| Fitnets: Hints for thin deep nets A Romero, N Ballas, SE Kahou, A Chassang, C Gatta, Y Bengio arXiv preprint arXiv:1412.6550, 2014 | 4122 | 2014 |
| 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 | 1101* | 2016 |
| The" something something" video database for learning and evaluating visual common sense R Goyal, S Ebrahimi Kahou, V Michalski, J Materzynska, S Westphal, ... Proceedings of the IEEE international conference on computer vision, 5842-5850, 2017 | 963 | 2017 |
| Emonets: Multimodal deep learning approaches for emotion recognition in video SE Kahou, X Bouthillier, P Lamblin, C Gulcehre, V Michalski, K Konda, ... Journal on Multimodal User Interfaces 10, 99-111, 2016 | 457 | 2016 |
| Combining Modality Specific Deep Neural Networks for Emotion Recognition in Video SE Kahou, C Pal, X Bouthillier, P Froumenty, Ç Gülçehre, R Memisevic, ... Proceedings of the 15th ACM on International Conference on Multimodal …, 2013 | 397 | 2013 |
| Recurrent neural networks for emotion recognition in video S Ebrahimi Kahou, V Michalski, K Konda, R Memisevic, C Pal Proceedings of the 2015 ACM on international conference on multimodal …, 2015 | 394 | 2015 |
| Do deep convolutional nets really need to be deep and convolutional? G Urban, KJ Geras, SE Kahou, O Aslan, S Wang, R Caruana, A Mohamed, ... arXiv preprint arXiv:1603.05691, 2016 | 272 | 2016 |
| Towards deep conversational recommendations R Li, S Ebrahimi Kahou, H Schulz, V Michalski, L Charlin, C Pal Advances in neural information processing systems 31, 2018 | 244 | 2018 |
| Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events E Racah, C Beckham, T Maharaj, S Ebrahimi Kahou, M Prabhat, C Pal Advances in neural information processing systems 30, 2017 | 205 | 2017 |
| Figureqa: An annotated figure dataset for visual reasoning SE Kahou, V Michalski, A Atkinson, Á Kádár, A Trischler, Y Bengio arXiv preprint arXiv:1710.07300, 2017 | 155 | 2017 |
| Tell, draw, and repeat: Generating and modifying images based on continual linguistic instruction A El-Nouby, S Sharma, H Schulz, D Hjelm, LE Asri, SE Kahou, Y Bengio, ... Proceedings of the IEEE International Conference on Computer Vision, 10304-10312, 2019 | 111* | 2019 |
| Ratm: Recurrent attentive tracking model S Ebrahimi Kahou, V Michalski, R Memisevic, C Pal, P Vincent Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 106* | 2017 |
| Chatpainter: Improving text to image generation using dialogue S Sharma, D Suhubdy, V Michalski, SE Kahou, Y Bengio arXiv preprint arXiv:1802.08216, 2018 | 89 | 2018 |
| Deep-hurricane-tracker: Tracking and forecasting extreme climate events S Kim, H Kim, J Lee, S Yoon, SE Kahou, K Kashinath, M Prabhat 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 1761-1769, 2019 | 83 | 2019 |
| Highres-net: Recursive fusion for multi-frame super-resolution of satellite imagery M Deudon, A Kalaitzis, I Goytom, MR Arefin, Z Lin, K Sankaran, ... arXiv preprint arXiv:2002.06460, 2020 | 79* | 2020 |
| Accounting for variance in machine learning benchmarks X Bouthillier, P Delaunay, M Bronzi, A Trofimov, B Nichyporuk, J Szeto, ... Proceedings of Machine Learning and Systems 3, 747-769, 2021 | 73 | 2021 |
| 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 | 57 | 2019 |
| Facial Expression Analysis Based on High Dimensional Binary Features SE Kahou, P Froumenty, C Pal Computer Vision - ECCV 2014 Workshops, Part II, 135-147, 2015 | 44 | 2015 |
| Multi-image super-resolution for remote sensing using deep recurrent networks MR Arefin, V Michalski, PL St-Charles, A Kalaitzis, S Kim, SE Kahou, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 35 | 2020 |
| Segmenting and tracking extreme climate events using neural networks M Mudigonda, S Kim, A Mahesh, S Kahou, K Kashinath, D Williams, ... Deep Learning for Physical Sciences (DLPS) Workshop, held with NIPS Conference, 2017 | 30 | 2017 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
