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cs.LG2025
Harnessing intuitive local evolution rules for physical learning
Roie Ezraty, Menachem Stern, Shmuel M. Rubinstein
Machine Learning, however popular and accessible, is computationally intensive and highly power-consuming, prompting interest in alternative physical implementations of learning ta…
cs.LG2023
Applying statistical learning theory to deep learning
Cédric Gerbelot, Avetik Karagulyan, Stefani Karp +3
Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear, in particular how diff…