Ruslan Salakhutdinov

Associate Professor
Microsoft Faculty Fellow
Sloan Fellow
Carnegie Mellon University
rsalakhu[at]cs.cmu.edu
CV Google Scholar  

I am a UPMC professor of Computer Science in the Machine Learning Department, School of Computer Science at Carnegie Mellon University. I work in the field of statistical machine learning (See my CV.)

My research interests include Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization.

Prospective students: Please read this to ensure that I read your email.

Recent Research Highlights:




Recent Papers:

  • Style Transfer Through Back-Translation
    Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov and Alan Black
    ACL 2018

  • Neural Models for Reasoning over Multiple Mentions using Coreference
    Bhuwan Dhingra, Qiao Jin, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov
    NAACL, 2018 (Short Paper) arXiv [arXiv].

  • Structured Control Nets for Deep Reinforcement Learning
    Mario Srouji, Jian Zhang, Ruslan Salakhutdinov
    arXiv [arXiv], 2018.

  • Global Pose Estimation with an Attention-based Recurrent Network
    Emilio Parisotto, Devendra Singh Chaplot, Jian Zhang, Ruslan Salakhutdinov
    CVPR 2018 workshop on Deep Learning for Visual SLAM [arXiv], 2018.

  • On Characterizing the Capacity of Neural Networks using Algebraic Topology
    William H. Guss, Ruslan Salakhutdinov
    arXiv [arXiv], 2018.

  • Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
    Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W. Cohen
    arXiv [arXiv], [code], ICLR 2018, oral.

  • Active Neural Localization
    Devendra Singh Chaplot, Emilio Parisotto, Ruslan Salakhutdinov
    arXiv [arXiv], ICLR 2018.

  • Neural Map: Structured Memory for Deep Reinforcement Learning
    Emilio Parisotto, Ruslan Salakhutdinov
    arXiv [arXiv], ICLR 2018.

  • On Unifying Deep Generative Models
    Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing
    arXiv [arXiv], ICLR 2018.

  • A Generic Approach for Escaping Saddle Points
    Sashank J Reddi, Manzil Zaheer, Suvrit Sra, Barnabas Poczos, Francis Bach, Ruslan Salakhutdinov, Alexander J Smola
    arXiv [arXiv], AIStats 2018.

  • Gated-Attention Architectures for Task-Oriented Language Grounding
    Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, Ruslan Salakhutdinov
    arXiv [arXiv], AAAI 2018, oral.

  • Knowledge-based Word Sense Disambiguation using Topic Models
    Devendra Singh Chaplot, Ruslan Salakhutdinov
    [pdf], AAAI 2018, oral.

  • A Comparative Study of Word Embeddings for Reading Comprehension
    Bhuwan Dhingra, Hanxiao Liu, Ruslan Salakhutdinov, William W. Cohen
    arXiv [arXiv], 2017.

  • Good Semi-supervised Learning that Requires a Bad GAN
    Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, Ruslan Salakhutdinov
    NIPS 2017, [arXiv].

  • Deep Sets
    Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander Smola
    NIPS 2017, oral [arXiv].

  • Geometry of Optimization and Implicit Regularization in Deep Learning
    Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
    Survey paper, arXiv [arXiv].

  • Deep Conditional Determinantal Point Process for Large-Scale Multi-Label Classification
    P. Xie, R. Salakhutdinov, L. Mou and E.P. Xing
    ICCV 2017.

  • Learning Robust Visual-Semantic Embeddings
    Yao-Hung Hubert Tsai, Liang-Kang Huang, Ruslan Salakhutdinov
    ICCV 2017, arXiv [arXiv].

  • Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
    Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, Taylor Berg-Kirkpatrick
    ICML 2017, arXiv [arXiv].

  • Controllable Text Generation
    Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing
    ICML 2017, arXiv [arXiv].

  • Semi-Supervised QA with Generative Domain-Adaptive Nets
    Zhilin Yang, Junjie Hu, Ruslan Salakhutdinov, William W. Cohen
    ACL 2017, arXiv].

  • Gated-Attention Readers for Text Comprehension
    Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov
    ACL 2017, [arXiv], [Code].

  • The More You Know: Using Knowledge Graphs for Image Classification
    Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta
    CVPR 2017, [arXiv].

  • Spatially Adaptive Computation Time for Residual Networks
    Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov
    CVPR 2017, [arXiv], [Code].

  • Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
    Zhilin Yang, Ruslan Salakhutdinov, William W. Cohen
    ICLR 2017, [arXiv].

  • On the Quantitative Analysis of Decoder-Based Generative Models
    Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger Grosse
    ICLR 2017, [arXiv], [Code].

  • Words or Characters? Fine-grained Gating for Reading Comprehension
    Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov
    ICLR 2017, [arXiv].

  • Transfer Deep Reinforcement Learning in 3D Environments: An Empirical Study
    Devendra Singh Chaplot, Guillaume Lample, Kanthashree Mysore Sathyendra, Ruslan Salakhutdinov
    Deep Reinforcement Learning Workshop, NIPS 2016
    [pdf].

  • Deep Neural Networks with Massive Learned Knowledge
    Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, and Eric Xing
    Conference on Empirical Methods in Natural Language Processing (EMNLP'16).
    [pdf], [supp].

  • Iterative Refinement of Approximate Posterior for Training Directed Belief Networks
    Devon Hjelm, Kyunghyun Cho, Junyoung Chung, Ruslan Salakhutdinov, Vince Calhoun, Nebojsa Jojic
    NIPS 2016, [arXiv].

  • Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
    Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nathan Srebro
    NIPS 2016, [arXiv].

  • Stochastic Variational Deep Kernel Learning
    Andrew Gordon Wilson, Zhiting Hu, Eric Xing, Ruslan Salakhutdinov
    NIPS 2016, [arXiv], [Code].

  • On Multiplicative Integration with Recurrent Neural Networks
    Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov
    NIPS 2016, [arXiv].

  • Encode, Review, and Decode: Reviewer Module for Caption Generation
    Zhilin Yang, Ye Yuan, Yuexin Wu, Ruslan Salakhutdinov, William W. Cohen
    NIPS 2016, [arXiv], [Code].

  • Architectural Complexity Measures of Recurrent Neural Networks
    Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio
    NIPS 2016, [arXiv].

  • Multi-Task Cross-Lingual Sequence Tagging from Scratch
    Zhilin Yang, Ruslan Salakhutdinov, William Cohen
    [arXiv].

  • Revisiting Semi-Supervised Learning with Graph Embeddings
    Zhilin Yang, William Cohen, Ruslan Salakhutdinov
    ICML 2016, [arXiv], [Code].

  • 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].