Search: “models”

A Cold Diffusion Approach for Percussive Dereverberation

arXiv:2605.10256v1 Announce Type: cross Abstract: Most recent advances in audio dereverberation focus almost exclusively on speech, leaving percussive and drum signals largely unexplored despite their

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A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting

DGX agent

arXiv:2605.09061v1 Announce Type: cross Abstract: Accurate and efficient imbalance electricity price forecasting is critical for industrial energy trading systems, especially as battery assets and aut

researcharxiv-cs-lg

12 May 2026

A probabilistic framework for crystal structure denoising, phase classification, and order parameters

arXiv:2512.11077v3 Announce Type: replace-cross Abstract: Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a

local-aiarxiv-cs-ai

Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights

arXiv:2605.10654v1 Announce Type: cross Abstract: We consider the active learning problem where the goal is to learn an unknown function with low prediction error under an unknown Boltzmann distributi

tutorialsarxiv-cs-ai

Active-SAOOD: Active Sparsely Annotated Oriented Object Detection in Remote Sensing Images

arXiv:2605.10162v1 Announce Type: new Abstract: Reducing the annotation cost of oriented object detection in remote sensing remains a major challenge. Recently, sparse annotation has gained attention

applicationsarxiv-cs-cv

Adaptive Context Matters: Towards Provable Multi-Modality Guidance for Super-Resolution

arXiv:2605.10470v1 Announce Type: new Abstract: Super-resolution (SR) is a severely ill-posed problem with inherent ambiguity, as widely recognized in both empirical and theoretical studies. Although

safetyarxiv-cs-cv

Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III

arXiv:2605.08161v1 Announce Type: new Abstract: Tumor segmentation in whole-body PET/CT imaging is crucial for precise disease evaluation and treatment planning. However, it remains challenging due to

Alignment-Sensitive Minimax Rates for Spectral Algorithms with Learned Kernels

arXiv:2509.20294v4 Announce Type: replace Abstract: We study spectral algorithms in the setting where kernels are learned from data. We introduce the effective span dimension (ESD), an alignment-sensi

An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data

arXiv:2605.08242v1 Announce Type: cross Abstract: Clinical dietary assessment can generate detailed but high-dimensional nutrient and food-group information that is difficult to translate quickly into

Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring

arXiv:2510.13397v3 Announce Type: replace Abstract: Dropout is common in clinical studies, with up to half of patients leaving early due to side effects or other reasons. When dropout is informative (

Agents

AstroSplat: Physics-Based Gaussian Splatting for Rendering and Reconstruction of Small Celestial Bodies

arXiv:2603.11969v2 Announce Type: replace Abstract: Image-based surface reconstruction and characterization are crucial for missions to small celestial bodies (e.g., asteroids), as it informs mission

agentsarxiv-cs-cv

Attention-based graph neural networks: a survey

arXiv:2605.08679v1 Announce Type: cross Abstract: Graph neural networks (GNNs) aim to learn well-trained representations in a lower-dimension space for downstream tasks while preserving the topologica

tutorialsarxiv-cs-ai

Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge

arXiv:2605.10370v1 Announce Type: new Abstract: Scientific knowledge on the Web is published as passive assertions and cannot decide when to validate evidence, reconcile contradictions, or update conf

Bangla-WhisperDiar: Fine-Tuning Whisper and PyAnnote for Bangla Long-Form Speech Recognition and Speaker Diarization

arXiv:2605.08214v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) and speaker diarization in Bangla remain challenging due to long form recordings, diverse acoustic conditions, and

Bayesian Reasoning for Physics Informed Neural Networks

arXiv:2308.13222v3 Announce Type: replace-cross Abstract: We introduce an evidence-driven Bayesian formulation of physics-informed neural networks that enables automatic optimization of loss weights b

Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning

arXiv:2605.08202v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) faces a critical challenge of overestimating the value of out-of-distribution (OOD) actions. Existing methods miti

