Concept-Aware Batch Sampling Improves Language-Image Pretraining [TOP LAB](arxiv.org)2025-11-26|paper|arXiv<think>cs-CVcs-LG
PaTAS: A Parallel System for Trust Propagation in Neural Networks Using Subjective Logic [TOP LAB](arxiv.org)2025-11-26|paper|arXiv<think>cs-AIcs-LG
MSTN: Fast and Efficient Multivariate Time Series Model [TOP LAB](arxiv.org)2025-11-26|paper|arXiv<think>cs-LG
Harnessing Data from Clustered LQR Systems: Personalized and Collaborative Policy Optimization [TOP LAB](arxiv.org)2025-11-24|paper|arXiv<think>cs-LGeess-SYmath-OC
That's not natural: The Impact of Off-Policy Training Data on Probe Performance [TOP LAB](arxiv.org)2025-11-24|paper|arXiv<think>cs-AIcs-LG
Dataset Distillation for Pre-Trained Self-Supervised Vision Models(arxiv.org)2025-11-23|paper|arXiv<think>cs-CVcs-AIcs-LG
Solving Spatial Supersensing Without Spatial Supersensing [TOP LAB](arxiv.org)2025-11-23|paper|arXiv<think>cs-CVcs-LG
Solving Spatial Supersensing Without Spatial Supersensing [TOP LAB](arxiv.org)2025-11-21|paper|arXiv<think>cs-CVcs-LG
Dataset Distillation for Pre-Trained Self-Supervised Vision Models(arxiv.org)2025-11-21|paper|arXiv<think>cs-CVcs-AIcs-LG
$π^{*}_{0.6}$: a VLA That Learns From Experience [TOP LAB](arxiv.org)2025-11-19|paper|arXiv<think>cs-LGcs-RO
OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation [TOP LAB](arxiv.org)2025-11-18|paper|arXiv<think>cs-CVcs-LG
Scaling Spatial Intelligence with Multimodal Foundation Models(arxiv.org)2025-11-18|paper|arXiv<think>cs-CVcs-AIcs-LG
Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates [TOP LAB](arxiv.org)2025-11-17|paper|arXiv<think>math-OCcs-LG
MoCap2Radar: A Spatiotemporal Transformer for Synthesizing Micro-Doppler Radar Signatures from Motion Capture [TOP LAB](arxiv.org)2025-11-17|paper|arXiv<think>cs-LG
Synergy vs. Noise: Performance-Guided Multimodal Fusion For Biochemical Recurrence-Free Survival in Prostate Cancer [TOP LAB](arxiv.org)2025-11-17|paper|arXiv<think>q-bio-QMcs-CVcs-LG
DiffPro: Joint Timestep and Layer-Wise Precision Optimization for Efficient Diffusion Inference [TOP LAB](arxiv.org)2025-11-17|paper|arXiv<think>cs-LG
Completion of partial structures using Patterson maps with the CrysFormer machine learning model [TOP LAB](arxiv.org)2025-11-16|paper|arXiv<think>physics-bio-phcs-AIcs-LG
A Diffusion Model to Shrink Proteins While Maintaining Their Function [TOP LAB](arxiv.org)2025-11-11|paper|arXiv<think>cs-LGq-bio-QM
TNT: Improving Chunkwise Training for Test-Time Memorization [TOP LAB](arxiv.org)2025-11-11|paper|arXiv<think>cs-LGcs-AI
Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis [TOP LAB](arxiv.org)2025-11-11|paper|arXiv<think>cs-LGcs-CV
Can Training Dynamics of Scale-Invariant Neural Networks Be Explained by the Thermodynamics of an Ideal Gas? [TOP LAB](arxiv.org)2025-11-11|paper|arXiv<think>cs-LG
Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs(arxiv.org)2025-11-11|paper|arXiv<think>cs-LG
Dark Energy Survey Year 3 results: Simulation-based $w$CDM inference from weak lensing and galaxy clustering maps with deep learning. I. Analysis design [TOP LAB](arxiv.org)2025-11-09|paper|arXiv<think>astro-ph-COcs-LG
End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit [TOP LAB](arxiv.org)2025-11-09|paper|arXiv<think>cs-LGmath-OC
Dark Energy Survey Year 3 results: Simulation-based $w$CDM inference from weak lensing and galaxy clustering maps with deep learning. I. Analysis design [TOP LAB](arxiv.org)2025-11-08|paper|arXiv<think>astro-ph-COcs-LG
End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit [TOP LAB](arxiv.org)2025-11-08|paper|arXiv<think>cs-LGmath-OC
Dark Energy Survey Year 3 results: Simulation-based $w$CDM inference from weak lensing and galaxy clustering maps with deep learning. I. Analysis design [TOP LAB](arxiv.org)2025-11-07|paper|arXiv<think>astro-ph-COcs-LG
End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit [TOP LAB](arxiv.org)2025-11-07|paper|arXiv<think>cs-LGmath-OC