Canada Research Chair in Statistical Machine Learning
In the Spring of 2016, I will be moving to the Machine Learning Department at Carnegie Mellon University. I am looking for strong PhD students, please apply to CMU if you are interested in working with me.I am an assistant professor of Computer Science and Statistics at the University of Toronto. I work in the field of statistical machine learning (See my CV.) I received my PhD in computer science from the University of Toronto in 2009. After spending two post-doctoral years at MIT, I joined the University of Toronto in 2011.
My research interests include
Probabilistic Graphical Models, and
Prospective students: Please
read this to ensure that I read your email.
Recent Research Highlights:
- See our recent Deep Learning Tutorial in Montreal:
Part 1:[Slides (pdf)], [Video]
Part 2:[Slides (pdf)], [Video]
- See our recent Deep Learning Tutorial at KDD 2014: [Video], [ Slides].
- Check out our new website with demos and software.
- I was helping to run Thematic Program on Statistical Inference, Learning, and Big Data at the Fields Institute.
- I was teaching an advanced Machine Learning course at the Fields Institute. Videos of my lectures will be available online. Also, check out Live Streaming of my course.
Multi-Task Cross-Lingual Sequence Tagging from Scratch
Zhilin Yang, Ruslan Salakhutdinov, William Cohen
Architectural Complexity Measures of Recurrent Neural Networks
Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio
Revisiting Semi-Supervised Learning with Graph Embeddings
Zhilin Yang, William Cohen, Ruslan Salakhutdinov
ICML 2016, arXiv [arXiv].
Importance Weighted Autoencoders
Yuri Burda, Roger Grosse, Ruslan Salakhutdinov
ICLR, 2016, [arXiv]. Code is available [here].
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov
ICLR, 2016, [arXiv].
Generating Images from Captions with Attention
Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov
ICLR, 2016, oral [arXiv]. [Generated Samples].
Data-Dependent Path Normalization in Neural Networks
Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
ICLR, 2016, [arXiv].
Action Recognition using Visual Attention
Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov
ICLR workshop, 2016 [arXiv]. [Code]. [Project Website].
Deep Kernel Learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric Xing
AI and Statistics, 2016, [arXiv].
Human-level concept learning through probabilistic program induction
Brenden Lake, Ruslan Salakhutdinov, and Joshua Tenenbaum (2015),
Science, 350(6266), 1332-1338, [paper], [Supporting Info.], [visual Turing tests], [Omniglot data set], [Code].
- See also New York Times, CBC, Reuters, CBS, MIT Tech Review, Toronto Star, UofT News, MIT News, Washington Post, CIFAR, Business Insider.
Learning Wake-Sleep Recurrent Attention Models
Lei Jimmy Ba, Roger Grosse, Ruslan Salakhutdinov, Brendan Frey
NIPS 2015. [arXiv].
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler
NIPS 2015, [arXiv].
Path-SGD: Path-Normalized Optimization in Deep Neural Networks
Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro
NIPS 2015, [arXiv].
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
ICCV 2015, [arXiv], oral , [ project page ]
Predicting Deep Zero-Shot Convolutional Neural Networks
using Textual Descriptions
Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov
ICCV 2015, [arXiv].
Learning Deep Generative Models
Annual Review of Statistics and Its Application, Vol. 2, pp. 361–385, 2015
Scaling Up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix
Roger Grosse, Ruslan Salakhutdinov
ICML, 2015. [pdf].
Unsupervised Learning of Video Representations using LSTMs
Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
ICML, 2015, [arXiv], [Code]
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio
ICML, 2015, [arXiv], [project page],
Siamese neural networks for one-shot image recognition.
Gregory Koch, Richard Zemel, Ruslan Salakhutdinov
ICML 2015 Deep Learning Workshop (2015), [pdf].
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
Shengxin Zha, Florian Luisier, Walter Andrews, Nitish Srivastava, Ruslan Salakhutdinov
BMVC 2015, [arXiv], 2015
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
Y. Zhu, R. Urtasun, R. Salakhutdinov and S.Fidler
In Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015,
[ arXiv ]
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel.
To appear in Transactions of the Association for Computational Linguistics (TACL), 2015.
[ arXiv], [ results], [ demo ].
An encoder-decoder architecture for ranking and generating image descriptions.
Previous version appeared in NIPS Deep Learning Workshop, 2014.
Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing
Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov,
AI and Statistics, 2015 [arXiv]
Learning Generative Models with Visual Attention
Yichuan Tang, Nitish Srivastava, and Ruslan Salakhutdinov
Neural Information Processing Systems (NIPS 28), 2014, oral,
[ pdf ], Supplementary material [ pdf].
- A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov.
Neural Information Processing Systems (NIPS 28), 2014.
[ pdf ], Supplementary material [ zip].
Multimodal Learning with Deep Boltzmann Machines
Nitish Srivastava and Ruslan Salakhutdinov
Journal of Machine Learning Research, 2014. [ pdf ]. Code is available [ here].