Beyond Static Bias: Adaptive Multi-Fidelity Bandits with Improving Proxies

arXiv:2605.08558v1 Announce Type: new Abstract: As an extension of the classical multi-armed bandit problem, multi-fidelity multi-armed bandits (MF-MAB) enable individual arms to be evaluated using di

Agents

Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems

arXiv:2605.08994v1 Announce Type: cross Abstract: The current catalyst discovery and development pipeline for energy-intensive applications like methane conversion remains bottlenecked by expensive tr

agentsarxiv-cs-lg

CapCLIP: A Vision-Language Representation Alignment Approach for Wireless Capsule Endoscopy Analysis

arXiv:2605.08493v1 Announce Type: new Abstract: Wireless capsule endoscopy (WCE) enables non-invasive visual assessment of the small bowel, but its clinical utility is constrained by the large volume

safetyarxiv-cs-cv

Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation

arXiv:2605.09663v1 Announce Type: cross Abstract: Machine learning classifiers in dynamic environments face concept drift -- changes in the data-generating process that degrade performance. Convention

CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients

arXiv:2605.10111v1 Announce Type: cross Abstract: Motor imagery electroencephalography (MI-EEG) decoding offers a non-invasive route for post-stroke rehabilitation, but cross-patient use remains diffi

Change My View? The Dynamics of Persuasion and Polarization in Online Discourse

arXiv:2605.08383v1 Announce Type: new Abstract: Philosophical accounts of persuasion often assume that shared evidence and rational argumentation should lead to a convergence of views between peers, y

safetyarxiv-cs-cl

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking

arXiv:2605.09858v1 Announce Type: new Abstract: Multi-Object Tracking (MOT) in dynamic environments relies on robust temporal reasoning to maintain consistent object identities over time. Transformer-

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks

arXiv:2601.22427v2 Announce Type: replace-cross Abstract: Temporal link prediction is crucial for rapidly growing social networks. Existing methods often overlook the underlying causal mechanisms that

COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing

arXiv:2511.06894v3 Announce Type: replace-cross Abstract: Reconstruction-based methods are a dominant paradigm in time series anomaly detection (TSAD), however, their near-universal reliance on Mean S

Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision

arXiv:2509.14234v3 Announce Type: replace Abstract: Where do learning signals come from when there is no ground truth in post-training? We show that inference compute itself can serve as supervision.

Consolidation-Expansion Operator Mechanics:A Unified Framework for Adaptive Learning

arXiv:2605.09968v1 Announce Type: new Abstract: Every adaptive learning system must alternate between two operations: consolidating what it already knows and expanding into new evidence. We propose Co

Constraint-Aware Diffusion Priors for High-Fidelity and Versatile Quadruped Locomotion

arXiv:2605.08804v1 Announce Type: new Abstract: Reinforcement learning combined with imitation learning has significantly advanced biomimetic quadrupedal locomotion. However, scaling these frameworks

safetyarxiv-cs-ro

Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy

arXiv:2505.05707v2 Announce Type: replace Abstract: The integration of AI into daily life has generated considerable attention and excitement, while also raising concerns about automating algorithmic

Curriculum Learning for LLM Pretraining: An Analysis of Learning Dynamics

arXiv:2601.21698v2 Announce Type: replace-cross Abstract: Curriculum learning changes the order of pretraining data, but it remains unclear how ordering changes the learning dynamics. We pretrain mode

Curvature-Aware Captioning:Leveraging Geodesic Attention for 3D Scene Understanding

arXiv:2605.08808v1 Announce Type: cross Abstract: Accurate 3D scene description is fundamental to robotic navigation and augmented reality, yet current dense captioning methods face significant limita

local-aiarxiv-cs-ai

D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement

arXiv:2605.09400v1 Announce Type: new Abstract: Batch selection is crucial for improving both training efficiency and predictive performance in deep multi-label classification (MLC). Existing batch se

