| A survey of data augmentation approaches for NLP SY Feng, V Gangal, J Wei, S Chandar, S Vosoughi, T Mitamura, E Hovy arXiv preprint arXiv:2105.03075, 2021 | 980 | 2021 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 483 | 2020 |
| Guesswhat?! visual object discovery through multi-modal dialogue H De Vries, F Strub, S Chandar, O Pietquin, H Larochelle, A Courville Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 479 | 2017 |
| An autoencoder approach to learning bilingual word representations S Chandar, S Lauly, H Larochelle, M Khapra, B Ravindran, VC Raykar, ... Advances in Neural Information Processing Systems, 1853-1861, 2014 | 400 | 2014 |
| A deep reinforcement learning chatbot IV Serban, C Sankar, M Germain, S Zhang, Z Lin, S Subramanian, T Kim, ... arXiv preprint arXiv:1709.02349, 2017 | 377 | 2017 |
| Generating factoid questions with recurrent neural networks: The 30m factoid question-answer corpus IV Serban, A García-Durán, C Gulcehre, S Ahn, S Chandar, A Courville, ... arXiv preprint arXiv:1603.06807, 2016 | 370 | 2016 |
| Post-hoc interpretability for neural nlp: A survey A Madsen, S Reddy, S Chandar ACM Computing Surveys 55 (8), 1-42, 2022 | 291 | 2022 |
| Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph A Saha, V Pahuja, M Khapra, K Sankaranarayanan, S Chandar Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 256 | 2018 |
| Correlational neural networks S Chandar, MM Khapra, H Larochelle, B Ravindran Neural computation 28 (2), 257-285, 2016 | 209 | 2016 |
| Learning to navigate the synthetically accessible chemical space using reinforcement learning SK Gottipati, B Sattarov, S Niu, Y Pathak, H Wei, S Liu, S Blackburn, ... International conference on machine learning, 3668-3679, 2020 | 172 | 2020 |
| Do neural dialog systems use the conversation history effectively? an empirical study C Sankar, S Subramanian, C Pal, S Chandar, Y Bengio arXiv preprint arXiv:1906.01603, 2019 | 151 | 2019 |
| An empirical investigation of the role of pre-training in lifelong learning SV Mehta, D Patil, S Chandar, E Strubell Journal of Machine Learning Research 24 (214), 1-50, 2023 | 146 | 2023 |
| Dynamic neural turing machine with continuous and discrete addressing schemes C Gulcehre, S Chandar, K Cho, Y Bengio Neural computation 30 (4), 857-884, 2018 | 135* | 2018 |
| Toward training recurrent neural networks for lifelong learning S Sodhani, S Chandar, Y Bengio Neural computation 32 (1), 1-35, 2020 | 130* | 2020 |
| Hierarchical memory networks S Chandar, S Ahn, H Larochelle, P Vincent, G Tesauro, Y Bengio arXiv preprint arXiv:1605.07427, 2016 | 112 | 2016 |
| Patchup: A feature-space block-level regularization technique for convolutional neural networks M Faramarzi, M Amini, A Badrinaaraayanan, V Verma, S Chandar Proceedings of the AAAI conference on artificial intelligence 36 (1), 589-597, 2022 | 83 | 2022 |
| Memory augmented neural networks with wormhole connections C Gulcehre, S Chandar, Y Bengio arXiv preprint arXiv:1701.08718, 2017 | 82 | 2017 |
| Towards non-saturating recurrent units for modelling long-term dependencies S Chandar, C Sankar, E Vorontsov, SE Kahou, Y Bengio Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3280-3287, 2019 | 80 | 2019 |
| Bridge correlational neural networks for multilingual multimodal representation learning J Rajendran, MM Khapra, S Chandar, B Ravindran arXiv preprint arXiv:1510.03519, 2015 | 78 | 2015 |
| Clustering is efficient for approximate maximum inner product search A Auvolat, S Chandar, P Vincent, H Larochelle, Y Bengio arXiv preprint arXiv:1507.05910, 2015 | 48 | 2015 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
