id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.02623 | Sample Complexity of Bias Detection with Subsampled Point-to-Subspace
Distances | [
"cs.LG",
"cs.AI",
"math.ST",
"stat.TH"
] | Sample complexity of bias estimation is a lower bound on the runtime of any bias detection method. Many regulatory frameworks require the bias to be tested for all subgroups, whose number grows exponentially with the number of protected attributes. Unless one wishes to run a bias detection with a doubly-exponential run... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02624 | Muographic Image Upsampling with Machine Learning for Built
Infrastructure Applications | [
"eess.IV",
"cs.CV"
] | The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting interactions ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02625 | Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers | [
"cs.LG",
"quant-ph"
] | Parameter shift rules (PSRs) are key techniques for efficient gradient estimation in variational quantum eigensolvers (VQEs). In this paper, we propose its Bayesian variant, where Gaussian processes with appropriate kernels are used to estimate the gradient of the VQE objective. Our Bayesian PSR offers flexible gradien... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02628 | e-SimFT: Alignment of Generative Models with Simulation Feedback for
Pareto-Front Design Exploration | [
"cs.LG",
"cs.AI"
] | Deep generative models have recently shown success in solving complex engineering design problems where models predict solutions that address the design requirements specified as input. However, there remains a challenge in aligning such models for effective design exploration. For many design problems, finding a solut... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02629 | Graph Structure Learning for Tumor Microenvironment with Cell Type
Annotation from non-spatial scRNA-seq data | [
"q-bio.GN",
"cs.AI",
"cs.LG"
] | The exploration of cellular heterogeneity within the tumor microenvironment (TME) via single-cell RNA sequencing (scRNA-seq) is essential for understanding cancer progression and response to therapy. Current scRNA-seq approaches, however, lack spatial context and rely on incomplete datasets of ligand-receptor interacti... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02630 | scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based
Prediction for Alzheimer's Disease Diagnosis | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | Functional MRI (fMRI) and single-cell transcriptomics are pivotal in Alzheimer's disease (AD) research, each providing unique insights into neural function and molecular mechanisms. However, integrating these complementary modalities remains largely unexplored. Here, we introduce scBIT, a novel method for enhancing AD ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02631 | ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | The optimal bit-width for achieving the best trade-off between quantized model size and accuracy has been a subject of ongoing debate. While some advocate for 4-bit quantization, others propose that 1.58-bit offers superior results. However, the lack of a cohesive framework for different bits has left such conclusions ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02649 | Fully Autonomous AI Agents Should Not be Developed | [
"cs.AI"
] | This paper argues that fully autonomous AI agents should not be developed. In support of this position, we build from prior scientific literature and current product marketing to delineate different AI agent levels and detail the ethical values at play in each, documenting trade-offs in potential benefits and risks. Ou... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02657 | SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with
Uncertainty Quantification | [
"cs.RO",
"cs.CV"
] | We present a neural radiance field (NeRF) based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photorealistic texture. Our system adopts the state-of-the-art NeRF representation to additionally incorporate lidar. Ad... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02659 | A Training-Free Length Extrapolation Approach for LLMs: Greedy Attention
Logit Interpolation (GALI) | [
"cs.CL",
"cs.AI"
] | Transformer-based Large Language Models (LLMs) struggle to process inputs exceeding their training context window, with performance degrading due to positional out-of-distribution (O.O.D.) that disrupt attention computations. Existing solutions, fine-tuning and training-free methods, are limited by computational ineffi... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02663 | Learning to Double Guess: An Active Perception Approach for Estimating
the Center of Mass of Arbitrary Objects | [
"cs.RO",
"cs.LG"
] | Manipulating arbitrary objects in unstructured environments is a significant challenge in robotics, primarily due to difficulties in determining an object's center of mass. This paper introduces U-GRAPH: Uncertainty-Guided Rotational Active Perception with Haptics, a novel framework to enhance the center of mass estima... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02664 | Differentiable Composite Neural Signed Distance Fields for Robot
Navigation in Dynamic Indoor Environments | [
"cs.RO"
] | Neural Signed Distance Fields (SDFs) provide a differentiable environment representation to readily obtain collision checks and well-defined gradients for robot navigation tasks. However, updating neural SDFs as the scene evolves entails re-training, which is tedious, time consuming, and inefficient, making it unsuitab... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02666 | Deep Reinforcement Learning Enabled Persistent Surveillance with
Energy-Aware UAV-UGV Systems for Disaster Management Applications | [
"cs.RO"
