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Dzmitry Bahdanau
Dzmitry Bahdanau
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Title
Cited by
Cited by
Year
Neural machine translation by jointly learning to align and translate
D Bahdanau, K Cho, Y Bengio
arXiv preprint arXiv:1409.0473, 2014
288732014
Learning phrase representations using RNN encoder-decoder for statistical machine translation
K Cho, B Van Merriënboer, C Gulcehre, D Bahdanau, F Bougares, ...
arXiv preprint arXiv:1406.1078, 2014
243322014
On the properties of neural machine translation: Encoder-decoder approaches
K Cho, B Van Merriënboer, D Bahdanau, Y Bengio
arXiv preprint arXiv:1409.1259, 2014
68922014
Attention-based models for speech recognition
JK Chorowski, D Bahdanau, D Serdyuk, K Cho, Y Bengio
Advances in neural information processing systems 28, 2015
27632015
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
13212016
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
1041*2016
Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv 2014
K Cho, B Van Merriënboer, C Gulcehre, D Bahdanau, F Bougares, ...
arXiv preprint arXiv:1406.1078, 2020
8462020
An actor-critic algorithm for sequence prediction
D Bahdanau, P Brakel, K Xu, A Goyal, R Lowe, J Pineau, A Courville, ...
arXiv preprint arXiv:1607.07086, 2016
5982016
Neural machine translation by jointly learning to align and translate. arXiv 2014
D Bahdanau, K Cho, Y Bengio
arXiv preprint arXiv:1409.0473, 2014
5932014
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
5342014
Neural machine translation by jointly learning to align and translate (2014)
D Bahdanau, K Cho, Y Bengio
arXiv preprint arXiv:1409.0473, 2018
4792018
BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop
M Chevalier-Boisvert, D Bahdanau, S Lahlou, L Willems, C Saharia, ...
arXiv preprint arXiv:1810.08272, 2018
270*2018
Blocks and fuel: Frameworks for deep learning
B Van Merriënboer, D Bahdanau, V Dumoulin, D Serdyuk, ...
arXiv preprint arXiv:1506.00619, 2015
2002015
Systematic generalization: What is required and can it be learned?
D Bahdanau, S Murty, M Noukhovitch, TH Nguyen, H de Vries, A Courville
arXiv preprint arXiv:1811.12889, 2018
1662018
Learning to understand goal specifications by modelling reward
D Bahdanau, F Hill, J Leike, E Hughes, A Hosseini, P Kohli, ...
arXiv preprint arXiv:1806.01946, 2018
149*2018
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control
N Jaques, S Gu, D Bahdanau, JM Hernández-Lobato, RE Turner, D Eck
International Conference on Machine Learning, 1645-1654, 2017
1412017
PICARD: Parsing incrementally for constrained auto-regressive decoding from language models
T Scholak, N Schucher, D Bahdanau
arXiv preprint arXiv:2109.05093, 2021
1042021
Learning to compute word embeddings on the fly
D Bahdanau, T Bosc, S Jastrzębski, E Grefenstette, P Vincent, Y Bengio
arXiv preprint arXiv:1706.00286, 2017
872017
Overcoming the curse of sentence length for neural machine translation using automatic segmentation
J Pouget-Abadie, D Bahdanau, B Van Merrienboer, K Cho, Y Bengio
arXiv preprint arXiv:1409.1257, 2014
872014
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
K Cho, B van Merrienboer, C Gulcehre, D Bahdanau, F Bougares, ...
Association for Computational Linguistics. https://doi. org/10.3115/v1/d14-1179, 2014
782014
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