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For a more complete list of publications, please see arXiv


2023
  • Multimodal Graph Learning for Generative Tasks,
    Minji Yoon, Jing Yu Koh, Bryan Hooi, Ruslan Salakhutdinov
    [arXiv] [Code]

  • Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks,
    Murtaza Dalal, Tarun Chiruvolu, Devendra Chaplot, Ruslan Salakhutdinov,
    [arXiv] [Codev]

  • Effective Data Augmentation With Diffusion Models
    Brandon Trabucco, Kyle Doherty, Max Gurinas, Ruslan Salakhutdinov
    ICLR 2023 Foundation Models Workshop [arXiv] [code]

  • Generating Images with Multimodal Language Models
    Jing Yu Koh, Daniel Fried, Ruslan Salakhutdinov
    NeurIPS 2023, [arXiv] [code]

  • Factorized Contrastive Learning: Going Beyond Multi-view Redundancy
    Paul Pu Liang*, Zihao Deng*, Martin Ma*, James Zou, Louis-Philippe Morency, Ruslan Salakhutdinov
    NeurIPS 2023, [arXiv] [code]

  • Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications
    Paul Pu Liang, Chun Kai Ling, Yun Cheng, Alex Obolenskiy, Yudong Liu, Rohan Pandey, Alex Wilf, Louis-Philippe Morency, Ruslan Salakhutdinov
    [arXiv] [code]

  • Quantifying & Modeling Feature Interactions: An Information Decomposition Framework
    Paul Pu Liang, Yun Cheng, Xiang Fan, Chun Kai Ling, Suzanne Nie, Richard Chen, Zihao Deng, Nicholas Allen, Randy Auerbach, Faisal Mahmood, Ruslan Salakhutdinov, Louis-Philippe Morency
    NeurIPS 2023 [arXiv] [code]

  • SPRING: Studying Papers and Reasoning to play Games
    Yue Wu, So Yeon Min, Shrimai Prabhumoye, Yonatan Bisk, Ruslan Salakhutdinov, Amos Azaria, Tom Mitchell, Yuanzhi Li
    NeurIPS 2023 [arXiv]

  • Imitating Task and Motion Planning with Visuomotor Transformers
    Murtaza Dalal, Ajay Mandlekar, Caelan Garrett, Ankur Handa, Ruslan Salakhutdinov, Dieter Fox
    CoRL 2023 [arXiv] [code]

  • Graph Generative Model for Benchmarking Graph Neural Networks
    Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov
    ICML 2023 [arXiv]

  • A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
    Benjamin Eysenbach, Matthieu Geist, Sergey Levine, and Ruslan Salakhutdinov
    ICML 2023 [arXiv]

  • Grounding Language Models to Images for Multimodal Inputs and Outputs
    Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried
    ICML 2023 [arXiv] [code]

  • Multimodal Fusion Interactions: A Study of Human and Automatic Quantification
    Paul Pu Liang, Yun Cheng, Ruslan Salakhutdinov, Louis-Philippe Morency
    ICMI 2023 [arXiv] [code]

  • Contrastive Example-Based Control
    Kyle Beltran Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, and Chelsea Finn
    In Learning for Dynamics and Control Conference 2023 [pdf]

  • Cross-modal Attention Congruence Regularization for Vision-Language Relation Alignment
    Rohan Pandey, Rulin Shao, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency
    ACL 2023 [arXiv] [code]

  • Reasoning over Logically Interacted Conditions for Question Answering
    Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
    ICLR 2023 [arXiv]

  • A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search
    Brandon Trabucco, Gunnar Sigurdsson, Robinson Piramuthu, Gaurav S. Sukhatme, Ruslan Salakhutdinov
    ICLR 2023 [arXiv]

  • Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective
    Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov
    ICLR 2023 [arXiv]

  • MultiViz: An Analysis Benchmark for Visualizing and Understanding Multimodal Models
    Paul Pu Liang, Yiwei Lyu, Gunjan Chhablani, Nihal Jain, Zihao Deng, Xingbo Wang, Louis-Philippe Morency, Ruslan Salakhutdinov
    ICLR 2023 [arXiv]


2022
  • MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning
    Paul Pu Liang, Yiwei Lyu, Xiang Fan, Jeffrey Tsaw, Yudong Liu, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, Ruslan Salakhutdinov
    JMLR Open Source Software 2022 [website] [code]