] | Integrating Unmanned Aerial Vehicles (UAVs) with Unmanned Ground Vehicles (UGVs) provides an effective solution for persistent surveillance in disaster management. UAVs excel at covering large areas rapidly, but their range is limited by battery capacity. UGVs, though slower, can carry larger batteries for extended mis... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02668 | Recovering Imbalanced Clusters via Gradient-Based Projection Pursuit | [
"cs.LG"
] | Projection Pursuit is a classic exploratory technique for finding interesting projections of a dataset. We propose a method for recovering projections containing either Imbalanced Clusters or a Bernoulli-Rademacher distribution using a gradient-based technique to optimize the projection index. As sample complexity is a... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02669 | Distributed Prescribed-Time Observer for Nonlinear Systems | [
"eess.SY",
"cs.SY"
] | This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite limited local sensor measurements. ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2502.02671 | On Teacher Hacking in Language Model Distillation | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.ML"
] | Post-training of language models (LMs) increasingly relies on the following two stages: (i) knowledge distillation, where the LM is trained to imitate a larger teacher LM, and (ii) reinforcement learning from human feedback (RLHF), where the LM is aligned by optimizing a reward model. In the second RLHF stage, a well-k... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02672 | Transformers Boost the Performance of Decision Trees on Tabular Data
across Sample Sizes | [
"cs.CL",
"cs.LG"
] | Large language models (LLMs) perform remarkably well on tabular datasets in zero- and few-shot settings, since they can extract meaning from natural language column headers that describe features and labels. Similarly, TabPFN, a recent non-LLM transformer pretrained on numerous tables for in-context learning, has demon... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02673 | MedRAX: Medical Reasoning Agent for Chest X-ray | [
"cs.LG",
"cs.AI",
"cs.MA"
] | Chest X-rays (CXRs) play an integral role in driving critical decisions in disease management and patient care. While recent innovations have led to specialized models for various CXR interpretation tasks, these solutions often operate in isolation, limiting their practical utility in clinical practice. We present MedR... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 1,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02676 | Blind Visible Watermark Removal with Morphological Dilation | [
"cs.CV",
"cs.CR",
"cs.LG"
] | Visible watermarks pose significant challenges for image restoration techniques, especially when the target background is unknown. Toward this end, we present MorphoMod, a novel method for automated visible watermark removal that operates in a blind setting -- without requiring target images. Unlike existing methods, M... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02679 | Networks with Finite VC Dimension: Pro and Contra | [
"stat.ML",
"cs.LG"
] | Approximation and learning of classifiers of large data sets by neural networks in terms of high-dimensional geometry and statistical learning theory are investigated. The influence of the VC dimension of sets of input-output functions of networks on approximation capabilities is compared with its influence on consiste... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02681 | Building Bridges between Users and Content across Multiple Platforms
during Natural Disasters | [
"cs.SI"
] | Social media is a primary medium for information diffusion during natural disasters. The social media ecosystem has been used to identify destruction, analyze opinions and organize aid. While the overall picture and aggregate trends may be important, a crucial part of the picture is the connections on these sites. Thes... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2502.02682 | Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning
from Limited Data | [
"cs.LG",
"physics.comp-ph"
] | Neural operators have shown great potential in surrogate modeling. However, training a well-performing neural operator typically requires a substantial amount of data, which can pose a major challenge in complex applications. In such scenarios, detailed physical knowledge can be unavailable or difficult to obtain, and ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02683 | Streaming Speaker Change Detection and Gender Classification for
Transducer-Based Multi-Talker Speech Translation | [
"cs.SD",
"cs.AI",
"cs.CL",
"eess.AS"
] | Streaming multi-talker speech translation is a task that involves not only generating accurate and fluent translations with low latency but also recognizing when a speaker change occurs and what the speaker's gender is. Speaker change information can be used to create audio prompts for a zero-shot text-to-speech system... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 1,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02684 | Three-dimensional signal processing: a new approach in dynamical
sampling via tensor products | [
"eess.SP",
"cs.IT",
"cs.LG",
"math.IT"
] | The dynamical sampling problem is centered around reconstructing signals that evolve over time according to a dynamical process, from spatial-temporal samples that may be noisy. This topic has been thoroughly explored for one-dimensional signals. Multidimensional signal recovery has also been studied, but primarily in ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02685 | A Methodology for Process Design Kit Re-Centering Using TCAD and
Experimental Data for Cryogenic Temperatures | [
"physics.app-ph",
"cond-mat.mes-hall",
"cs.SY",
"eess.SY"
] | In this work, we describe and demonstrate a novel Technology Computer Aided Design (TCAD) driven methodology that allows measurement data from 'non-ideal' silicon wafers to be used for re-centering a room temperature-based Process Design Kit (PDK) to cryogenic temperatures. This comprehensive approach holds promise for... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2502.02687 | NDKF: A Neural-Enhanced Distributed Kalman Filter for Nonlinear
