Volumetric Modulated Arc Therapy (VMAT) revolutionizes cancer treatment by precisely delivering radiation while sparing healthy tissues. Fluence maps generation, crucial in VMAT planning, traditionally involves complex and iterative, and thus time consuming processes. These fluence maps are subsequently leveraged for l... | {
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2502.03364 | Scaling laws in wearable human activity recognition | [
"cs.LG"
] | Many deep architectures and self-supervised pre-training techniques have been proposed for human activity recognition (HAR) from wearable multimodal sensors. Scaling laws have the potential to help move towards more principled design by linking model capacity with pre-training data volume. Yet, scaling laws have not be... | {
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2502.03365 | A Match Made in Heaven? Matching Test Cases and Vulnerabilities With the
VUTECO Approach | [
"cs.SE",
"cs.CR",
"cs.LG"
] | Software vulnerabilities are commonly detected via static analysis, penetration testing, and fuzzing. They can also be found by running unit tests - so-called vulnerability-witnessing tests - that stimulate the security-sensitive behavior with crafted inputs. Developing such tests is difficult and time-consuming; thus,... | {
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2502.03366 | Rethinking Approximate Gaussian Inference in Classification | [
"cs.LG",
"stat.ML"
] | In classification tasks, softmax functions are ubiquitously used as output activations to produce predictive probabilities. Such outputs only capture aleatoric uncertainty. To capture epistemic uncertainty, approximate Gaussian inference methods have been proposed, which output Gaussian distributions over the logit spa... | {
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} |
2502.03367 | SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and
Parsimonious Model Discovery in Science and Beyond | [
"cs.LG"
] | Symbolic regression (SR) is an emerging branch of machine learning focused on discovering simple and interpretable mathematical expressions from data. Although a wide-variety of SR methods have been developed, they often face challenges such as high computational cost, poor scalability with respect to the number of inp... | {
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2502.03368 | PalimpChat: Declarative and Interactive AI analytics | [
"cs.AI",
"cs.DB",
"cs.IR"
] | Thanks to the advances in generative architectures and large language models, data scientists can now code pipelines of machine-learning operations to process large collections of unstructured data. Recent progress has seen the rise of declarative AI frameworks (e.g., Palimpzest, Lotus, and DocETL) to build optimized a... | {
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} |
2502.03369 | Learning from Active Human Involvement through Proxy Value Propagation | [
"cs.AI",
"cs.RO"
] | Learning from active human involvement enables the human subject to actively intervene and demonstrate to the AI agent during training. The interaction and corrective feedback from human brings safety and AI alignment to the learning process. In this work, we propose a new reward-free active human involvement method ca... | {
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} |
2502.03370 | Deep Learning-Based Approach for Identification of Potato Leaf Diseases
Using Wrapper Feature Selection and Feature Concatenation | [
"cs.CV",
"cs.LG"
] | The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent lea... | {
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} |
2502.03373 | Demystifying Long Chain-of-Thought Reasoning in LLMs | [
"cs.CL",
"cs.LG"
] | Scaling inference compute enhances reasoning in large language models (LLMs), with long chains-of-thought (CoTs) enabling strategies like backtracking and error correction. Reinforcement learning (RL) has emerged as a crucial method for developing these capabilities, yet the conditions under which long CoTs emerge rema... | {
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} |
2502.03375 | Interactive Visualization Recommendation with Hier-SUCB | [
"cs.IR"
] | Visualization recommendation aims to enable rapid visual analysis of massive datasets. In real-world scenarios, it is essential to quickly gather and comprehend user preferences to cover users from diverse backgrounds, including varying skill levels and analytical tasks. Previous approaches to personalized visualizatio... | {
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} |
2502.03376 | Ethical Considerations for the Military Use of Artificial Intelligence
in Visual Reconnaissance | [
"cs.CY",
"cs.CV"
] | This white paper underscores the critical importance of responsibly deploying Artificial Intelligence (AI) in military contexts, emphasizing a commitment to ethical and legal standards. The evolving role of AI in the military goes beyond mere technical applications, necessitating a framework grounded in ethical princip... | {
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} |
2502.03377 | Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement
Learning Approach | [
"cs.NI",
"cs.LG"
] | With the rapid development of next-generation Internet of Things (NG-IoT) networks, the increasing number of connected devices has led to a surge in power consumption. This rise in energy demand poses significant challenges to resource availability and raises sustainability concerns for large-scale IoT deployments. Eff... | {
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} |
2502.03381 | Integrating automatic speech recognition into remote healthcare
interpreting: A pilot study of its impact on interpreting quality | [
"cs.CL"
] | This paper reports on the results from a pilot study investigating the impact of automatic speech recognition (ASR) technology on interpreting quality in remote healthcare interpreting settings. Employing a within-subjects experiment design with four randomised conditions, this study utilises scripted medical consultat... | {
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