  • HighMMT: Quantifying Modality and Interaction Heterogeneity for High-Modality Representation Learning
    Paul Pu Liang, Yiwei Lyu, Xiang Fan, Jeffrey Tsaw, Yudong Liu, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, Ruslan Salakhutdinov
    TMLR [arXiv] [code]

  • Contrastive Learning as Goal-Conditioned Reinforcement Learning
    Benjamin Eysenbach*, Tianjun Zhang*, Sergey Levine, Ruslan Salakhutdinov
    NeurIPS 2022 [arXiv] [Website]

  • Mismatched No More: Joint Model-Policy Optimization for Model-Based RL.
    Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov
    NeurIPS 2022 [arXiv]

  • Imitating Past Successes can be Very Suboptimal.
    Benjamin Eysenbach, Soumith Udatha, Sergey Levine, Ruslan Salakhutdinov
    NeurIPS 2022 [arXiv]

  • Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks
    Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi
    NeurIPS 2022 [arXiv]

  • Paraphrasing Is All You Need for Novel Object Captioning
    Cheng-Fu Yang, Yao-Hung Hubert Tsai, Wan-Cyuan Fan, Ruslan Salakhutdinov, Louis-Philippe Morency, Yu-Chiang, Frank Wang
    NeurIPS 2022 [arXiv]

  • Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs
    Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov
    ICML 2022 [arXiv]

  • PACS: A Dataset for Physical Audiovisual CommonSense Reasoning
    Samuel Yu, Peter Wu, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency
    ECCV 2022 [arXiv]

  • Conditional QA: A complex reading comprehension dataset with conditional answers
    H. Sun, W. Cohen, R. Salakhutdinov
    ACL 2022 [arXiv]

  • FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
    Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Jian Li, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang
    ACL 2022 [arXiv]

  • Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue
    So Yeon Min, Hao Zhu, Ruslan Salakhutdinov, Yonatan Bisk
    EMNLP 2022 [arXiv]

  • Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
    Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency
    EMNLP 2022, findings [arXiv]

  • Conditional Contrastive Learning with Kernels
    Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov
    ICRL 2022 [arXiv]

  • Learning Weakly-supervised Contrastive Representations
    Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu, Peiyuan Liao, Ruslan Salakhutdinov, Louis-Philippe Morency
    ICRL 2022 [arXiv]

  • FILM: Following Instructions in Language with Modular Methods
    So Yeon Min, Devendra Singh Chaplot, Pradeep Kumar Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov
    ICRL 2022 [arXiv]

  • C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
    Tianjun Zhang, Benjamin Eysen- bach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez
    ICRL 2022 [arXiv]

  • The Information Geometry of Unsupervised Reinforcement Learning
    Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
    ICRL 2022, oral, [arXiv]


2021
  • SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency
    Devendra Singh Chaplot, Murtaza Dalal, Saurabh Gupta, Jitendra Malik, Ruslans Salakhutdinov, NeurIPS 2021
    NeurIPS 2021 [arXiv]

  • Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification.
    Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov
    NeurIPS 2021, oral, [arXiv]

  • Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives
    Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov
    NeurIPS 2021, [arXiv]

  • Robust Predictable Control
    Benjamin Eysenbach, Ruslabn Salakhutdinov, Sergey Levine
    NeurIPS 2021 [arXiv]

  • MultiBench: Multiscale Benchmarks for Multimodal Representation Learning
    Paul Pu Liang, Yiwei Lyu, Xiang Fan, Zetian Wu, Yun Cheng, Jason Wu, Leslie (Yufan) Chen, Peter Wu, Michelle A. Lee, Yuke Zhu, Ruslan Salakhutdinov, Louis-Philippe Morency
    NeurIPS 2021, Datasets and Benchmarks Proceedings. [arXiv]

  • End-to-End Multihop Retrieval for Compositional Question Answering over Long Documents
    Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
    [arXiv]

  • Towards Understanding and Mitigating Social Biases in Language Models
    Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, Ruslan Salakhutdinov
    ICML 2021 [arXiv]

  • Information Obfuscation of Graph Neural Networks
    P. Liao, H. Zhao, K. Xu, T. Jaakkola, G. Gordon, S. Jegelka and R. Salakhutdinov
    ICML 2021 [arXiv]

  • Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
    Haitian Sun, Patrick Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William Cohen
    ICML 2021 [arXiv]

