| Neural machine translation by jointly learning to align and translate D Bahdanau arXiv preprint arXiv:1409.0473, 2014 | 36736 | 2014 |
| Learning phrase representations using RNN encoder-decoder for statistical machine translation K Cho arXiv preprint arXiv:1406.1078, 2014 | 32218 | 2014 |
| On the Properties of Neural Machine Translation: Encoder-decoder Approaches K Cho arXiv preprint arXiv:1409.1259, 2014 | 9468 | 2014 |
| Attention-based models for speech recognition JK Chorowski, D Bahdanau, D Serdyuk, K Cho, Y Bengio Advances in neural information processing systems 28, 2015 | 3334 | 2015 |
| 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 | 1528 | 2016 |
| Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv 2014 K Cho, B Van Merrienboer, C Gulcehre, D Bahdanau, F Bougares, ... arXiv preprint arXiv:1406.1078, 2020 | 1326 | 2020 |
| 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 | 1103* | 2016 |
| Neural machine translation by jointly learning to align and translate. arXiv 2014 D Bahdanau, K Cho, Y Bengio arXiv preprint arXiv:1409.0473, 2014 | 864 | 2014 |
| 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 | 734 | 2016 |
| Starcoder: may the source be with you! R Li, LB Allal, Y Zi, N Muennighoff, D Kocetkov, C Mou, M Marone, C Akiki, ... arXiv preprint arXiv:2305.06161, 2023 | 684 | 2023 |
| 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 | 609 | 2014 |
| 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 | 408* | 2018 |
| PICARD: Parsing incrementally for constrained auto-regressive decoding from language models T Scholak, N Schucher, D Bahdanau arXiv preprint arXiv:2109.05093, 2021 | 339 | 2021 |
| The stack: 3 tb of permissively licensed source code D Kocetkov, R Li, LB Allal, J Li, C Mou, CM Ferrandis, Y Jernite, M Mitchell, ... arXiv preprint arXiv:2211.15533, 2022 | 234 | 2022 |
| Blocks and fuel: Frameworks for deep learning B Van Merriënboer, D Bahdanau, V Dumoulin, D Serdyuk, ... arXiv preprint arXiv:1506.00619, 2015 | 206 | 2015 |
| SantaCoder: don't reach for the stars! LB Allal, R Li, D Kocetkov, C Mou, C Akiki, CM Ferrandis, N Muennighoff, ... arXiv preprint arXiv:2301.03988, 2023 | 201 | 2023 |
| 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 | 200* | 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 | 191 | 2017 |
| 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 | 190 | 2018 |
| Evaluating the text-to-sql capabilities of large language models N Rajkumar, R Li, D Bahdanau arXiv preprint arXiv:2204.00498, 2022 | 121 | 2022 |