Ruslan Salakhutdinov

Ruslan Salakhutdinov />
</td><td WIDTH=  Assistant Professor,
Department of Computer Science and
Department of Statistical Sciences
University of Toronto

My Contact Info

Quick link to my papers, invited talks, and short bio.
My Google Scholar Profile.

I have received Alfred P. Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and Google Faculty Award.


Prospective students -- please read this to ensure that I read your email.

I am teaching:


Consider submitting a paper to our NIPS 2013 Deep Learning workshop.

I gave a tutorial on Deep Learning at the International Symposium on Biomedical Imaging.
Check out how Toronto team won the Merck Molecular Activity Challenge.

IPAM Graduate Summer School on Deep Learning and Feature Learning.
See our recent CVPR tutorial on Deep Learning Methods for Vision.

Recent Papers:

    Annealing between Distributions by Averaging Moments
    Roger Grosse, Chris Maddison, and Ruslan Salakhutdinov
    In Neural Information Processing Systems (NIPS 27), oral, [pdf], Supplementary material [ pdf].

    Discriminative Transfer Learning with Tree-based Priors
    Nitish Srivastava and Ruslan Salakhutdinov
    In Neural Information Processing Systems (NIPS 27), [pdf], Supplementary material [ zip].

    Learning Stochastic Feedforward Neural Networks
    Yichuan Tang and Ruslan Salakhutdinov
    In Neural Information Processing Systems (NIPS 27), [pdf], Supplementary material [ pdf].

    One-shot Learning by Inverting a Compositional Causal Process
    Brenden Lake, Ruslan Salakhutdinov, and Josh Tenenbaum
    In Neural Information Processing Systems (NIPS 27), [pdf], Supplementary material [ pdf].

    The Power of Asymmetry in Binary Hashing
    B. Neyshabur, N. Srebro, R. Salakhutdinov, Y. Makarychev, and P. Yadollahpour
    In Neural Information Processing Systems (NIPS 27), [pdf].

    Learning with Hierarchical-Deep Models
    Ruslan Salakhutdinov, Josh Tenenbaum, and Antonio Torralba
    To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2013, [pdf].

    Modeling Documents with Deep Boltzmann Machines
    Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey Hinton
    In Uncertainty in Artificial Intelligence (UAI), Seattle, USA, 2013, oral [pdf].

    Tensor Analyzers
    Yichuan Tang, Ruslan Salakhutdinov and Geoffrey Hinton
    In 30th International Conference on Machine Learning (ICML), Atlanta, USA, 2013 [pdf], [ supp ], [ code].

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

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

    A Better Way to Pretrain Deep Boltzmann Machines
    Ruslan Salakhutdinov and Geoffrey Hinton
    In Neural Information Processing Systems (NIPS 26) [ pdf].

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

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


[ | Information | Research | Teaching | Professional | ]

Ruslan Salakhutdinov, Department of Statistics and Computer Science, University of Toronto, http://www.cs.toronto.edu/~rsalakhu/