  • Instabilities of Offline RL with Pre-Trained Neural Representation
    Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, Sham M. Kakade
    ICML 2021 [arXiv]

  • Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
    Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M Susskind, Jian Zhang, Ruslan Salakhutdinov, Hanlin Goh
    ICML 2021 [arXiv]

  • On Proximal Policy Optimization's Heavy-tailed Gradients.
    S. Garg, J. Zhanson, E. Parisotto, A. Prasad, Z. Kolter, S. Balakrishnan, Z. Lipton, R. Salakhutdinov, P. Ravikumar.
    ICML 2021 [arXiv]

  • Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data
    Paul Pu Liang, Terrance Liu, Anna Cai, Michal Muszynski, Ryo Ishii, Nick Allen, Randy Auerbach, David Brent, Ruslan Salakhutdinov, Louis-Philippe Morency
    ACL 2021 [arXiv]

  • Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers.
    Benjamin Eysenbach*, Swapnil Asawa*, Shreyas Chaudhari*, Sergey Levine, Ruslan Salakhutdinov
    International Conference on Learning Representations (ICLR) 2021. [arXiv]

  • Self-supervised Representation Learning with Relative Predictive Coding
    Yao-Hung Hubert Tsai, Martin Q. Ma, Muqiao Yang, Han Zhao, Louis-Philippe Morency, Ruslan Salakhutdinov.
    International Conference on Learning Representations (ICLR) 2021. [arXiv]

  • Self-supervised Learning from a Multi-view Perspective
    Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, Louis-Philippe Morency.
    International Conference on Learning Representations (ICLR) 2021. [arXiv]

  • C-Learning: Learning to Achieve Goals via Recursive Classification.
    Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
    International Conference on Learning Representations (ICLR) 2021. [arXiv]

  • HUBERT: How much can a bad teacher benefit ASR pre-training?
    Wei-Ning Hsu, Yao-Hung Hubert Tsai, Benjamin Bolte, Ruslan Salakhutdinov, Abdelrahman Mohamed.
    International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021. [arXiv]

  • StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer
    Wei-Ning Hsu, Yao-Hung Hubert Tsai, Benjamin Bolte, Ruslan Salakhutdinov, Abdelrahman Mohamed.
    NAACL 2021 [arXiv]


2020
  • Planning with Submodular Objective Functions
    Ruosong Wang, Hanrui Zhang, Devendra Singh Chaplot, Denis Garagić, Ruslan Salakhutdinov
    arXiv [arXiv]

  • Exploring Controllable Text Generation Techniques
    Shrimai Prabhumoye, Alan W Black, Ruslan Salakhutdinov.
    28th International Conference on Computational Linguistics (COLING) 2020, [arXiv]

  • Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
    Benjamin Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov
    NeurIPS 2020, oral, [arXiv]

  • Object Goal Navigation using Goal-oriented Semantic Exploration
    Devendra Singh Chaplot, Dhiraj Gandhi, Abhinav Gupta, Ruslan Salakhutdinov.
    NeurIPS 2020, [arXiv]

  • Neural Methods for Point-wise Dependency Estimation
    Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov.
    NeurIPS 2020, [arXiv]

  • On Reward-Free Reinforcement Learning with Linear Function Approximation
    Ruosong Wang, Simon S. Du, Lin F. Yang, Ruslan Salakhutdinov
    NeurIPS 2020, [arXiv]

  • Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension
    Ruosong Wang, Ruslan Salakhutdinov, Lin F. Yang
    NeurIPS 2020, [arXiv]

  • Planning with General Objective Functions: Going Beyond Total Rewards
    Ruosong Wang, Peilin Zhong, Simon S. Du, Ruslan Salakhutdinov, Lin F. Yang
    NeurIPS 2020, [arXiv]

  • A Closer Look at Robustness vs. Accuracy
    Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov and Kamalika Chaudhuri
    NeurIPS 2020, [arXiv]

  • Weakly-Supervised Reinforcement Learning for Controllable Behavior
    Lisa Lee, Benjamin Eysenbach, Ruslan Salakhutdinov, Shane Gu, Chelsea Finn
    NeurIPS 2020, [arXiv]

  • Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis
    Yao-Hung Hubert Tsai*, Martin Q. Ma*, Muqiao Yang*, Ruslan Salakhutdinov, Louis-Philippe Morency.
    Empirical Methods in Natural Language Processing (EMNLP) 2020, [arXiv]

