Preprint2024
Explaining neural scaling laws
Unknown
This paper explains neural scaling laws by deriving them from realistic data assumptions within learning theory.
0Jan 1, 2024Scaling Laws
AI Research
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5 papersPreprintarXiv.org2503
A comprehensive survey of mixture-of-experts: Algorithms, theory, and applications
Siyuan Mu, Sen-Fon Lin
A comprehensive survey of Mixture-of-Experts architectures covering algorithms, theory, and applications.
115Jan 1, 2503Attention Mechanisms
arXiv
JournalProceedings of the National Academy of Sciences2019
A mathematical theory of semantic development in deep neural networks
Andrew Saxe, James L. McClelland, Surya Ganguli
This paper mathematically analyzes deep linear networks to explain how neural networks acquire, organize, and deploy semantic knowledge, recapitulating many empirical regularities of human semantic development.
357May 17, 2019Neural Networks
arXiv
JournalIEEE Access2021
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications
Nahian Siddique, Sidike Paheding, Colin Elkin, et al.
A narrative literature review examining U-Net architecture developments, breakthroughs, and applications in medical image segmentation across multiple modalities.
1.9kJan 1, 2021
arXiv
JournalProceedings of the IEEE2021
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, et al.
A comprehensive review of post-hoc explainability methods for deep neural networks, covering theory, comparative evaluation, best practices, and applications.
1.3kMar 1, 2021Attention MechanismsNeural Networks