Ruslan Salakhutdinov

Google Scholar

For a more complete list of publications, please see arXiv

2024

VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks
Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, Daniel Fried
ACL 2014 [arXiv] [Code]
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks,
Murtaza Dalal, Tarun Chiruvolu, Devendra Chaplot, Ruslan Salakhutdinov
ICLR 2024 [arXiv] [Code]
Effective Data Augmentation With Diffusion Models
Brandon Trabucco, Kyle Doherty, Max Gurinas, Ruslan Salakhutdinov
ICLR 2024 [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
ICLR 2024 [arXiv] [code]
Contrastive Difference Predictive Coding
Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach
ICLR 2024 [arXiv] [code]
Manifold Preserving Guided Diffusion
Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim♨, Wei-Hsiang Liao, Yuki Mitsufuji, Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon
ICLR 2024 [arXiv] [code]
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
Chongyi Zheng, Benjamin Eysenbach, Homer Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine
ICLR 2024 [arXiv] [code]
Confronting Reward Model Overoptimization with Constrained RLHF
Ted Moskovitz, Aaditya K. Singh, DJ Strouse, Tuomas Sandholm, Ruslan Salakhutdinov, Anca D. Dragan, Stephen McAleer
ICLR 2024 [arXiv]

2023

Multimodal Graph Learning for Generative Tasks,
Minji Yoon, Jing Yu Koh, Bryan Hooi, Ruslan Salakhutdinov
[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]
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
ICLR 2022 [arXiv]
Learning Weakly-supervised Contrastive Representations
Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu, Peiyuan Liao, Ruslan Salakhutdinov, Louis-Philippe Morency
ICLR 2022 [arXiv]
FILM: Following Instructions in Language with Modular Methods
So Yeon Min, Devendra Singh Chaplot, Pradeep Kumar Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov
ICLR 2022 [arXiv]
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
Tianjun Zhang, Benjamin Eysen- bach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez
ICLR 2022 [arXiv]
The Information Geometry of Unsupervised Reinforcement Learning
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
ICLR 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].
- See also Bloomberg news, Motherboard.
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].
- 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].
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].
- See also Scientific American, CIFAR.
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

Publications

Google Scholar

For a more complete list of publications, please see arXiv

Technical Reports/Unpublished Manuscripts