Multi-Sensor Estimation | [
"eess.SY",
"cs.SY"
] | We propose a Neural-Enhanced Distributed Kalman Filter (NDKF) for multi-sensor state estimation in nonlinear systems. Unlike traditional Kalman filters that rely on explicit, linear models and centralized data fusion, the NDKF leverages neural networks to learn both the system dynamics and measurement functions directl... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2502.02688 | Efficient Implementation of the Global Cardinality Constraint with Costs | [
"cs.AI",
"cs.DS"
] | The success of Constraint Programming relies partly on the global constraints and implementation of the associated filtering algorithms. Recently, new ideas emerged to improve these implementations in practice, especially regarding the all different constraint. In this paper, we consider the cardinality constraint with... | {
"Other": 1,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02689 | Multidimensional Swarm Flight Approach For Chasing Unauthorized UAVs
Leveraging Asynchronous Deep Learning | [
"eess.SY",
"cs.SY"
] | This paper introduces a novel unmanned aerial vehicles (UAV) chasing system designed to track and chase unauthorized UAVs, significantly enhancing their neutralization effectiveness. | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2502.02690 | Controllable Video Generation with Provable Disentanglement | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Controllable video generation remains a significant challenge, despite recent advances in generating high-quality and consistent videos. Most existing methods for controlling video generation treat the video as a whole, neglecting intricate fine-grained spatiotemporal relationships, which limits both control precision ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02692 | Intelligent Sensing-to-Action for Robust Autonomy at the Edge:
Opportunities and Challenges | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Autonomous edge computing in robotics, smart cities, and autonomous vehicles relies on the seamless integration of sensing, processing, and actuation for real-time decision-making in dynamic environments. At its core is the sensing-to-action loop, which iteratively aligns sensor inputs with computational models to driv... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02696 | How Inclusively do LMs Perceive Social and Moral Norms? | [
"cs.CL"
] | This paper discusses and contains offensive content. Language models (LMs) are used in decision-making systems and as interactive assistants. However, how well do these models making judgements align with the diversity of human values, particularly regarding social and moral norms? In this work, we investigate how incl... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02700 | Scalable Higher Resolution Polar Sea Ice Classification and Freeboard
Calculation from ICESat-2 ATL03 Data | [
"cs.LG"
] | ICESat-2 (IS2) by NASA is an Earth-observing satellite that measures high-resolution surface elevation. The IS2's ATL07 and ATL10 sea ice elevation and freeboard products of 10m-200m segments which aggregated 150 signal photons from the raw ATL03 (geolocated photon) data. These aggregated products can potentially overe... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02701 | Practically Effective Adjustment Variable Selection in Causal Inference | [
"cs.LG",
"cs.AI",
"physics.data-an",
"stat.ME"
] | In the estimation of causal effects, one common method for removing the influence of confounders is to adjust the variables that satisfy the back-door criterion. However, it is not always possible to uniquely determine sets of such variables. Moreover, real-world data is almost always limited, which means it may be ins... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02703 | Developing multilingual speech synthesis system for Ojibwe, Mi'kmaq, and
Maliseet | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] | We present lightweight flow matching multilingual text-to-speech (TTS) systems for Ojibwe, Mi'kmaq, and Maliseet, three Indigenous languages in North America. Our results show that training a multilingual TTS model on three typologically similar languages can improve the performance over monolingual models, especially ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 1,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02705 | Rapidly Adapting Policies to the Real World via Simulation-Guided
Fine-Tuning | [
"cs.RO",
"cs.LG"
] | Robot learning requires a considerable amount of high-quality data to realize the promise of generalization. However, large data sets are costly to collect in the real world. Physics simulators can cheaply generate vast data sets with broad coverage over states, actions, and environments. However, physics engines are f... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02707 | Multiple Instance Learning with Coarse-to-Fine Self-Distillation | [
"cs.CV"
] | Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervision is typically provided only at the bag level. In this work, we present PathMIL, a framework designed to improve MIL through two perspectives: (1) employing instance-level... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02709 | Enforcing Demographic Coherence: A Harms Aware Framework for Reasoning
about Private Data Release | [
"cs.CR",
"cs.DB"
] | The technical literature about data privacy largely consists of two complementary approaches: formal definitions of conditions sufficient for privacy preservation and attacks that demonstrate privacy breaches. Differential privacy is an accepted standard in the former sphere. However, differential privacy's powerful ad... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 1,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02710 | Achievable distributional robustness when the robust risk is only
