Decoupled greedy learning of cnns for synchronous and asynchronous distributed learning

E Belilovsky, L Leconte, L Caccia, M Eickenberg… - arXiv preprint arXiv …, 2021 - arxiv.org
A commonly cited inefficiency of neural network training using back-propagation is the
update locking problem: each layer must wait for the signal to propagate through the full …
被引用次数：11 相关文章

Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning

E Belilovsky, L Leconte, L Caccia… - arXiv e …, 2021 - ui.adsabs.harvard.edu
A commonly cited inefficiency of neural network training using back-propagation is the
update locking problem: each layer must wait for the signal to propagate through the full …

Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning

E Belilovsky, L Leconte, L Caccia, M Eickenberg… - 2021 - hal.science
A commonly cited inefficiency of neural network training using back-propagation is the
update locking problem: each layer must wait for the signal to propagate through the full …

Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning

E Belilovsky, L Leconte, L Caccia, M Eickenberg… - 2021 - inria.hal.science
A commonly cited inefficiency of neural network training using back-propagation is the
update locking problem: each layer must wait for the signal to propagate through the full …
[PDF] hal.science

[PDF][PDF] Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning

E Belilovsky, L Leconte, L Caccia, M Eickenberg… - hal.science
A commonly cited inefficiency of neural network training using back-propagation is the
update locking problem: each layer must wait for the signal to propagate through the full …