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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)

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Temporal Pooling and Multiscale Learning for Automatic Annotation and Ranking of Music Audio.P. Hamel, S. Lemieux, Y. Bengio, and D. Eck. ISMIR, page 729-734. University of Miami, (2011)Guest Introduction: Special Issue on New Methods for Model Selection and Model Combination.Y. Bengio, and D. Schuurmans. Machine Learning, 48 (1-3): 5-7 (2002)Learning Structured Embeddings of Knowledge Bases.A. Bordes, J. Weston, R. Collobert, and Y. Bengio. AAAI, AAAI Press, (2011)Deep Belief Networks Are Compact Universal Approximators.N. Roux, and Y. Bengio. Neural Computation, 22 (8): 2192-2207 (2010)Phonetically motivated acoustic parameters for continuous speech recognition using artificial neural networks.Y. Bengio, R. de Mori, G. Flammia, and R. Kompe. Speech Communication, 11 (2-3): 261-271 (1992)Browsing through High Quality Document Images with DjVu.P. Haffner, L. Bottou, P. Howard, P. Simard, Y. Bengio, and Y. LeCun. ADL, page 309-318. IEEE Computer Society, (1998)Taking on the curse of dimensionality in joint distributions using neural networks.S. Bengio, and Y. Bengio. IEEE Trans. Neural Netw. Learning Syst., 11 (3): 550-557 (2000)Non-Local Manifold Tangent Learning.Y. Bengio, and M. Monperrus. NIPS, page 129-136. (2004)An Input Output HMM Architecture.Y. Bengio, and P. Frasconi. NIPS, page 427-434. MIT Press, (1994)Incorporating Second-Order Functional Knowledge for Better Option Pricing.C. Dugas, Y. Bengio, F. Bélisle, C. Nadeau, and R. Garcia. NIPS, page 472-478. MIT Press, (2000)

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Learning long-term dependencies with gradient descent is difficult.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Learning long-term dependencies with gradient descent is difficult.

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Learning long-term dependencies with gradient descent is difficult.