partially identified | [
"stat.ML",
"cs.LG"
] | In safety-critical applications, machine learning models should generalize well under worst-case distribution shifts, that is, have a small robust risk. Invariance-based algorithms can provably take advantage of structural assumptions on the shifts when the training distributions are heterogeneous enough to identify th... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02711 | Tensor Network Structure Search Using Program Synthesis | [
"cs.CE",
"cs.PL"
] | Tensor networks provide a powerful framework for compressing multi-dimensional data. The optimal tensor network structure for a given data tensor depends on both the inherent data properties and the specific optimality criteria, making tensor network structure search a crucial research problem. Existing solutions typic... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 1,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02715 | An Analysis of LLM Fine-Tuning and Few-Shot Learning for Flaky Test
Detection and Classification | [
"cs.SE",
"cs.AI"
] | Flaky tests exhibit non-deterministic behavior during execution and they may pass or fail without any changes to the program under test. Detecting and classifying these flaky tests is crucial for maintaining the robustness of automated test suites and ensuring the overall reliability and confidence in the testing. Howe... | {
"Other": 1,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02716 | A Unified Understanding and Evaluation of Steering Methods | [
"cs.LG",
"cs.CL"
] | Steering methods provide a practical approach to controlling large language models by applying steering vectors to intermediate activations, guiding outputs toward desired behaviors while avoiding retraining. Despite their growing importance, the field lacks a unified understanding and consistent evaluation across task... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02717 | Astromer 2 | [
"astro-ph.IM",
"cs.AI",
"cs.LG"
] | Foundational models have emerged as a powerful paradigm in deep learning field, leveraging their capacity to learn robust representations from large-scale datasets and effectively to diverse downstream applications such as classification. In this paper, we present Astromer 2 a foundational model specifically designed f... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02719 | Beyond Topological Self-Explainable GNNs: A Formal Explainability
Perspective | [
"cs.LG"
] | Self-Explainable Graph Neural Networks (SE-GNNs) are popular explainable-by-design GNNs, but the properties and the limitations of their explanations are not well understood. Our first contribution fills this gap by formalizing the explanations extracted by SE-GNNs, referred to as Trivial Explanations (TEs), and compar... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02722 | Cross-Lingual Transfer for Low-Resource Natural Language Processing | [
"cs.CL"
] | Natural Language Processing (NLP) has seen remarkable advances in recent years, particularly with the emergence of Large Language Models that have achieved unprecedented performance across many tasks. However, these developments have mainly benefited a small number of high-resource languages such as English. The majori... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02723 | Dobi-SVD: Differentiable SVD for LLM Compression and Some New
Perspectives | [
"cs.LG"
] | We provide a new LLM-compression solution via SVD, unlocking new possibilities for LLM compression beyond quantization and pruning. We point out that the optimal use of SVD lies in truncating activations, rather than merely using activations as an optimization distance. Building on this principle, we address three crit... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02725 | The Design of On-Body Robots for Older Adults | [
"cs.RO",
"cs.HC"
] | Wearable technology has significantly improved the quality of life for older adults, and the emergence of on-body, movable robots presents new opportunities to further enhance well-being. Yet, the interaction design for these robots remains under-explored, particularly from the perspective of older adults. We present f... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02727 | Parameter Tracking in Federated Learning with Adaptive Optimization | [
"cs.LG",
"cs.AI",
"cs.DC"
] | In Federated Learning (FL), model training performance is strongly impacted by data heterogeneity across clients. Gradient Tracking (GT) has recently emerged as a solution which mitigates this issue by introducing correction terms to local model updates. To date, GT has only been considered under Stochastic Gradient De... | {
"Other": 1,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02732 | Peri-LN: Revisiting Layer Normalization in the Transformer Architecture | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Designing Transformer architectures with the optimal layer normalization (LN) strategy that ensures large-scale training stability and expedite convergence has remained elusive, even in this era of large language models (LLMs). To this end, we present a comprehensive analytical foundation for understanding how differen... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02735 | A Modal-Based Approach for System Frequency Response and Frequency Nadir
Prediction | [
"eess.SY",
"cs.SY"
] | This letter introduces a novel approach for predicting system frequency response and frequency nadir by leveraging modal information. It significantly differentiates from traditional methods rooted in the average system frequency model. The proposed methodology targets system modes associated with the slower dynamics o... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2502.02737 | SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language
Model | [
"cs.CL"