  • Neural Topological SLAM for Visual Navigation
    Devendra Singh Chaplot, Ruslan Salakhutdinov, Abhinav Gupta, Saurabh Gupta.
    CVPR 2020, [arXiv]

  • Topological Sort for Sentence Ordering
    Shrimai Prabhumoye, Ruslan Salakhutdinov, Alan W Black.
    ACL 2020, [arXiv]

  • Politeness Transfer: A Tag and Generate Approach
    Aman Madaan*, Amrith Setlur*, Tanmay Parekh*, Barnabas Poczos, Graham Neubig,Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye.
    ACL 2020, [arXiv]

  • Towards Debiasing Sentence Representations
    Paul Pu Liang, Irene Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, Louis-Philippe Morency
    ACL 2020, [arXiv]

  • Capsules with Inverted Dot-Product Attention Routing
    Yao-Hung Hubert Tsai, Nitish Srivastava, Hanlin Goh, Ruslan Salakhutdinov
    ICLR 2020, [arXiv] [Code]

  • Learning To Explore Using Active Neural Mapping
    Devendra Singh Chaplot, Saurabh Gupta, Dhiraj Gandhi, Abhinav Gupta, Ruslan Salakhutdinov
    ICLR 2020, [arXiv]

  • Differentiable Reasoning over a Virtual Knowledge Base
    Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen
    ICLR 2020, [arXiv]

  • Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
    Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu
    ICLR 2020, [arXiv]

  • On Emergent Communication in Competitive Multi-Agent Teams
    Paul Pu Liang, Jeffrey Chen, Ruslan Salakhutdinov, Louis-Philippe Morency, Satwik Kottur
    AAMAS 2020.

  • Embodied Multimodal Multitask Learning
    Devendra Singh Chaplot, Lisa Lee, Ruslan Salakhutdinov, Devi Parikh, Dhruv Batra
    IJCAI 2020 [arXiv]

  • Complex Transformer: A Framework for Modeling Complex-Valued Sequence
    Muqiao Yang*, Martin Q. Ma*, Dongyu Li*, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov.
    International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 [arXiv], [Code]



2019
  • Devendra Singh Chaplot, Saurabh Gupta, Abhinav Gupta, Ruslan Salakhutdinov
    Modular Visual Navigation using Active Neural Mapping
    [pdf] [Videos], Winner of the Habitat Navigation Challenge at CVPR 2019.

  • Concurrent Episodic Meta Reinforcement Learning
    Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi, Varsha Chinnaobireddy, Yuhuai Wu, Ruslan Salakhutdinov
    [arXiv]

  • Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, Ruslan Salakhutdinov
    Efficient Exploration via State Marginal Matching
    [arXiv] [Code]

  • Yao-Hung Hubert Tsai, Shaojie Bai, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov
    Transformer Dissection: A Unified Understanding of Transformer Attention via the Lens of Kernel
    EMNLP 2019, [arXiv]

  • Worst cases policy gradients
    Charlie Tang, Jian Zhang, Ruslan Salakhutdinov
    CoRL 2019

  • Multiple Futures Prediction
    Charlie Tang, Ruslan Salakhutdinov
    NeurIPS 2019, [arXiv]

  • Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
    Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
    NeurIPS 2019, [arXiv] [Code]

  • Learning Data Manipulation for Augmentation and Weighting
    Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom Mitchell, Eric P. Xing
    NeurIPS 2019

  • Mixtape: Breaking the Softmax Bottleneck Efficiently
    Zhilin Yang, Thang Luong, Ruslan Salakhutdinov, Quoc V Le
    NeurIPS 2019

  • Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
    XLNet: Generalized Autoregressive Pretraining for Language Understanding
    NeurIPS 2019 [arXiv] [Code], oral,

  • Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
    Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu
    NeurIPS 2019, [arXiv]

  • On Exact Computation with an Infinitely Wide Neural Net
    Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang
    NeurIPS 2019, [arXiv]

  • Learning Neural Networks with Adaptive Regularization
    Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon
    NeurIPS 2019 [arXiv]

  • Deep Gamblers: Learning to Abstain with Portfolio Theory
    Liu Ziyin, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
    NeurIPS 2019 [arXiv]

  • MineRL: A Large-Scale Dataset of Minecraft Demonstrations
    William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, Ruslan Salakhutdinov
    IJCAI 2019 [arXiv], [Web]

  • My Way of Telling a Story": Persona based Grounded Story Generation
    Shrimai Prabhumoye, Khyathi Chandu, Ruslan Salakhutdinov, Alan W Black.
    Storytelling Workshop at ACL 2019 [arXiv],

  • Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
    Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov
    ACL 2019 [arXiv], [Code]

  • Multimodal Transformer for Unaligned Multimodal Language Sequences
    Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, Zico Kolter, Louis-Philippe Morency, Ruslan Salakhutdinov
    ACL 2019 [arXiv], [Code]

  • Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
    Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai, Qibin Zhao, Ruslan Salakhutdinov, Louis-Philippe Morency
    ACL 2019 [arXiv]

  • The Omniglot Challenge: A 3-Year Progress Report
    Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
    Current Opinion in Behavioral Sciences, 2019 [arXiv]

  • Video Relationship Reasoning using Gated Spatio-Temporal Energy Graph
    Yao-Hung Hubert Tsai, Santosh Kumar Divvala, Louis-Philippe Morency, Ruslan Salakhutdinov, Ali Farhadi
    CVPR 2019, [arXiv]

  • A Strong and Simple Baseline for Multimodal Utterance Embeddings
    Paul Pu Liang, Yao Chong Lim, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Louis-Philippe Morency
    NAACL 2019, [arXiv] [Code]

  • Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex
    Hongyang Zhang, Junru Shao, Ruslan Salakhutdinov
    AIStats 2019, [arXiv]

  • Learning Factorized Multimodal Representations
    Yao Hung Tsai, Paul Pu Liang, Amir Ali Bagherzade, Louis-Philippe Morency, Ruslan Salakhutdinov
    ICLR 2019, [arXiv] [Code]

  • AutoLoss: Learning Discrete Schedule for Alternate Optimization
    Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing
    ICLR 2019, [arXiv]

  • Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
    Makoto Yamada, Yi Wu, Yao Hung Tsai, Hirofumi Ohtai, Ruslan Salakhutdinov, Ichiro Takeeuchi, Kenji Fukumizu
    ICLR 2019, [arXiv]

  • Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
    Devendra Singh Sachan, Manzil Zaheer, Ruslan Salakhutdinov
    AAAI 2019, [pdf]



    2018

  • Connecting the Dots Between MLE and RL for Sequence Prediction
    Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric Xing
    [arXiv]

  • GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
    Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaiming He, William Cohen, Ruslan Salakhutdinov, Yann LeCun
    NeurIPS 2018, [arXiv]

  • Deep Generative Models with Learnable Knowledge Constraints
    Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Xiaodan Liang, Lianhui Qin, Haoye Dong, Eric Xing
    NeurIPS 2018, [arXiv]

  • How Many Samples are Needed to Learn a Convolutional Neural Network?
    Simon Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnani, Ruslan Salakhutdinov, Aarti Singh
    NeurIPS 2018, [arXiv]

  • HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
    Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, Christopher Manning
    EMNLP, 2018, [arXiv], [Code, Data]

  • Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
    Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, William Cohen
    EMNLP, 2018, [arXiv], [Code]

  • Investigating the Working of Text Classifiers
    Devendra Singh Sachan, Manzil Zaheer, Ruslan Salakhutdinov
    COLING 2018, [arXiv]

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

  • Gated Path Planning Networks
    Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov
    ICML 2018, [arXiv], [Code].

  • Transformation Autoregressive Networks
    Junier B. Oliva, Avinava Dubey, Manzil Zaheer, BarnabásPoczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider
    ICML 2018 [arXiv].

  • Learning Cognitive Models using Neural Networks, (oral)
    Devendra Singh Chaplot, Christopher MacLellan, Ruslan Salakhutdinov, Kenneth Koedinger.
    [arXiv],, 19th International Conference on Artificial Intelligence in Education (AIED-18), London, UK

  • 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, best paper award .

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

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

  • 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].

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

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

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

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

  • Selecting the Best in GANs Family: a Post Selection Inference Framework
    Yao-Hung Hubert Tsai, Denny Wu, Makoto Yamada, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu
    [arXiv], ICLR workshop 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], AIStats 2018.

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

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



    2017

  • Question Answering from Unstructured Text by Retrieval and Comprehension
    Yusuke Watanabe, Bhuwan Dhingra, Ruslan Salakhutdinov
    [arXiv], 2017.

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

  • Linguistic Knowledge as Memory for Recurrent Neural Networks
    Bhuwan Dhingra, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov
    [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].

