Scientific Publications & Preprints

• Semantic Hashing.
  Ruslan Salakhutdinov and Geoffrey Hinton
  International Journal of Approximate Reasoning, 2009
  [bibtex] [pdf]
  Earlier verision appeared in: SIGIR workshop on Information Retrieval and applications of Graphical Models (2007)
  [bibtex] [ps.gz, pdf]
  

• Learning Nonlinear Dynamic Models.
  John Langford, Ruslan Salakhutdinov and Tong Zhang.
  Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.
  [bibtex] [ps.gz][ pdf]
  

• Evaluation Methods for Topic Models.
  Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov and David Mimno.
  Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.
  [bibtex] [ pdf]
  

• Deep Boltzmann Machines
  Ruslan Salakhutdinov and Geoffrey Hinton
  12th International Conference on Artificial Intelligence and Statistics (2009).
  [bibtex] [ps.gz][ pdf]
  

• Evaluating probabilities under high-dimensional latent variable models.
  Iain Murray and Ruslan Salakhutdinov
  Neural Information Processing Systems 21 (NIPS 2009)
  [bibtex] [ pdf], Jan 2009
  

• Learning and Evaluating Boltzmann Machines
  Ruslan Salakhutdinov
  Technical Report UTML TR 2008-002, Dept. of Computer Science, University of Toronto
  [bibtex] [ps.gz][ pdf]
  This paper introduces a new Boltzmann machine learning algorithm that combines variational techniques and MCMC.
  

• On the Quantitative Analysis of Deep Belief Networks.
  Ruslan Salakhutdinov and Iain Murray
  In 25th International Conference on Machine Learning (ICML-2008)
  [bibtex] [ps.gz],[ pdf], [code]
  

• Bayesian Probabilistic Matrix Factorization using MCMC.
  Ruslan Salakhutdinov and Andriy Mnih
  In 25th International Conference on Machine Learning (ICML-2008)
  [bibtex] [ps.gz],[ pdf]
  

• Probabilistic Matrix Factorization.
  Ruslan Salakhutdinov and Andriy Mnih
  Neural Information Processing Systems 20 (NIPS 2008)
  [bibtex] [ps.gz][pdf], Jan 2008
  (accepted for an oral presentation)
  

• Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes.
  Ruslan Salakhutdinov and Geoffrey Hinton
  Neural Information Processing Systems 20 (NIPS 2008)
  [bibtex] [ps.gz][pdf], Jan 2008
  

• Restricted Boltzmann Machines for Collaborative Filtering.
  Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton
  ICML 2007
  [bibtex] [ps.gz][pdf]
  

• Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure.
  Ruslan Salakhutdinov and Geoffrey Hinton
  AI and Statistics 2007
  [bibtex] [ps.gz][ pdf]

• Reducing the Dimensionality of Data with Neural Networks.
  Geoffrey E. Hinton and Ruslan R. Salakhutdinov
  Science, 28 July 2006:
  Vol. 313. no. 5786, pp. 504 - 507
  [bibtex] [pdf][ Science Online]
  Supporting Online Material [pdf, Science Online]
  Matlab Code is available here
  Figures are available in eps format: [fig1, fig2, fig3, fig4]
  and in jpeg format: [fig1, fig2, fig3, fig4]
  

• Simultaneous Localization and Surveying with Multiple Agents.
  Sam Roweis & Ruslan Salakhutdinov (2005)
  In R. Murray-Smith, R. Shorten (eds), Switching and Learning in Feedback Systems (Springer LNCS vol 3355, 2005). pp. 313--332
  [bibtex] [pdf]

• Neighbourhood Component Analysis
  Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov
  Neural Information Processing Systems 17 (NIPS'04).
  [bibtex] [pdf]

• Semi-Supervised Mixture-of-Experts Classification
  Grigoris Karakoulas & Ruslan Salakhutdinov
  The Fourth IEEE International Conference on Data Mining (ICDM 04)
  [bibtex]

• On the Convergence of Bound Optimization Algorithms
  Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
  Uncertainty in Artificial Intelligence (UAI-2003). pp 509-516
  [bibtex] [ps.gz] [pdf]

• Optimization with EM and Expectation-Conjugate-Gradient
  Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
  International Conference on Machine Learning (ICML-2003). pp 672-679
  [bibtex] [ps.gz] [pdf]

• Adaptive Overrelaxed Bound Optimization Methods.
  Ruslan Salakhutdinov & Sam T. Roweis (2003).
  International Conference on Machine Learning (ICML-2003). pp 664-671
  [bibtex] [ps.gz] [pdf]

  Also check out demos on Adaptive vs Standard EM for Mixture of Factor Analyzers here and Mixture of Gaussians here

Technical Reports/Unpublished Manuscripts

• Notes on the KL-divergence between a Markov chain and its equilibrium distribution
  Iain Murray and Ruslan Salakhutdinov (2008)
  [pdf]

• Relationship between gradient and EM steps in latent variable models.
  Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2002).
  Unpublished Report. [draft version (sep.02)-->ps.gz(32K) pdf(70K)]

• Expectation Conjugate-Gradient: An Alternative to EM
  Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
  [draft version (june.02)-->ps.gz(186K) pdf(640K)]

Ruslan Salakhutdinov, Artificial Intelligence Group, Toronto Computer Science || www.cs.toronto.edu/~rsalakhu