] | While large language models have facilitated breakthroughs in many applications of artificial intelligence, their inherent largeness makes them computationally expensive and challenging to deploy in resource-constrained settings. In this paper, we document the development of SmolLM2, a state-of-the-art "small" (1.7 bil... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02740 | Vision-Language Model Dialog Games for Self-Improvement | [
"cs.LG",
"cs.AI"
] | The increasing demand for high-quality, diverse training data poses a significant bottleneck in advancing vision-language models (VLMs). This paper presents VLM Dialog Games, a novel and scalable self-improvement framework for VLMs. Our approach leverages self-play between two agents engaged in a goal-oriented play cen... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02741 | RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical
Image Segmentation with SAM 2 | [
"cs.CV"
] | Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending SAM to both image and video domains, has shown superior zero-shot performance compared to its predecessor. Building on SAM's success in medical image segmentation, SAM 2 presents significant potential for further advancement. However, similar ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02743 | LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned
Dynamic Routing | [
"cs.LG"
] | The rapid advancement in large language models (LLMs) has brought forth a diverse range of models with varying capabilities that excel in different tasks and domains. However, selecting the optimal LLM for user queries often involves a challenging trade-off between accuracy and cost, a problem exacerbated by the divers... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02747 | PatchPilot: A Stable and Cost-Efficient Agentic Patching Framework | [
"cs.RO",
"cs.AI",
"cs.CR"
] | Recent research builds various patching agents that combine large language models (LLMs) with non-ML tools and achieve promising results on the state-of-the-art (SOTA) software patching benchmark, SWE-Bench. Based on how to determine the patching workflows, existing patching agents can be categorized as agent-based pla... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02748 | ReGNet: Reciprocal Space-Aware Long-Range Modeling and Multi-Property
Prediction for Crystals | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science. Unlike molecules, crystal structures exhibit infinite periodic arrangements of atoms, requiring methods capable of capturing both local and global information effectively. However, most current works fall... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02753 | MuST: Multi-Head Skill Transformer for Long-Horizon Dexterous
Manipulation with Skill Progress | [
"cs.RO"
] | Robot picking and packing tasks require dexterous manipulation skills, such as rearranging objects to establish a good grasping pose, or placing and pushing items to achieve tight packing. These tasks are challenging for robots due to the complexity and variability of the required actions. To tackle the difficulty of l... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02756 | Adaptive Voxel-Weighted Loss Using L1 Norms in Deep Neural Networks for
Detection and Segmentation of Prostate Cancer Lesions in PET/CT Images | [
"eess.IV",
"cs.AI",
"cs.CV"
] | This study proposes a new loss function for deep neural networks, L1-weighted Dice Focal Loss (L1DFL), that leverages L1 norms for adaptive weighting of voxels based on their classification difficulty, towards automated detection and segmentation of metastatic prostate cancer lesions in PET/CT scans. We obtained 380 PS... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02761 | Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction | [
"cs.LG",
"cs.CV",
"cs.DC"
] | Low-rank tensor estimation offers a powerful approach to addressing high-dimensional data challenges and can substantially improve solutions to ill-posed inverse problems, such as image reconstruction under noisy or undersampled conditions. Meanwhile, tensor decomposition has gained prominence in federated learning (FL... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02763 | Rethinking Vision Transformer for Object Centric Foundation Models | [
"cs.CV"
] | Recent state-of-the-art object segmentation mechanisms, such as the Segment Anything Model (SAM) and FastSAM, first encode the full image over several layers and then focus on generating the mask for one particular object or area. We present an off-grid Fovea-Like Input Patching (FLIP) approach, which selects image inp... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02764 | LLM-USO: Large Language Model-based Universal Sizing Optimizer | [
"cs.AR",
"cs.LG"
] | The design of analog circuits is a cornerstone of integrated circuit (IC) development, requiring the optimization of complex, interconnected sub-structures such as amplifiers, comparators, and buffers. Traditionally, this process relies heavily on expert human knowledge to refine design objectives by carefully tuning s... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02766 | Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks | [
"cs.LG",
"cs.IT",
"math.IT"
] | Deep neural networks have achieved state-of-the-art performance across numerous applications, but their high memory and computational demands present significant challenges, particularly in resource-constrained environments. Model compression techniques, such as low-rank approximation, offer a promising solution by red... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02768 | Planning with affordances: Integrating learned affordance models and
symbolic planning | [
"cs.AI",
"cs.RO"
] | Intelligent agents working in real-world environments must be able to learn about the environment and its capabilities which enable them to take actions to change to the state of the world to complete a complex multi-step task in a photorealistic environment. Learning about the environment is especially important to pe... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02770 | Twilight: Adaptive Attention Sparsity with Hierarchical Top-$p$ Pruning | [
