Xavier Glorot
Xavier Glorot
Vahvistettu sähköpostiosoite verkkotunnuksessa
Understanding the difficulty of training deep feedforward neural networks
X Glorot, Y Bengio
Proceedings of the thirteenth international conference on artificial …, 2010
Deep sparse rectifier neural networks
X Glorot, A Bordes, Y Bengio
Proceedings of the fourteenth international conference on artificial …, 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, ...
Domain adaptation for large-scale sentiment classification: A deep learning approach
X Glorot, A Bordes, Y Bengio
ICML, 2011
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
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
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
A semantic matching energy function for learning with multi-relational data
A Bordes, X Glorot, J Weston, Y Bengio
Machine Learning, 1-27, 2012
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
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
A Bordes, X Glorot, J Weston, Y Bengio
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
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
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
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
Large-scale learning of embeddings with reconstruction sampling
Y Dauphin, X Glorot, Y Bengio
ICML, 2011
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
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
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
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
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
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Artikkelit 1–20