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.


Check out our new deep learning demos and code.

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

I am teaching:


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:

    Multimodal Learning with Deep Boltzmann Machines
    Nitish Srivastava and Ruslan Salakhutdinov
    To appear in Journal of Machine Learning Research, 2014.
    Code is available [ here].

    Dropout: A simple way to prevent neural networks from overfitting
    Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
    Journal of Machine Learning Research, 2014. [ pdf].

    A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
    Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov.
    In ICML Workshop on Knowledge-Powered Deep Learning for Text Mining, 2014. [ arXiv].

    Multi-task Neural Networks for QSAR Prediction
    George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov, 2014.
    [ arXiv].

    Restricted Boltzmann Machines for Neuroimaging: An Application in Identifying Intrinsic Networks
    Devon Hjelma, Vince Calhouna, Ruslan Salakhutdinov, Elena Allena, Tulay Adali, and Sergey Plisa
    In NeuroImage, Volume 96, Aug 1 2014, pages 245 - 260. [ pdf].

    Multimodal Neural Language Models
    Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel.
    In 31th International Conference on Machine Learning (ICML 2014)
    [pdf], [ Project Page].

    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].


[ | Information | Research | Teaching | Professional | ]

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