"cs.LG",
"cs.CL"
] | Leveraging attention sparsity to accelerate long-context large language models (LLMs) has been a hot research topic. However, current algorithms such as sparse attention or key-value (KV) cache compression tend to use a fixed budget, which presents a significant challenge during deployment because it fails to account f... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02771 | When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with
Sparse-view CT | [
"physics.med-ph",
"cs.CV",
"cs.LG",
"eess.IV",
"stat.AP"
] | Diffusion models demonstrate state-of-the-art performance on image generation, and are gaining traction for sparse medical image reconstruction tasks. However, compared to classical reconstruction algorithms relying on simple analytical priors, diffusion models have the dangerous property of producing realistic looking... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02772 | Cross-Modality Embedding of Force and Language for Natural Human-Robot
Communication | [
"cs.RO",
"cs.AI",
"cs.HC"
] | A method for cross-modality embedding of force profile and words is presented for synergistic coordination of verbal and haptic communication. When two people carry a large, heavy object together, they coordinate through verbal communication about the intended movements and physical forces applied to the object. This n... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02773 | SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge
using LLMs | [
"cs.RO",
"cs.CV"
] | High-definition maps (HD maps) are detailed and informative maps capturing lane centerlines and road elements. Although very useful for autonomous driving, HD maps are costly to build and maintain. Furthermore, access to these high-quality maps is usually limited to the firms that build them. On the other hand, standar... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02774 | Optimal Computational Secret Sharing | [
"cs.IT",
"cs.CR",
"math.IT"
] | In $(t, n)$-threshold secret sharing, a secret $S$ is distributed among $n$ participants such that any subset of size $t$ can recover $S$, while any subset of size $t-1$ or fewer learns nothing about it. For information-theoretic secret sharing, it is known that the share size must be at least as large as the secret, i... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02777 | Symmetry of information for space-bounded online Kolmogorov complexity | [
"cs.CC",
"cs.IT",
"math.IT"
] | The even online Kolmogorov complexity of a string $x = x_1 x_2 \cdots x_{n}$ is the minimal length of a program that for all $i\le n/2$, on input $x_1x_3 \cdots x_{2i-1}$ outputs $x_{2i}$. The odd complexity is defined similarly. The sum of the odd and even complexities is called the dialogue complexity. In [Bauwens,... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02779 | 3D Foundation AI Model for Generalizable Disease Detection in Head
Computed Tomography | [
"cs.CV",
"cs.AI"
] | Head computed tomography (CT) imaging is a widely-used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull, and cerebrovascular system. It is commonly the first-line imaging in neurologic emergencies given its rapidity of image acquisition, safety, cost, and ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02780 | Classroom Simulacra: Building Contextual Student Generative Agents in
Online Education for Learning Behavioral Simulation | [
"cs.HC",
"cs.AI",
"cs.LG"
] | Student simulation supports educators to improve teaching by interacting with virtual students. However, most existing approaches ignore the modulation effects of course materials because of two challenges: the lack of datasets with granularly annotated course materials, and the limitation of existing simulation models... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02783 | Runway capacity expansion planning for public airports under demand
uncertainty | [
"eess.SY",
"cs.SY",
"math.OC"
] | Flight delay is a significant issue affecting air travel. The runway system, frequently falling short of demand, serves as a bottleneck. As demand increases, runway capacity expansion becomes imperative to mitigate congestion. However, the decision to expand runway capacity is challenging due to inherent uncertainties ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2502.02785 | OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in
Soccer | [
"cs.LG"
] | Sports analytics has become both more professional and sophisticated, driven by the growing availability of detailed performance data. This progress enables applications such as match outcome prediction, player scouting, and tactical analysis. In soccer, the effective utilization of event and tracking data is fundament... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02786 | When Machine Learning Gets Personal: Understanding Fairness of
Personalized Models | [
"cs.LG"
] | Personalization in machine learning involves tailoring models to individual users by incorporating personal attributes such as demographic or medical data. While personalization can improve prediction accuracy, it may also amplify biases and reduce explainability. This work introduces a unified framework to evaluate th... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02787 | SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm
for Large Language Models | [
"cs.CL",
"cs.CR",
"cs.CY",
"cs.LG"
] | The rapid proliferation of large language models (LLMs) has created an urgent need for reliable methods to detect whether a text is generated by such models. In this paper, we propose SimMark, a posthoc watermarking algorithm that makes LLMs' outputs traceable without requiring access to the model's internal logits, en... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 1,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02788 | Inducing Diversity in Differentiable Search Indexing | [
"cs.IR",
"cs.AI",
