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End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures.

G. Synnaeve, Q. Xu, J. Kahn, E. Grave, T. Likhomanenko, V. Pratap, A. Sriram, V. Liptchinsky, and R. Collobert. CoRR, (2019)

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Semi-Supervised Sequence Labeling with Self-Learned Features.Y. Qi, P. Kuksa, R. Collobert, K. Sadamasa, K. Kavukcuoglu, and J. Weston. ICDM, page 428-437. IEEE Computer Society, (2009)Rehabilitation of Count-based Models for Word Vector Representations.R. Lebret, and R. Collobert. CoRR, (2014)Natural Language Processing (almost) from ScratchR. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. CoRR, (2011)Letter-Based Speech Recognition with Gated ConvNets.V. Liptchinsky, G. Synnaeve, and R. Collobert. CoRR, (2017)Who Needs Words? Lexicon-Free Speech Recognition.T. Likhomanenko, G. Synnaeve, and R. Collobert. CoRR, (2019)Deep Learning via Semi-supervised Embedding.J. Weston, F. Ratle, H. Mobahi, and R. Collobert. Neural Networks: Tricks of the Trade (2nd ed.), volume 7700 of Lecture Notes in Computer Science, Springer, (2012)Semi-supervised Abstraction-Augmented String Kernel for Multi-level Bio-Relation Extraction.P. Kuksa, Y. Qi, B. Bai, R. Collobert, J. Weston, V. Pavlovic, and X. Ning. ECML/PKDD (2), volume 6322 of Lecture Notes in Computer Science, page 128-144. Springer, (2010)Learning to Refine Object Segments.P. Pinheiro, T. Lin, R. Collobert, and P. Dollár. ECCV (1), volume 9905 of Lecture Notes in Computer Science, page 75-91. Springer, (2016)Deep learning from temporal coherence in video.H. Mobahi, R. Collobert, and J. Weston. ICML, volume 382 of ACM International Conference Proceeding Series, page 737-744. ACM, (2009)N-gram-Based Low-Dimensional Representation for Document Classification.R. Lebret, and R. Collobert. ICLR (Workshop), (2015)

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End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures.

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End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures.