| Understanding the difficulty of training deep feedforward neural networks X Glorot, Y Bengio Proceedings of the thirteenth international conference on artificial …, 2010 | 11874 | 2010 |
| Deep sparse rectifier neural networks X Glorot, A Bordes, Y Bengio Proceedings of the fourteenth international conference on artificial …, 2011 | 6409 | 2011 |
| Domain adaptation for large-scale sentiment classification: A deep learning approach X Glorot, A Bordes, Y Bengio ICML, 2011 | 1723 | 2011 |
| beta-vae: Learning basic visual concepts with a constrained variational framework I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ... | 1530 | 2016 |
| Contractive auto-encoders: Explicit invariance during feature extraction S Rifai, P Vincent, X Muller, X Glorot, Y Bengio Icml, 2011 | 1319 | 2011 |
| Clinically applicable deep learning for diagnosis and referral in retinal disease J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ... Nature medicine 24 (9), 1342-1350, 2018 | 886 | 2018 |
| 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 | 757* | 2016 |
| A semantic matching energy function for learning with multi-relational data A Bordes, X Glorot, J Weston, Y Bengio Machine Learning, 1-27, 2012 | 501 | 2012 |
| Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing A Bordes, X Glorot, J Weston, Y Bengio | 355 | 2011 |
| A clinically applicable approach to continuous prediction of future acute kidney injury N Tomašev, X Glorot, JW Rae, M Zielinski, H Askham, A Saraiva, ... Nature 572 (7767), 116-119, 2019 | 255 | 2019 |
| Unsupervised and transfer learning challenge: a deep learning approach GMY Dauphin, X Glorot, S Rifai, Y Bengio, I Goodfellow, E Lavoie, ... Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 97-110, 2012 | 212 | 2012 |
| Higher order contractive auto-encoder S Rifai, G Mesnil, P Vincent, X Muller, Y Bengio, Y Dauphin, X Glorot Joint European conference on machine learning and knowledge discovery in …, 2011 | 185 | 2011 |
| Early visual concept learning with unsupervised deep learning I Higgins, L Matthey, X Glorot, A Pal, B Uria, C Blundell, S Mohamed, ... arXiv preprint arXiv:1606.05579, 2016 | 126 | 2016 |
| Deep learners benefit more from out-of-distribution examples Y Bengio, F Bastien, A Bergeron, N Boulanger-Lewandowski, T Breuel, ... JMLR W&CP: Proceedings of the Fourteenth International Conference on …, 2011 | 120 | 2011 |
| Adding noise to the input of a model trained with a regularized objective S Rifai, X Glorot, Y Bengio, P Vincent arXiv preprint arXiv:1104.3250, 2011 | 53 | 2011 |
| Large-scale learning of embeddings with reconstruction sampling Y Dauphin, X Glorot, Y Bengio ICML, 2011 | 39 | 2011 |
| Deep self-taught learning for handwritten character recognition F Bastien, Y Bengio, A Bergeron, N Boulanger-Lewandowski, T Breuel, ... arXiv preprint arXiv:1009.3589, 2010 | 22* | 2010 |
| Unsupervised Learning of Semantics of Object Detections for Scene Categorization G Mesnil, S Rifai, A Bordes, X Glorot, Y Bengio, P Vincent Pattern Recognition Applications and Methods, 209-224, 2015 | 18 | 2015 |
| Learning invariant features through local space contraction S Rifai, X Muller, X Glorot, G Mesnil, Y Bengio, P Vincent arXiv preprint arXiv:1104.4153, 2011 | 17 | 2011 |
| Unsupervised and Transfer Learning under Uncertainty-From Object Detections to Scene Categorization. G Mesnil, S Rifai, A Bordes, X Glorot, Y Bengio, P Vincent ICPRAM, 345-354, 2013 | 11 | 2013 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFAREmail verificata su umontreal.ca
