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Z-Forcing: Training Stochastic Recurrent Networks.

A. Goyal, A. Sordoni, M. Côté, N. Ke, and Y. Bengio. NIPS, page 6713-6723. (2017)

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DNN's Sharpest Directions Along the SGD TrajectoryS. Jastrzębski, Z. Kenton, N. Ballas, A. Fischer, Y. Bengio, and A. Storkey. (2018)Support vector machines for improving the classification of brain PET images.M. Bonneville, J. Meunier, Y. Bengio, and J. Soucy. Medical Imaging: Image Processing, volume 3338 of SPIE Proceedings, SPIE, (1998)A Walk with SGD.C. Xing, D. Arpit, C. Tsirigotis, and Y. Bengio. CoRR, (2018)Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible.Y. Bengio, B. Scellier, O. Bilaniuk, J. Sacramento, and W. Senn. CoRR, (2016)A3T: Adversarially Augmented Adversarial Training.A. Erraqabi, A. Baratin, Y. Bengio, and S. Lacoste-Julien. CoRR, (2018)Dendritic error backpropagation in deep cortical microcircuits.J. Sacramento, R. Costa, Y. Bengio, and W. Senn. CoRR, (2018)Curriculum learning.Y. Bengio, J. Louradour, R. Collobert, and J. Weston. ICML, volume 382 of ACM International Conference Proceeding Series, page 41-48. ACM, (2009)Reinforced Imitation in Heterogeneous Action Space.K. Zolna, N. Rostamzadeh, Y. Bengio, S. Ahn, and P. Pinheiro. CoRR, (2019)Focused Hierarchical RNNs for Conditional Sequence Processing.N. Ke, K. Zolna, A. Sordoni, Z. Lin, A. Trischler, Y. Bengio, J. Pineau, L. Charlin, and C. Pal. ICML, volume 80 of Proceedings of Machine Learning Research, page 2559-2568. PMLR, (2018)An empirical evaluation of deep architectures on problems with many factors of variation.H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio. ICML, volume 227 of ACM International Conference Proceeding Series, page 473-480. ACM, (2007)

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Z-Forcing: Training Stochastic Recurrent Networks.

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Disambiguation of "Bengio, Yoshua"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Z-Forcing: Training Stochastic Recurrent Networks.

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Z-Forcing: Training Stochastic Recurrent Networks.