Travelled to:1 × Canada
1 × China
1 × Finland
1 × France
1 × Israel
1 × United Kingdom
4 × USA
Collaborated with:G.E.Hinton ∅ Y.Tang A.Mnih S.T.Roweis I.Murray R.B.Grosse R.Kiros R.S.Zemel N.Srivastava E.Mansimov J.Langford T.Zhang Z.Ghahramani H.M.Wallach D.M.Mimno K.Xu J.Ba K.Cho A.C.Courville Y.Bengio
Talks about:deep (5) learn (4) factor (3) model (3) use (3) boltzmann (2) gradient (2) network (2) neural (2) method (2)
Person: Ruslan Salakhutdinov
DBLP: Salakhutdinov:Ruslan
Contributed to:
20152014
20142013201220102009200820072003Wrote 16 papers:
- ICML-2015-GrosseS #matrix #scalability
- Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix (RBG, RS), pp. 2304–2313.
- ICML-2015-SrivastavaMS #learning #using #video
- Unsupervised Learning of Video Representations using LSTMs (NS, EM, RS), pp. 843–852.
- ICML-2015-XuBKCCSZB #generative #image #visual notation
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention (KX, JB, RK, KC, ACC, RS, RSZ, YB), pp. 2048–2057.
- ICML-c2-2014-KirosSZ #modelling #multimodal
- Multimodal Neural Language Models (RK, RS, RSZ), pp. 595–603.
- KDD-2014-Salakhutdinov #learning
- Deep learning (RS), p. 1973.
- ICML-c3-2013-TangSH
- Tensor Analyzers (YT, RS, GEH), pp. 163–171.
- ICML-2012-TangSH
- Deep Mixtures of Factor Analysers (YT, RS, GEH), p. 147.
- ICML-2012-TangSH12a #network
- Deep Lambertian Networks (YT, RS, GEH), p. 184.
- ICML-2010-Salakhutdinov #adaptation #learning #using
- Learning Deep Boltzmann Machines using Adaptive MCMC (RS), pp. 943–950.
- ICML-2009-LangfordSZ #learning #modelling
- Learning nonlinear dynamic models (JL, RS, TZ), pp. 593–600.
- ICML-2009-WallachMSM #evaluation #modelling #topic
- Evaluation methods for topic models (HMW, IM, RS, DMM), pp. 1105–1112.
- ICML-2008-SalakhutdinovM #analysis #network #on the
- On the quantitative analysis of deep belief networks (RS, IM), pp. 872–879.
- ICML-2008-SalakhutdinovM08a #markov #matrix #monte carlo #probability #using
- Bayesian probabilistic matrix factorization using Markov chain Monte Carlo (RS, AM), pp. 880–887.
- ICML-2007-SalakhutdinovMH #collaboration #strict
- Restricted Boltzmann machines for collaborative filtering (RS, AM, GEH), pp. 791–798.
- ICML-2003-SalakhutdinovR #adaptation #bound #optimisation
- Adaptive Overrelaxed Bound Optimization Methods (RS, STR), pp. 664–671.
- ICML-2003-SalakhutdinovRG #optimisation
- Optimization with EM and Expectation-Conjugate-Gradient (RS, STR, ZG), pp. 672–679.