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Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules.

S. Mittal, A. Lamb, A. Goyal, V. Voleti, M. Shanahan, G. Lajoie, M. Mozer, and Y. Bengio. ICML, volume 119 of Proceedings of Machine Learning Research, page 6972-6986. PMLR, (2020)

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Recurrent Independent Mechanisms.A. Goyal, A. Lamb, J. Hoffmann, S. Sodhani, S. Levine, Y. Bengio, and B. Schölkopf. ICLR, OpenReview.net, (2021)Graph Neural Networks with Learnable Structural and Positional Representations.V. Dwivedi, A. Luu, T. Laurent, Y. Bengio, and X. Bresson. ICLR, OpenReview.net, (2022)Chunked Autoregressive GAN for Conditional Waveform Synthesis.M. Morrison, R. Kumar, K. Kumar, P. Seetharaman, A. Courville, and Y. Bengio. ICLR, OpenReview.net, (2022)An Analysis of the Adaptation Speed of Causal Models.R. Priol, R. Babanezhad, Y. Bengio, and S. Lacoste-Julien. AISTATS, volume 130 of Proceedings of Machine Learning Research, page 775-783. PMLR, (2021)Multi-scale Feature Learning Dynamics: Insights for Double Descent.M. Pezeshki, A. Mitra, Y. Bengio, and G. Lajoie. ICML, volume 162 of Proceedings of Machine Learning Research, page 17669-17690. PMLR, (2022)Perceptual Generative Autoencoders.Z. Zhang, R. Zhang, Z. Li, Y. Bengio, and L. Paull. ICML, volume 119 of Proceedings of Machine Learning Research, page 11298-11306. PMLR, (2020)Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach.W. Du, Z. Lin, Y. Shen, T. O'Donnell, Y. Bengio, and Y. Zhang. ACL, page 6611-6628. Association for Computational Linguistics, (2020)Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks.G. Hacene, V. Gripon, M. Arzel, N. Farrugia, and Y. Bengio. NEWCAS, page 206-209. IEEE, (2020)The Z-Coder Adaptive Binary Coder.L. Bottou, P. Howard, and Y. Bengio. Data Compression Conference, page 13-22. IEEE Computer Society, (1998)Non-Local Manifold Tangent Learning.Y. Bengio, and M. Monperrus. NIPS, page 129-136. (2004)

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Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules.

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Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules.