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arXiv:2603.02447v1 Announce Type: new Abstract: Diffusion models are typically trained using pointwise reconstruction objectives that are agnostic to the spectral and multi-scale structure of natural …
arXiv:2603.02452v1 Announce Type: new Abstract: A major focus in designing methods for learning distributions defined on manifolds is to alleviate the need to implicitly learn …
arXiv:2603.02462v1 Announce Type: new Abstract: A key challenge in deriving unified neural solvers for combinatorial optimization (CO) is efficient generalization of models between a given …
arXiv:2603.02491v1 Announce Type: new Abstract: As artificial agents become increasingly capable, what internal structure is *necessary* for an agent to act competently under uncertainty? Classical …
arXiv:2603.02510v1 Announce Type: new Abstract: The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning …
arXiv:2603.02525v1 Announce Type: new Abstract: Restricted Boltzmann Machines (RBMs) are typically trained using finite-length Gibbs chains under a fixed sampling temperature. This practice implicitly assumes …
arXiv:2603.02531v1 Announce Type: new Abstract: Classifier-Free Guidance (CFG) has significantly enhanced the generative quality of diffusion models by extrapolating between conditional and unconditional outputs. However, …
arXiv:2603.02562v1 Announce Type: new Abstract: Federated Learning (FL) has emerged as a transformative distributed learning paradigm in the era of Internet of Things (IoT), reconceptualizing …
CVPR 2026 Media Center CVPR is the foremost computer vision event of the year. Covering advances in artificial intelligence (AI), autonomous vehicles, robotics, Large Language …
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The Supreme Court unanimously sided with the federal government on Wednesday in Urias-Orellana v. Bondi, holding in an opinion by Justice Ketanji Brown Jackson that …