Salakhutdinov, Ruslan

Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel

Tsai, Y.-H. H., Bai, S., Yamada, M., Morency, L.-P., & Salakhutdinov, R. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 173

Towards Understanding and Mitigating Social Biases in Language Models

Liang, P. P., Wu, C., Morency, L.-P., & Salakhutdinov, R. (2021) · arXiv

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 153

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

Eysenbach, B., Salakhutdinov, R., & Levine, S. (2019) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 123

Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis

Tsai, Y.-H. H., Ma, M., Yang, M., Salakhutdinov, R., & Morency, L.-P. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Multimodal Machine Learning ApplicationsExplainable Artificial Intelligence (XAI)

Cited by 98

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., & Manning, C. D. (2018) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 97

Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization

Liang, P. P., Liu, Z., Tsai, Y.-H. H., Zhao, Q., Salakhutdinov, R., & Morency, L.-P. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Tensor decomposition and applicationsMultimodal Machine Learning Applications

Cited by 95

MineRL: A Large-Scale Dataset of Minecraft Demonstrations

Guss, W. H., Houghton, B., Topin, N., Wang, P., Codel, C., Veloso, M., & Salakhutdinov, R. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 94

OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web

Kapoor, R., Butala, Y. P., Russak, M., Koh, J. Y., Kamble, K., Alshikh, W., & Salakhutdinov, R. (2024) · arXiv

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 93

Global Pose Estimation with an Attention-Based Recurrent Network

Parisotto, E., Chaplot, D. S., Zhang, J., & Salakhutdinov, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Robotics and Sensor-Based LocalizationReinforcement Learning in Robotics

Cited by 88

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning

Liang, P. P., Lyu, Y., Fan, X., Wu, Z., Cheng, Y., Wu, J., Chen, L., Wu, P., Lee, M. A., Zhu, Y., et al. (2021) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 85

Gated-Attention Architectures for Task-Oriented Language Grounding

Chaplot, D. S., Sathyendra, K. M., Pasumarthi, R. K., Rajagopal, D., & Salakhutdinov, R. (2017) · arXiv

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 82

FILM: Following Instructions in Language with Modular Methods

Min, S. Y., Chaplot, D. S., Ravikumar, P., Bisk, Y., & Salakhutdinov, R. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 81

The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

Guss, W. H., Codel, C., Hofmann, K., Houghton, B., Kuno, N., Milani, S., Mohanty, S., Liebana, D. P., Salakhutdinov, R., Topin, N., et al. (2019) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 46

Accelerating robotic reinforcement learning via parameterized action primitives

Dalal, M., Pathak, D., & Salakhutdinov, R. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 44

Grounding Language Models to Images for Multimodal Inputs and Outputs

Koh, J. Y., Salakhutdinov, R., & Fried, D. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 38

Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment

Liang, P. P., Wu, P., Ziyin, L., Morency, L.-P., & Salakhutdinov, R. (2021) · Proceedings of the 29th ACM International Conference on Multimedia

Domain Adaptation and Few-Shot LearningMultimodal Machine Learning Applications

Cited by 22

SEAL: Self-supervised embodied active learning using exploration and 3D consistency

Chaplot, D. S., Dalal, M., Gupta, S., Malik, J., & Salakhutdinov, R. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 22

C-Learning: Learning to Achieve Goals via Recursive Classification

Eysenbach, B., Salakhutdinov, R., & Levine, S. (2020) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 18

Robust Predictable Control

Eysenbach, B., Salakhutdinov, R., & Levine, S. (2021) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 15

Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

Sun, H., Verga, P., Dhingra, B., Salakhutdinov, R., & Cohen, W. W. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

OmniACT: a dataset and benchmark for enabling multimodal generalist autonomous agents for desktop and web

Kapoor, R., Butala, Y. P., Russak, M., Koh, J. Y., Kamble, K., AlShikh, W., & Salakhutdinov, R. (2025) · Lecture Notes in Computer Science

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 12

Learning Cognitive Models Using Neural Networks

Chaplot, D. S., MacLellan, C., Salakhutdinov, R., & Koedinger, K. (2018) · Lecture Notes in Computer Science

Intelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

Cited by 11

Imitating Task and Motion Planning with Visuomotor Transformers

Dalal, M., Mandlekar, A., Garrett, C., Handa, A., Salakhutdinov, R., & Fox, D. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 10

Plan, Eliminate, and Track - Language Models are Good Teachers for Embodied Agents

Wu, Y., Min, S. Y., Bisk, Y., Salakhutdinov, R., Azaria, A., Li, Y., Mitchell, T., & Prabhumoye, S. (2023) · arXiv

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 7