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Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning.

R. Islam, H. Zang, A. Goyal, A. Lamb, K. Kawaguchi, X. Li, R. Laroche, Y. Bengio, and R. des Combes. CoRR, (2022)

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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.Markovian Models for Sequential DataY. Bengio. Neural computing surveys, 2 (199): 129--162 (1999)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).Noisy Activation FunctionsC. Gulcehre, M. Moczulski, M. Denil, and Y. Bengio. (2016)cite arxiv:1603.00391v3.pdf.Unitary Evolution Recurrent Neural Networks.M. Arjovsky, A. Shah, and Y. Bengio. CoRR, (2015)Learning long-term dependencies with gradient descent is difficult.Y. Bengio, P. Simard, and P. Frasconi. IEEE Trans. Neural Networks, 5 (2): 157-166 (1994)Input-output HMMs for sequence processing.Y. Bengio, and P. Frasconi. IEEE Trans. Neural Networks, 7 (5): 1231-1249 (1996)Interpretable Convolutional Filters with SincNet.M. Ravanelli, and Y. Bengio. CoRR, (2018)Extracting and composing robust features with denoising autoencoders.P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol. ICML, volume 307 of ACM International Conference Proceeding Series, page 1096-1103. ACM, (2008)Workshop summary: Workshop on learning feature hierarchies.K. Yu, R. Salakhutdinov, Y. LeCun, G. Hinton, and Y. Bengio. ICML, volume 382 of ACM International Conference Proceeding Series, page 5. ACM, (2009)

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Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning.

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Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning.