Mathew Monfort

According to our database, Mathew Monfort authored at least 15 papers between 2015 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding.

Mathew Monfort, Bowen Pan, Kandan Ramakrishnan, Alex Andonian, Barry A. McNamara, Alex Lascelles, Quanfu Fan, Dan Gutfreund, Rogério Schmidt Feris, Aude Oliva

IEEE Trans. Pattern Anal. Mach. Intell., 2022

2021

Spoken Moments: Learning Joint Audio-Visual Representations From Video Descriptions.

Mathew Monfort, SouYoung Jin, Alexander H. Liu, David Harwath, Rogério Feris, James R. Glass, Aude Oliva

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Moments in Time Dataset: One Million Videos for Event Understanding.

Mathew Monfort, Carl Vondrick, Aude Oliva, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa M. Brown, Quanfu Fan, Dan Gutfreund

IEEE Trans. Pattern Anal. Mach. Intell., 2020

We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos.

Alex Andonian, Camilo Fosco, Mathew Monfort, Allen Lee, Rogério Feris, Carl Vondrick, Aude Oliva

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Reasoning About Human-Object Interactions Through Dual Attention Networks.

Tete Xiao, Quanfu Fan, Danny Gutfreund, Mathew Monfort, Aude Oliva, Bolei Zhou

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Multi-Agent Tensor Fusion for Contextual Trajectory Prediction.

Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris L. Baker, Yibiao Zhao, Yizhou Wang, Ying Nian Wu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Identifying Interpretable Action Concepts in Deep Networks.

Kandan Ramakrishnan, Mathew Monfort, Barry A. McNamara, Alex Lascelles, Dan Gutfreund, Rogério Schmidt Feris, Aude Oliva

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2017

Goal-predictive robotic teleoperation from noisy sensors.

Christopher Schultz, Sanket Gaurav, Mathew Monfort, Lingfei Zhang, Brian D. Ziebart

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Asynchronous Data Aggregation for Training End to End Visual Control Networks.

Mathew Monfort, Matthew Johnson, Aude Oliva, Katja Hofmann

Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

2016

End to End Learning for Self-Driving Cars.

Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, Karol Zieba

CoRR, 2016

Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer.

Xiangli Chen, Mathew Monfort, Brian D. Ziebart, Peter Carr

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Robust Covariate Shift Regression.

Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Softstar: Heuristic-Guided Probabilistic Inference.

Mathew Monfort, Brenden M. Lake, Brian D. Ziebart, Patrick Lucey, Joshua B. Tenenbaum

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Graph-Based Inverse Optimal Control for Robot Manipulation.

Arunkumar Byravan, Mathew Monfort, Brian D. Ziebart, Byron Boots, Dieter Fox

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation.

Mathew Monfort, Anqi Liu, Brian D. Ziebart

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015