"cs.LG"
] | Differentiable Search Indexing (DSI) is a recent paradigm for information retrieval which uses a transformer-based neural network architecture as the document index to simplify the retrieval process. A differentiable index has many advantages enabling modifications, updates or extensions to the index. In this work, we ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 1,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02789 | Speculative Prefill: Turbocharging TTFT with Lightweight and
Training-Free Token Importance Estimation | [
"cs.CL",
"cs.AI"
] | Improving time-to-first-token (TTFT) is an essentially important objective in modern large language model (LLM) inference engines. Because optimizing TTFT directly results in higher maximal QPS and meets the requirements of many critical applications. However, boosting TTFT is notoriously challenging since it is purely... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02790 | Leveraging the true depth of LLMs | [
"cs.LG",
"cs.CL"
] | Large Language Models demonstrate remarkable capabilities at the cost of high compute requirements. While recent research has shown that intermediate layers can be removed or have their order shuffled without impacting performance significantly, these findings have not been employed to reduce the computational cost of ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02797 | Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as "catastrophic forgetting". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigat... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02802 | Consistent Client Simulation for Motivational Interviewing-based
Counseling | [
"cs.CL"
] | Simulating human clients in mental health counseling is crucial for training and evaluating counselors (both human or simulated) in a scalable manner. Nevertheless, past research on client simulation did not focus on complex conversation tasks such as mental health counseling. In these tasks, the challenge is to ensure... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02807 | CAMI: A Counselor Agent Supporting Motivational Interviewing through
State Inference and Topic Exploration | [
"cs.CL"
] | Conversational counselor agents have become essential tools for addressing the rising demand for scalable and accessible mental health support. This paper introduces CAMI, a novel automated counselor agent grounded in Motivational Interviewing (MI) -- a client-centered counseling approach designed to address ambivalenc... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02810 | Mol-LLM: Generalist Molecular LLM with Improved Graph Utilization | [
"cs.LG",
"cs.AI",
"physics.chem-ph",
"q-bio.BM"
] | Recent advances in Large Language Models (LLMs) have motivated the development of general LLMs for molecular tasks. While several studies have demonstrated that fine-tuned LLMs can achieve impressive benchmark performances, they are far from genuine generalist molecular LLMs due to a lack of fundamental understanding o... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02813 | Covert Communications in Active-IOS Aided Uplink NOMA Systems With
Full-Duplex Receiver | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, an active intelligent omni-surface (A-IOS) is deployed to aid uplink transmissions in a non-orthogonal multiple access (NOMA) system. In order to shelter the covert signal embedded in the superposition transmissions, a multi-antenna full-duplex (FD) receiver is utilized at the base-station to recover sig... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02817 | A Decade of Action Quality Assessment: Largest Systematic Survey of
Trends, Challenges, and Future Directions | [
"cs.AI",
"cs.CV"
] | Action Quality Assessment (AQA) -- the ability to quantify the quality of human motion, actions, or skill levels and provide feedback -- has far-reaching implications in areas such as low-cost physiotherapy, sports training, and workforce development. As such, it has become a critical field in computer vision & video u... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02818 | Accessible and Portable LLM Inference by Compiling Computational Graphs
into SQL | [
"cs.DB",
"cs.LG"
] | Serving large language models (LLMs) often demands specialized hardware, dedicated frameworks, and substantial development efforts, which restrict their accessibility, especially for edge devices and organizations with limited technical resources. We propose a novel compiler that translates LLM inference graphs into SQ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 1,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02820 | Slowing Learning by Erasing Simple Features | [
"cs.LG"
] | Prior work suggests that neural networks tend to learn low-order moments of the data distribution first, before moving on to higher-order correlations. In this work, we derive a novel closed-form concept erasure method, QLEACE, which surgically removes all quadratically available information about a concept from a repr... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02821 | AIoT-based smart traffic management system | [
"cs.CV"
] | This paper presents a novel AI-based smart traffic management system de-signed to optimize traffic flow and reduce congestion in urban environments. By analysing live footage from existing CCTV cameras, this approach eliminates the need for additional hardware, thereby minimizing both deployment costs and ongoing maint... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02829 | Global Contact-Rich Planning with Sparsity-Rich Semidefinite Relaxations | [
"cs.RO",
"math.OC"
] | We show that contact-rich motion planning is also sparsity-rich when viewed as polynomial optimization (POP). We can exploit not only the correlative and term sparsity patterns that are general to all POPs, but also specialized sparsity patterns from the robot kinematic structure and the separability of contact modes. ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02830 | Multimodal Brain-Computer Interfaces: AI-powered Decoding Methodologies | [
