Travelled to:1 × China
1 × Finland
1 × France
1 × Sweden
1 × United Kingdom
4 × USA
Collaborated with:Y.Bengio R.S.Zemel I.Murray B.Uria M.Havaei P.Jodoin L.Charlin M.Volkovs G.E.Dahl R.P.Adams M.Germain K.Gregor A.Lacoste M.Marchand F.Laviolette P.Vincent P.Manzagol L.Bazzani N.d.Freitas V.Murino J.Ting D.Erhan A.C.Courville J.Bergstra
Talks about:learn (3) deep (3) boltzmann (2) autoencod (2) restrict (2) classif (2) machin (2) estim (2) brain (2) architectur (1)
Person: Hugo Larochelle
DBLP: Larochelle:Hugo
Contributed to:
20152014
2014
2014
20122012201120082007Wrote 11 papers:
- ICML-2015-GermainGML #estimation #named
- MADE: Masked Autoencoder for Distribution Estimation (MG, KG, IM, HL), pp. 881–889.
- ICML-c1-2014-LacosteMLL #learning
- Agnostic Bayesian Learning of Ensembles (AL, MM, FL, HL), pp. 611–619.
- ICML-c1-2014-UriaML
- A Deep and Tractable Density Estimator (BU, IM, HL), pp. 467–475.
- ICPR-2014-HavaeiJL #classification #interactive #performance #segmentation
- Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification (MH, PMJ, HL), pp. 556–561.
- KDD-2014-CharlinZL #collaboration #library
- Leveraging user libraries to bootstrap collaborative filtering (LC, RSZ, HL), pp. 173–182.
- CIKM-2012-VolkovsLZ #learning #rank
- Learning to rank by aggregating expert preferences (MV, HL, RSZ), pp. 843–851.
- ICML-2012-DahlAL #strict #word
- Training Restricted Boltzmann Machines on Word Observations (GED, RPA, HL), p. 152.
- ICML-2011-BazzaniFLMT #learning #network #policy #recognition #video
- Learning attentional policies for tracking and recognition in video with deep networks (LB, NdF, HL, VM, JAT), pp. 937–944.
- ICML-2008-LarochelleB #classification #strict #using
- Classification using discriminative restricted Boltzmann machines (HL, YB), pp. 536–543.
- ICML-2008-VincentLBM #robust
- Extracting and composing robust features with denoising autoencoders (PV, HL, YB, PAM), pp. 1096–1103.
- ICML-2007-LarochelleECBB #architecture #empirical #evaluation #problem
- An empirical evaluation of deep architectures on problems with many factors of variation (HL, DE, ACC, JB, YB), pp. 473–480.