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Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy.

A. Lamb, V. Verma, J. Kannala, and Y. Bengio. AISec@CCS, page 95-103. ACM, (2019)

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Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation.I. Serban, T. Klinger, G. Tesauro, K. Talamadupula, B. Zhou, Y. Bengio, and A. Courville. (2016)cite arxiv:1606.00776Comment: 21 pages, 2 figures, 10 tables.How to Construct Deep Recurrent Neural Networks.R. Pascanu, Ç. Gülçehre, K. Cho, and Y. Bengio. CoRR, (2013)Disentangling the independently controllable factors of variation by interacting with the world.V. Thomas, E. Bengio, W. Fedus, J. Pondard, P. Beaudoin, H. Larochelle, J. Pineau, D. Precup, and Y. Bengio. CoRR, (2018)An objective function for STDP.Y. Bengio, T. Mesnard, A. Fischer, S. Zhang, and Y. Wu. CoRR, (2015)Markovian Models for Sequential DataY. Bengio. Neural computing surveys, 2 (199): 129--162 (1999)Three Factors Influencing Minima in SGDS. Jastrzębski, Z. Kenton, D. Arpit, N. Ballas, A. Fischer, Y. Bengio, and A. Storkey. (2017)cite arxiv:1711.04623Comment: First two authors contributed equally. Short version accepted into ICLR workshop.Empirical Evaluation of Gated Recurrent Neural Networks on Sequence ModelingJ. Chung, {. Gülcehre, K. Cho, and Y. Bengio. NIPS Deep Learning Workshop, (2014)Learning Long-Term Dependencies With Gradient Descent Is DifficultY. Bengio, P. Simard, and P. Frasconi. IEEE Transactions on Neural Networks, 5 (2): 157--166 (1994)Estimating or Propagating Gradients Through Stochastic NeuronsY. Bengio. CoRR, (2013)Efficient EM Training of Gaussian Mixtures with Missing DataO. Delalleau, A. Courville, and Y. Bengio. CoRR, (2012)

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Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy.

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Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy.