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On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length.

S. Jastrzebski, Z. Kenton, N. Ballas, A. Fischer, Y. Bengio, and A. Storkey. ICLR (Poster), OpenReview.net, (2019)

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Noisy Activation FunctionsC. Gulcehre, M. Moczulski, M. Denil, and Y. Bengio. (2016)cite arxiv:1603.00391v3.pdf.Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network ModelsI. Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau. (2015)cite arxiv:1507.04808Comment: 8 pages with references; Published in AAAI 2016 (Special Track on Cognitive Systems).Unitary Evolution Recurrent Neural Networks.M. Arjovsky, A. Shah, and Y. Bengio. CoRR, (2015)Universal Successor Representations for Transfer Reinforcement Learning.C. Ma, J. Wen, and Y. Bengio. ICLR (Workshop), OpenReview.net, (2018)Generalizable Features From Unsupervised Learning.M. Mirza, A. Courville, and Y. Bengio. ICLR (Workshop), OpenReview.net, (2017)Speech and Speaker Recognition from Raw Waveform with SincNet.M. Ravanelli, and Y. Bengio. CoRR, (2018)The effects of negative adaptation in Model-Agnostic Meta-Learning.T. Deleu, and Y. Bengio. CoRR, (2018)A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.Y. Bengio, T. Deleu, N. Rahaman, N. Ke, S. Lachapelle, O. Bilaniuk, A. Goyal, and C. Pal. CoRR, (2019)Torchmeta: A Meta-Learning library for PyTorch.T. Deleu, T. Würfl, M. Samiei, J. Cohen, and Y. Bengio. CoRR, (2019)On integrating a language model into neural machine translation.Ç. Gülçehre, O. Firat, K. Xu, K. Cho, and Y. Bengio. Comput. Speech Lang., (2017)

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On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length.

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On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length.