Understanding the difficulty of training deep feedforward neural networks X Glorot, Y Bengio Proceedings of the thirteenth international conference on artificial …, 2010 | 24766 | 2010 |
Deep sparse rectifier neural networks X Glorot, A Bordes, Y Bengio Proceedings of the fourteenth international conference on artificial …, 2011 | 12217 | 2011 |
beta-vae: Learning basic visual concepts with a constrained variational framework. I Higgins, L Matthey, A Pal, CP Burgess, X Glorot, MM Botvinick, ... ICLR (Poster) 3, 2017 | 5040 | 2017 |
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 | 2328 | 2018 |
Domain adaptation for large-scale sentiment classification: A deep learning approach X Glorot, A Bordes, Y Bengio Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 2281 | 2011 |
Contractive auto-encoders: Explicit invariance during feature extraction S Rifai, P Vincent, X Muller, X Glorot, Y Bengio Proceedings of the 28th international conference on international conference …, 2011 | 1989 | 2011 |
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 | 1085* | 2016 |
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 | 862 | 2019 |
A semantic matching energy function for learning with multi-relational data A Bordes, X Glorot, J Weston, Y Bengio Machine Learning, 1-27, 2012 | 853* | 2012 |
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing A Bordes, X Glorot, J Weston, Y Bengio | 493 | 2011 |
Unsupervised and transfer learning challenge: a deep learning approach G Mesnil, Y Dauphin, X Glorot, S Rifai, Y Bengio, I Goodfellow, E Lavoie, ... Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 97-110, 2012 | 288 | 2012 |
Higher order contractive auto-encoder S Rifai, G Mesnil, P Vincent, X Muller, Y Bengio, Y Dauphin, X Glorot Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 278 | 2011 |
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 | 202 | 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 | 196 | 2016 |
International Conference on Learning Representations I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ... ICLR 2017, Toulon, France, 2017 | 173 | 2017 |
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 | 88 | 2011 |
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records N Tomašev, N Harris, S Baur, A Mottram, X Glorot, JW Rae, M Zielinski, ... Nature Protocols 16 (6), 2765-2787, 2021 | 65 | 2021 |
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 | 51* | 2015 |
Generalizable medical image analysis using segmentation and classification neural networks J De Fauw, JR Ledsam, B Romera-paredes, S Nikolov, N Tomasev, ... US Patent App. 16/236,045, 2019 | 50 | 2019 |
Theano: A Python framework for fast computation of mathematical expressions. arXiv R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688 10, 2016 | 49 | 2016 |