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pro vyhledávání: '"Williams, Ezekiel"'
Autor:
Williams, Ezekiel, Ryoo, Avery Hee-Woon, Jiralerspong, Thomas, Payeur, Alexandre, Perich, Matthew G., Mazzucato, Luca, Lajoie, Guillaume
Landmark universal function approximation results for neural networks with trained weights and biases provided impetus for the ubiquitous use of neural networks as learning models in Artificial Intelligence (AI) and neuroscience. Recent work has push
Externí odkaz:
http://arxiv.org/abs/2407.00957
Publikováno v:
PMLR 202:37042-37065, 2023
Many learning algorithms used as normative models in neuroscience or as candidate approaches for learning on neuromorphic chips learn by contrasting one set of network states with another. These Contrastive Learning (CL) algorithms are traditionally
Externí odkaz:
http://arxiv.org/abs/2302.12431
Autor:
Williams, Ezekiel
How the brain encodes information in sequences of voltage spikes is an open question. Past literature suggests the importance of bursts, high-frequency spike events, as a key step towards answering this question. In particular, it was recently shown
Externí odkaz:
http://hdl.handle.net/10393/40407
Publikováno v:
Scientific Reports. 8/5/2021, Vol. 11 Issue 1, p1-16. 16p.
Autor:
Williams, Ezekiel J., Imig, Steven K.
Publikováno v:
Proceedings of the Rocky Mountain Mineral Law Annual Institute; 2011, Vol. 57, p6-1-6-43, 43p
Autor:
Williams, Ezekiel
Publikováno v:
Quest: Muscular Dystrophy Association; Jan/Feb2009, Vol. 16 Issue 1, p85-86, 2p, 2 Color Photographs
Publikováno v:
ArXiv [ArXiv] 2024 Jul 02. Date of Electronic Publication: 2024 Jul 02.