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Hugo Larochelle
Hugo Larochelle
Google DeepMind & Mila
Zweryfikowany adres z google.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
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Practical bayesian optimization of machine learning algorithms
J Snoek, H Larochelle, RP Adams
Advances in neural information processing systems 25, 2012
102312012
Domain-adversarial training of neural networks
Y Ganin, E Ustinova, H Ajakan, P Germain, H Larochelle, F Laviolette, ...
Journal of machine learning research 17 (59), 1-35, 2016
98552016
Extracting and composing robust features with denoising autoencoders
P Vincent, H Larochelle, Y Bengio, PA Manzagol
Proceedings of the 25th international conference on Machine learning, 1096-1103, 2008
93442008
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.
P Vincent, H Larochelle, I Lajoie, Y Bengio, PA Manzagol, L Bottou
Journal of machine learning research 11 (12), 2010
92262010
Greedy layer-wise training of deep networks
Y Bengio, P Lamblin, D Popovici, H Larochelle, U Montreal
Advances in neural information processing systems 19, 153, 2007
71962007
Optimization as a model for few-shot learning
S Ravi, H Larochelle
International conference on learning representations, 2016
37772016
Brain tumor segmentation with deep neural networks
M Havaei, A Davy, D Warde-Farley, A Biard, A Courville, Y Bengio, C Pal, ...
Medical image analysis 35, 18-31, 2017
36572017
Deep learning with coherent nanophotonic circuits
Y Shen, NC Harris, S Skirlo, M Prabhu, T Baehr-Jones, M Hochberg, ...
Nature photonics 11 (7), 441-446, 2017
29412017
Autoencoding beyond pixels using a learned similarity metric
ABL Larsen, SK Sønderby, H Larochelle, O Winther
International conference on machine learning, 1558-1566, 2016
26462016
Meta-learning for semi-supervised few-shot classification
M Ren, E Triantafillou, S Ravi, J Snell, K Swersky, JB Tenenbaum, ...
arXiv preprint arXiv:1803.00676, 2018
16192018
Efficient learning of deep boltzmann machines
R Salakhutdinov, H Larochelle
International Conference on Artificial Intelligence and Statistics, 2010
1520*2010
Exploring strategies for training deep neural networks.
H Larochelle, Y Bengio, J Louradour, P Lamblin
Journal of machine learning research 10 (1), 2009
14512009
An empirical evaluation of deep architectures on problems with many factors of variation
H Larochelle, D Erhan, A Courville, J Bergstra, Y Bengio
Proceedings of the 24th international conference on Machine learning, 473-480, 2007
14262007
Describing videos by exploiting temporal structure
L Yao, A Torabi, K Cho, N Ballas, C Pal, H Larochelle, A Courville
Proceedings of the IEEE international conference on computer vision, 4507-4515, 2015
13352015
Proceedings of the 32nd international conference on neural information processing systems
S Bengio, HM Wallach, H Larochelle, K Grauman, N Cesa-Bianchi
Curran Associates Inc., 2018
12882018
Machine behaviour
I Rahwan, M Cebrian, N Obradovich, J Bongard, JF Bonnefon, C Breazeal, ...
Nature 568 (7753), 477-486, 2019
11172019
Classification using discriminative restricted boltzmann machines
H Larochelle, Y Bengio
Proceedings of the 25th international conference on Machine learning, 536-543, 2008
10402008
Made: Masked autoencoder for distribution estimation
M Germain, K Gregor, I Murray, H Larochelle
International conference on machine learning, 881-889, 2015
9732015
Meta-dataset: A dataset of datasets for learning to learn from few examples
E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ...
arXiv preprint arXiv:1903.03096, 2019
7042019
The Neural Autoregressive Distribution Estimator
H Larochelle, I Murray
AISTATS, 2011
7012011
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