arXiv:2103.14203v5 Announce Type: replace-cross Abstract: Matrix reordering is a task to permute the rows and columns of a given observed matrix such that the resulting reordered matrix shows meaningful or interpretable structural patterns. Most existing matrix reordering techniques share the…
arXiv:2506.05402v3 Announce Type: replace-cross Abstract: The growing use of large pre-trained models in edge computing has made model inference on mobile clients both feasible and popular. Yet these devices remain vulnerable to adversarial attacks, threatening model robustness and security. Federated…
arXiv:2509.16117v2 Announce Type: replace Abstract: Online reinforcement learning (RL) has been central to post-training language models, but its extension to diffusion models remains challenging due to intractable likelihoods. Recent works discretize the reverse sampling process to enable GRPO-style training, yet…
arXiv:2510.17734v3 Announce Type: replace-cross Abstract: This paper presents low-complexity tensor completion algorithms and their efficient implementation to reconstruct highly oscillatory operators discretized as $ntimes n$ matrices. The underlying tensor decomposition is based on the reshaping of the input matrix and…
arXiv:2103.14203v5 Announce Type: replace-cross Abstract: Matrix reordering is a task to permute the rows and columns of a given observed matrix such that the resulting reordered matrix shows meaningful or interpretable structural patterns. Most existing matrix reordering techniques share the…
arXiv:2506.05402v3 Announce Type: replace-cross Abstract: The growing use of large pre-trained models in edge computing has made model inference on mobile clients both feasible and popular. Yet these devices remain vulnerable to adversarial attacks, threatening model robustness and security. Federated…
arXiv:2509.16117v2 Announce Type: replace Abstract: Online reinforcement learning (RL) has been central to post-training language models, but its extension to diffusion models remains challenging due to intractable likelihoods. Recent works discretize the reverse sampling process to enable GRPO-style training, yet…
arXiv:2510.17734v3 Announce Type: replace-cross Abstract: This paper presents low-complexity tensor completion algorithms and their efficient implementation to reconstruct highly oscillatory operators discretized as $ntimes n$ matrices. The underlying tensor decomposition is based on the reshaping of the input matrix and…
arXiv:2510.17734v3 Announce Type: replace-cross Abstract: This paper presents low-complexity tensor completion algorithms and their efficient implementation to reconstruct highly oscillatory operators discretized as $ntimes n$ matrices. The underlying tensor decomposition is based on the reshaping of the input matrix and…
arXiv:2103.14203v5 Announce Type: replace-cross Abstract: Matrix reordering is a task to permute the rows and columns of a given observed matrix such that the resulting reordered matrix shows meaningful or interpretable structural patterns. Most existing matrix reordering techniques share the…
