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pro vyhledávání: '"Singh, Marco"'
Autor:
Lopes, Vasco, Carlucci, Fabio Maria, Esperança, Pedro M, Singh, Marco, Gabillon, Victor, Yang, Antoine, Xu, Hang, Chen, Zewei, Wang, Jun
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective. Due to the large architecture parameter s
Externí odkaz:
http://arxiv.org/abs/1909.01051
Autor:
Singh, Marco, Pai, Akshay
Despite all the success that deep neural networks have seen in classifying certain datasets, the challenge of finding optimal solutions that generalize still remains. In this paper, we propose the Boundary Optimizing Network (BON), a new approach to
Externí odkaz:
http://arxiv.org/abs/1801.02642
Nontrivial connectivity has allowed the training of very deep networks by addressing the problem of vanishing gradients and offering a more efficient method of reusing parameters. In this paper we make a comparison between residual networks, densely-
Externí odkaz:
http://arxiv.org/abs/1711.10271
Akademický článek
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Autor:
Lopes, Vasco, Carlucci, Fabio Maria, Esperança, Pedro M., Singh, Marco, Yang, Antoine, Gabillon, Victor, Xu, Hang, Chen, Zewei, Wang, Jun
Publikováno v:
Machine Learning; Jan2024, Vol. 113 Issue 1, p73-96, 24p