"cs.HC",
"cs.LG",
"q-bio.NC"
] | Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02831 | How the Stroop Effect Arises from Optimal Response Times in Laterally
Connected Self-Organizing Maps | [
"q-bio.NC",
"cs.NE"
] | The Stroop effect refers to cognitive interference in a color-naming task: When the color and the word do not match, the response is slower and more likely to be incorrect. The Stroop task is used to assess cognitive flexibility, selective attention, and executive function. This paper implements the Stroop task with se... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 1,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02834 | Task-Aware Virtual Training: Enhancing Generalization in
Meta-Reinforcement Learning for Out-of-Distribution Tasks | [
"cs.LG",
"cs.AI"
] | Meta reinforcement learning aims to develop policies that generalize to unseen tasks sampled from a task distribution. While context-based meta-RL methods improve task representation using task latents, they often struggle with out-of-distribution (OOD) tasks. To address this, we propose Task-Aware Virtual Training (TA... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02835 | A Survey of Sample-Efficient Deep Learning for Change Detection in
Remote Sensing: Tasks, Strategies, and Challenges | [
"cs.CV"
] | In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust Change Detection (CD) on large volumes of Remote Sensing Images (RSIs). However, despite advances in CD methods, their practical application in real-world contexts remains limited due to the d... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02844 | Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement
Learning | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.MA"
] | Traditional robust methods in multi-agent reinforcement learning (MARL) often struggle against coordinated adversarial attacks in cooperative scenarios. To address this limitation, we propose the Wolfpack Adversarial Attack framework, inspired by wolf hunting strategies, which targets an initial agent and its assisting... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 1,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02850 | RS-YOLOX: A High Precision Detector for Object Detection in Satellite
Remote Sensing Images | [
"cs.CV"
] | Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this article proposes an improved YOLOX model for satellite remote sensing image automatic detection. This model ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02853 | Rethinking Latent Representations in Behavior Cloning: An Information
Bottleneck Approach for Robot Manipulation | [
"cs.RO",
"cs.LG"
] | Behavior Cloning (BC) is a widely adopted visual imitation learning method in robot manipulation. Current BC approaches often enhance generalization by leveraging large datasets and incorporating additional visual and textual modalities to capture more diverse information. However, these methods overlook whether the le... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02854 | TD3: Tucker Decomposition Based Dataset Distillation Method for
Sequential Recommendation | [
"cs.IR",
"cs.LG"
] | In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on large-scale datasets, but this also results in significant training costs. Dataset distillation has emerged as a key solution, condensing lar... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 1,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02856 | PH-VAE: A Polynomial Hierarchical Variational Autoencoder Towards
Disentangled Representation Learning | [
"cs.LG"
] | The variational autoencoder (VAE) is a simple and efficient generative artificial intelligence method for modeling complex probability distributions of various types of data, such as images and texts. However, it suffers some main shortcomings, such as lack of interpretability in the latent variables, difficulties in t... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02858 | Dexterous Safe Control for Humanoids in Cluttered Environments via
Projected Safe Set Algorithm | [
"cs.RO"
] | It is critical to ensure safety for humanoid robots in real-world applications without compromising performance. In this paper, we consider the problem of dexterous safety, featuring limb-level geometry constraints for avoiding both external and self-collisions in cluttered environments. Compared to safety with simplif... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02859 | Gap-Dependent Bounds for Federated $Q$-learning | [
"stat.ML",
"cs.LG"
] | We present the first gap-dependent analysis of regret and communication cost for on-policy federated $Q$-Learning in tabular episodic finite-horizon Markov decision processes (MDPs). Existing FRL methods focus on worst-case scenarios, leading to $\sqrt{T}$-type regret bounds and communication cost bounds with a $\log T... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02861 | Algorithms with Calibrated Machine Learning Predictions | [
"stat.ML",
"cs.DS",
"cs.LG"
] | The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. While this theoretical framework often assumes uniform reliability across all predictions, modern machine learning models can now provide instance-level uncertainty estimat... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2502.02862 | Learning Generalizable Features for Tibial Plateau Fracture Segmentation
Using Masked Autoencoder and Limited Annotations | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Accurate automated segmentation of tibial plateau fractures (TPF) from computed tomography (CT) requires large amounts of annotated data to train deep learning models, but obtaining such annotations presents unique challenges. The process demands expert knowledge to identify diverse fracture patterns, assess severity, ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.