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pro vyhledávání: '"Parkinson, Suzanna"'
We study depth separation in infinite-width neural networks, where complexity is controlled by the overall squared $\ell_2$-norm of the weights (sum of squares of all weights in the network). Whereas previous depth separation results focused on separ
Externí odkaz:
http://arxiv.org/abs/2402.08808
Neural networks often operate in the overparameterized regime, in which there are far more parameters than training samples, allowing the training data to be fit perfectly. That is, training the network effectively learns an interpolating function, a
Externí odkaz:
http://arxiv.org/abs/2305.15598
Autor:
Parkinson, Suzanna, Ringer, Hayden, Wall, Kate, Parkinson, Erik, Erekson, Lukas, Christensen, Daniel, Jarvis, Tyler J.
We examine several of the normal-form multivariate polynomial rootfinding methods of Telen, Mourrain, and Van Barel and some variants of those methods. We analyze the performance of these variants in terms of their asymptotic temporal complexity as w
Externí odkaz:
http://arxiv.org/abs/2104.03526
Autor:
Parkinson, Suzanna, Ringer, Hayden, Wall, Kate, Parkinson, Erik, Erekson, Lukas, Christensen, Daniel, Jarvis, Tyler J.
Publikováno v:
In Journal of Computational and Applied Mathematics September 2022 411
This paper explores the implicit bias of overparameterized neural networks of depth greater than two layers. Our framework considers a family of networks of varying depths that all have the same capacity but different implicitly defined representatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47fbf275395d5b43e1cf753aa2870b1b
http://arxiv.org/abs/2305.15598
http://arxiv.org/abs/2305.15598