Scientific Publications

Electronic versions are in gzipped postscript (.ps.gz) or in Acrobat PDF (.pdf).

  • Multimodal Learning with Deep Boltzmann Machines
    Nitish Srivastava and Ruslan Salakhutdinov
    To appear in Neural Information Processing Systems (NIPS 26), 2013, oral. [ pdf],
    Supplementary material [ zip].
    Code is available [ here].

  • Hamming Distance Metric Learning
    Mohammad Norouzi, David Fleet, and Ruslan Salakhutdinov
    To appear in Neural Information Processing Systems (NIPS 26), 2013 [ pdf], Supplementary material [ pdf].

  • A Better Way to Pretrain Deep Boltzmann Machines
    Ruslan Salakhutdinov and Geoffrey Hinton
    To appear in Neural Information Processing Systems (NIPS 26), 2013 [ pdf].

  • Matrix Reconstruction with the Local Max Norm.
    Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov
    To appear in Neural Information Processing Systems (NIPS 26), 2013 [ pdf], Supplementary material [ pdf].

  • Cardinality Restricted Boltzmann Machines
    Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard Zemel, and Ryan Adams.
    Neural Information Processing Systems (NIPS 26), 2013 [ pdf].


  • An Efficient Learning Procedure for Deep Boltzmann Machines
    Ruslan Salakhutdinov and Geoffrey Hinton
    Neural Computation August 2012, Vol. 24, No. 8: 1967 -- 2006. [ pdf],

  • Improving neural networks by preventing co-adaptation of feature detectors
    Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
    arXiv [ pdf],

  • Exploiting Compositionality to Explore a Large Space of Model Structures
    Roger Grosse, Ruslan Salakhutdinov, William Freeman, and Joshua Tenenbaum
    To appear in UAI 2012 [ pdf].
    Best student paper award (Congratulations Roger).

  • One-Shot Learning with a Hierarchical Nonparametric Bayesian Model
    Ruslan Salakhutdinov, Josh Tenenbaum, and Antonio Torralba
    JMLR WC&P Unsupervised and Transfer Learning, 2012, [ pdf] `

  • Deep Lambertian Networks
    Yichuan Tang , Ruslan Salakhut dinov, and Geoffrey Hinton
    The 29th International Conference on Machine Learning (ICML 2012) [ pdf],

  • Deep Mixtures of Factor Analysers
    Yichuan Tang , Ruslan Salakhut dinov, and Geoffrey Hinton
    The 29th International Conference on Machine Learning (ICML 2012) [ pdf],

  • Concept learning as motor program induction: A large-scale empirical study.
    Brenden Lake , Ruslan Salakhutdinov, and Josh Tenenbaum.
    Proceedings of the 34rd Annual Conference of the Cognitive Science Society, 2012 [ pdf], Supporting Info

  • Robust Boltzmann Machines for Recognition and Denoising
    Yichuan Tang , Ruslan Salakhut dinov, and Geoffrey Hinton
    IEEE Computer Vision and Pattern Recognition (CVPR) 2012. [ pdf]

  • Resource Configurable Spoken Query Detection using Deep Boltzmann Machines
    Yaodong Zhang, Ruslan Salakhutdinov, Hung-An Chang, and James Glass.
    37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2012 [ pdf]

  • Domain Adaptation: A Small Sample Statistical Approach
    Dean Foster, Sham Kakade, and Ruslan Salakhutdinov
    JMLR W&CP 15 (AISTATS), 2012 [ pdf]

  • Learning to Learn with Compound Hierarchical-Deep Models
    Ruslan Salakhutdinov, Josh Tenenbaum , Antonio Torralba
    Neural Information Processing Systems (NIPS 25), 2012 [ pdf]

  • Transfer Learning by Borrowing Examples
    Joseph Lim , Ruslan Salakhutdinov Antonio Torralba
    Neural Information Processing Systems (NIPS 25). 2012 [ pdf]

  • Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions
    Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nathan Srebro
    Neural Information Processing Systems (NIPS 25), 2012 [ pdf]
    Supplementary material [ pdf]


  • One-shot learning of simple visual concepts
    Brenden Lake , Ruslan Salakhutdinov, Jason Gross, and Josh Tenenbaum.
    Proceedings of the 33rd Annual Conference of the Cognitive Science Society, 2011 [ pdf], videos

  • Learning to Share Visual Appearance for Multiclass Object Detection
    Ruslan Salakhutdinov, Antonio Torralba , and Josh Tenenbaum.
    IEEE Computer Vision and Pattern Recognition (CVPR) 2011 [ pdf]

  • Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm.
    Ruslan Salakhutdinov and Nathan Srebro.
    Neural Information Processing Systems 24, 2011
    [bibtex] [ pdf]
    Earlier version: [arXiv:1002.2780v1], [ps.gz][ pdf]

  • Practical Large-Scale Optimization for Max-Norm Regularization.
    Jason Lee, Benjamin Recht, Ruslan Salakhutdinov, Nathan Srebro, and Joel A. Tropp
    Neural Information Processing Systems 24, 2011
    [bibtex] [ pdf]


  • Discovering Binary Codes for Documents by Learning Deep Generative Models.
    Geoffrey Hinton and Ruslan Salakhutdinov.
    Topics in Cognitive Science, 2010
    [bibtex] [ pdf]

  • One-Shot Learning with a Hierarchical Nonparametric Bayesian Model.
    Ruslan Salakhutdinov, Josh Tenenbaum, and Antonio Torralba.
    MIT Technical Report MIT-CSAIL-TR-2010-052, 2010, [ pdf]

  • Learning in Deep Boltzmann Machines using Adaptive MCMC.
    Ruslan Salakhutdinov.
    In 27th International Conference on Machine Learning (ICML-2010)
    [bibtex] [ps.gz], [ pdf]

  • Efficient Learning of Deep Boltzmann Machines.
    Ruslan Salakhutdinov and Hugo Larochelle.
    AI and Statistics, 2010
    [bibtex] [ps.gz][ pdf]

  • Learning in Markov Random Fields using Tempered Transitions.
    Ruslan Salakhutdinov.
    Neural Information Processing Systems 23, 2010
    [bibtex] [ps.gz][ pdf]

  • Replicated Softmax: an Undirected Topic Model.
    Ruslan Salakhutdinov and Geoffrey Hinton.
    Neural Information Processing Systems 23, 2010
    [bibtex] [ps.gz][pdf]

  • Modelling Relational Data using Bayesian Clustered Tensor Factorization.
    Ilya Sutskever, Ruslan Salakhutdinov, and Josh Tenenbaum.
    Neural Information Processing Systems 23, 2010
    [bibtex] [pdf]


  • Learning Deep Generative Models.
    Ruslan Salakhutdinov
    PhD Thesis, Sep 2009
    Dept. of Computer Science, University of Toronto
    [bibtex] [ps.gz][pdf]

  • 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 22 (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 21 (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 21 (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)]


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Ruslan Salakhutdinov, Department of Statistics, University of Toronto, http://www.utstat.toronto.edu/~rsalakhu/