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Jan Chorowski
Jan Chorowski
Pathway
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Attention-based models for speech recognition
JK Chorowski, D Bahdanau, D Serdyuk, K Cho, Y Bengio
Advances in neural information processing systems 28, 2015
31042015
End-to-end attention-based large vocabulary speech recognition
D Bahdanau, J Chorowski, D Serdyuk, P Brakel, Y Bengio
2016 IEEE international conference on acoustics, speech and signal …, 2016
14512016
State-of-the-art speech recognition with sequence-to-sequence models
CC Chiu, TN Sainath, Y Wu, R Prabhavalkar, P Nguyen, Z Chen, ...
2018 IEEE international conference on acoustics, speech and signal …, 2018
13952018
Regularizing neural networks by penalizing confident output distributions
G Pereyra, G Tucker, J Chorowski, Ł Kaiser, G Hinton
arXiv preprint arXiv:1701.06548, 2017
11742017
Theano: A Python framework for fast computation of mathematical expressions
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv e-prints, arXiv: 1605.02688, 2016
1067*2016
Robust 3d action recognition with random occupancy patterns
J Wang, Z Liu, J Chorowski, Z Chen, Y Wu
Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012
5992012
End-to-end continuous speech recognition using attention-based recurrent nn: First results
J Chorowski, D Bahdanau, K Cho, Y Bengio
arXiv preprint arXiv:1412.1602, 2014
5782014
Towards better decoding and language model integration in sequence to sequence models
J Chorowski, N Jaitly
arXiv preprint arXiv:1612.02695, 2016
4092016
Sequence-to-sequence models can directly translate foreign speech
RJ Weiss, J Chorowski, N Jaitly, Y Wu, Z Chen
arXiv preprint arXiv:1703.08581, 2017
3902017
Unsupervised speech representation learning using wavenet autoencoders
J Chorowski, RJ Weiss, S Bengio, A Van Den Oord
IEEE/ACM transactions on audio, speech, and language processing 27 (12 …, 2019
3722019
Review and performance comparison of SVM-and ELM-based classifiers
J Chorowski, J Wang, JM Zurada
Neurocomputing 128, 507-516, 2014
2232014
Blocks and fuel: Frameworks for deep learning
B Van Merriënboer, D Bahdanau, V Dumoulin, D Serdyuk, ...
arXiv preprint arXiv:1506.00619, 2015
2072015
Lingvo: a modular and scalable framework for sequence-to-sequence modeling
J Shen, P Nguyen, Y Wu, Z Chen, MX Chen, Y Jia, A Kannan, T Sainath, ...
arXiv preprint arXiv:1902.08295, 2019
1972019
Learning understandable neural networks with nonnegative weight constraints
J Chorowski, JM Zurada
IEEE transactions on neural networks and learning systems 26 (1), 62-69, 2014
1602014
Extracting rules from neural networks as decision diagrams
J Chorowski, JM Zurada
IEEE Transactions on Neural Networks 22 (12), 2435-2446, 2011
632011
Input switched affine networks: An RNN architecture designed for interpretability
JN Foerster, J Gilmer, J Sohl-Dickstein, J Chorowski, D Sussillo
International conference on machine learning, 1136-1145, 2017
47*2017
Task loss estimation for sequence prediction
D Bahdanau, D Serdyuk, P Brakel, NR Ke, J Chorowski, A Courville, ...
arXiv preprint arXiv:1511.06456, 2015
332015
Robust training of vector quantized bottleneck models
A Łańcucki, J Chorowski, G Sanchez, R Marxer, N Chen, HJGA Dolfing, ...
2020 International Joint Conference on Neural Networks (IJCNN), 1-7, 2020
312020
Speech recognition with sequence-to-sequence models
RP Prabhavalkar, Z Chen, B Li, CC Chiu, KK Rao, Y Wu, RJ Weiss, ...
US Patent 11,145,293, 2021
272021
A convolutional deep markov model for unsupervised speech representation learning
S Khurana, A Laurent, WN Hsu, J Chorowski, A Lancucki, R Marxer, ...
arXiv preprint arXiv:2006.02547, 2020
252020
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Artículos 1–20