Deep-ICE: the first globally optimal algorithm for minimizing 0-1 loss in two-layer ReLU and maxout networks

arXiv:2505.05740v3 Announce Type: replace Abstract: This paper introduces the first globally optimal algorithm for the empirical risk minimization problem of two-layer maxout and ReLU networks, i.e.,

DegBins: Degradation-Driven Binning for Depth Super-Resolution

arXiv:2605.09628v1 Announce Type: new Abstract: Depth super-resolution (DSR) aims to recover a high-resolution (HR) depth map from its low-resolution (LR) counterpart. With color image guidance, this

Dendritic Neural Networks with Equilibrium Propagation

arXiv:2605.08135v1 Announce Type: new Abstract: Equilibrium propagation (EP) is a biologically plausible alternative to backpropagation (BP), but its effectiveness can degrade in deeper and more chall

Digital Image Forgery Detection Using Transfer Learning

arXiv:2605.08167v1 Announce Type: cross Abstract: The increasing availability of advanced image editing tools has led to a significant rise in manipulated digital content, posing serious challenges fo

Don't Fix the Basis -- Learn It: Spectral Representation with Adaptive Basis Learning for PDEs

arXiv:2605.10451v1 Announce Type: new Abstract: Spectral neural operators achieve strong performance for PDE learning, but rely on fixed global bases that limit their ability to represent spatially he

tutorialsarxiv-cs-lg

DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection

arXiv:2605.10436v1 Announce Type: cross Abstract: Domain Generation Algorithms (DGAs) evolve continuously to evade botnet detection, posing a persistent challenge for dependable network defense. While

tutorialsarxiv-cs-lg

Effective Explanations Support Planning Under Uncertainty

arXiv:2605.08406v1 Announce Type: cross Abstract: Explaining how to get from A to B can be challenging. It requires mentally simulating what the listener will do based on what they are told. To captur

Egocentric Whole-Body Human Mesh Recovery with Prior-Guided Learning

arXiv:2605.08606v1 Announce Type: new Abstract: Egocentric human mesh recovery (HMR) from monocular head-mounted cameras is increasingly important for AR/VR applications, but remains challenging due t

enclawed: A Configurable, Sector-Neutral Hardening Framework for Single-User AI Assistant Gateways

arXiv:2604.16838v2 Announce Type: replace-cross Abstract: We present enclawed, a hard-fork hardening framework built on the OpenClaw AI assistant gateway. enclawed targets deployments that need attest

Evaluating Federated Learning approaches for mammography under breast density heterogeneity

arXiv:2605.09137v1 Announce Type: new Abstract: Breast density is a key factor that influences mammography interpretation and is a major source of heterogeneity in multicenter datasets. Such heterogen

local-aiarxiv-cs-lg

EventTSF: Event-Aware Non-Stationary Time Series Forecasting

arXiv:2508.13434v2 Announce Type: replace-cross Abstract: Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined wi

Agents

Evidence Over Plans: Online Trajectory Verification for Skill Distillation

arXiv:2605.09192v1 Announce Type: new Abstract: Agent skills can remarkably improve task success rates by using human-written procedural documents, but their quality is difficult to assess without env

agentsarxiv-cs-ai

Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI

arXiv:2605.09852v1 Announce Type: new Abstract: Machine learning algorithms are being used in high-stakes decisions, including those in criminal justice, healthcare, credit, and employment. The resear

Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration

arXiv:2605.10330v1 Announce Type: cross Abstract: We propose a novel adaptive Mixture-of-Experts (MoE) framework for time series forecasting that enhances expert specialization by incorporating expert

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting

arXiv:2602.07126v2 Announce Type: replace Abstract: Synthetic tabular data has gained attention for enabling privacy-preserving data sharing. While substantial progress has been made in single-table s

applicationsarxiv-cs-lg

FLARE: One-Shot PE-Level Fault Localization in Systolic Arrays via Algebraic Test Vectors

arXiv:2605.08594v1 Announce Type: cross Abstract: Systolic arrays are the dominant compute fabric for neural network inference. Prior work has addressed column-level fault detection efficiently with u

local-aiarxiv-cs-lg