  • Discovering Order in Unordered Datasets: Generative Markov Networks
    Yao-Hung Hubert Tsai, Han Zhao, Ruslan Salakhutdinov, Nebojsa Jojic
    NIPS Time Series Workshop (NIPS TSW) 2017 [arXiv], oral .

  • Improving One-Shot Learning through Fusing Side Information
    Yao-Hung Hubert Tsai, Ruslan Salakhutdinov
    NIPS Learning with Limited Labeled Data: Weak Supervision and Beyond (NIPS LLD) 2017
    Bay Area Machine Learning Symposium (BayLearn) 2017 (best poster) [arXiv].

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

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

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

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



    2016

  • 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, [arXiv], oral. [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].



    2015

  • 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].


  • Learning Wake-Sleep Recurrent Attention Models
    Lei Jimmy Ba, Roger Grosse, Ruslan Salakhutdinov, Brendan Frey
    NIPS 2015. [arXiv].

  • Skip-Thought Vectors
    Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler
    NIPS 2015, [arXiv]. Code is available [here].

  • 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], [ project page ], oral.

  • 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
    Ruslan Salakhutdinov
    Annual Review of Statistics and Its Application, Vol. 2, pp. 361–385, 2015
    [pdf], 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], [pdf].

  • 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], [pdf], [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
    In 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 ], [pdf], [ Project Page].

  • 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
    In AI and Statistics (AISTATS), 2015 [arXiv], [pdf].



    2014

  • 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].
    Previous version appeared in ICML Workshop on Knowledge-Powered Deep Learning for Text Mining, 2014. [ arXiv].

  • Multimodal Learning with Deep Boltzmann Machines
    Nitish Srivastava and Ruslan Salakhutdinov
    Journal of Machine Learning Research, 2014. [ pdf ]. 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].

  • Deep Learning for Neuroimaging: a Validation Study
    S. Plis, D. Hjelm, R. Salakhutdinov, E. Allen, H. Bockholt, J. Long, H. Johnson, J. Paulsen, J. Turner, and V. Calhoun
    Frontiers in Neuroscience, 2014. [ pdf].

  • 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].



    2013

  • Annealing between Distributions by Averaging Moments
    Roger Grosse, Chris Maddison, and Ruslan Salakhutdinov
    In Neural Information Processing Systems (NIPS 27), 2013, oral.
    [pdf], Supplementary material [ pdf].

  • Discriminative Transfer Learning with Tree-based Priors
    Nitish Srivastava and Ruslan Salakhutdinov
    In Neural Information Processing Systems (NIPS 27), 2013, [pdf], Supplementary material [ zip].

  • Learning Stochastic Feedforward Neural Networks
    Yichuan Tang and Ruslan Salakhutdinov
    In Neural Information Processing Systems (NIPS 27), 2013 [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), 2013, [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), 2013, [pdf].

  • Learning with Hierarchical-Deep Models
    Ruslan Salakhutdinov, Josh Tenenbaum, and Antonio Torralba
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1958-1971, Aug. 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
    Neural Information Processing Systems (NIPS 26), 2012, oral.
    [ pdf], Supplementary material [ zip].
    Code is available [ here].

  • Hamming Distance Metric Learning
    Mohammad Norouzi, David Fleet, and Ruslan Salakhutdinov
    Neural Information Processing Systems (NIPS 26), 2012 [ pdf], Supplementary material [ pdf].

  • A Better Way to Pretrain Deep Boltzmann Machines
    Ruslan Salakhutdinov and Geoffrey Hinton
    Neural Information Processing Systems (NIPS 26), 2012, [ pdf].

  • Matrix Reconstruction with the Local Max Norm.
    Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov
    Neural Information Processing Systems (NIPS 26), 2012, [ 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), 2012, [ 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
    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), 2011, [ pdf]

  • Transfer Learning by Borrowing Examples
    Joseph Lim , Ruslan Salakhutdinov Antonio Torralba
    Neural Information Processing Systems (NIPS 25). 2011, [ 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), 2011, [ 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, oral.

  • 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

      1. Notes on the KL-divergence between a Markov chain and its equilibrium distribution
        Iain Murray and Ruslan Salakhutdinov (2008)
        [pdf]

      2. 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)]

      3. Expectation Conjugate-Gradient: An Alternative to EM
        Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
        [draft version (june.02)-->ps.gz(186K) pdf(640K)]