Bisk, Yonatan

HellaSwag: Can a Machine Really Finish Your Sentence?

Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 830

From Recognition to Cognition: Visual Commonsense Reasoning

Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 769

Defending Against Neural Fake News

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019) · arXiv

Misinformation and Its ImpactsAdversarial Robustness in Machine Learning

Cited by 569

ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., & Fox, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 540

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0

O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 210

KAT: A Knowledge Augmented Transformer for Vision-and-Language

Gui, L., Wang, B., Huang, Q., Hauptmann, A., Bisk, Y., & Gao, J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 130

TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment

Yang, J., Bisk, Y., & Gao, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 115

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, O'Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 111

OpenEQA: Embodied Question Answering in the Era of Foundation Models

Majumdar, A., Ajay, A., Zhang, X., Putta, P., Yenamandra, S., Henaff, M., Silwal, S., Mcvay, P., Maksymets, O., Arnaud, S., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 101

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

ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Shridhar, M., Yuan, X., Côté, M.-A., Bisk, Y., Trischler, A., & Hausknecht, M. (2020) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 80

HEAR: Holistic Evaluation of Audio Representations

Turian, J., Shier, J., Khan, H. R., Raj, B., Schuller, B. W., Steinmetz, C. J., Malloy, C., Tzanetakis, G., Velarde, G., McNally, K., et al. (2022) · arXiv

Music and Audio ProcessingSpeech Recognition and Synthesis

Cited by 56

PIQA: Reasoning about Physical Commonsense in Natural Language

Bisk, Y., Zellers, R., Le bras, R., Gao, J., & Choi, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingMultimodal Machine Learning Applications

Cited by 37

WebArena: A Realistic Web Environment for Building Autonomous Agents

Zhou, S., Xu, F. F., Zhu, H., Zhou, X., Lo, R., Sridhar, A., Cheng, X., Ou, T., Bisk, Y., Fried, D., et al. (2023) · arXiv

Software Testing and Debugging TechniquesWeb Data Mining and Analysis

Cited by 31

EXCALIBUR: Encouraging and Evaluating Embodied Exploration

Zhu, H., Kapoor, R., Min, S. Y., Han, W., Li, J., Geng, K., Neubig, G., Bisk, Y., Kembhavi, A., & Weihs, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 9

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

Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue

Min, S. Y., Zhu, H., Salakhutdinov, R., & Bisk, Y. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsSocial Robot Interaction and HRI

Cited by 5

VISREAS: Complex Visual Reasoning with Unanswerable Questions

Akter, S. N., Lee, S., Chang, Y., Bisk, Y., & Nyberg, E. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Multimodal Machine Learning ApplicationsLogic, Reasoning, and Knowledge

Cited by 2

Language models need inductive biases to count inductively

Chang, Y., & Bisk, Y. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0