Mathew Monfort
Cited by
Cited by
End to end learning for self-driving cars
M Bojarski, D Del Testa, D Dworakowski, B Firner, B Flepp, P Goyal, ...
arXiv preprint arXiv:1604.07316, 2016
Moments in time dataset: one million videos for event understanding
M Monfort, A Andonian, B Zhou, K Ramakrishnan, SA Bargal, Y Yan, ...
IEEE transactions on pattern analysis and machine intelligence, 2019
Multi-agent tensor fusion for contextual trajectory prediction
T Zhao, Y Xu, M Monfort, W Choi, C Baker, Y Zhao, Y Wang, YN Wu
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
quality vs quantity: Improved shot prediction in soccer using strategic features from spatiotemporal data
P Lucey, A Bialkowski, M Monfort, P Carr, I Matthews
Proc. 8th annual mit sloan sports analytics conference, 1-9, 2014
Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation.
M Monfort, A Liu, BD Ziebart
AAAI, 3672-3678, 2015
Robust covariate shift regression
X Chen, M Monfort, A Liu, BD Ziebart
Artificial Intelligence and Statistics, 1270-1279, 2016
Graph-based inverse optimal control for robot manipulation
A Byravan, M Monfort, B Ziebart, B Boots, D Fox
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
Reasoning about human-object interactions through dual attention networks
T Xiao, Q Fan, D Gutfreund, M Monfort, A Oliva, B Zhou
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Multi-moments in time: Learning and interpreting models for multi-action video understanding
M Monfort, K Ramakrishnan, A Andonian, BA McNamara, A Lascelles, ...
arXiv preprint arXiv:1911.00232, 2019
Goal-Predictive Robotic Teleoperation from Noisy Sensors
C Schultz, S Gaurav, M Monfort, L Zhang, BD Ziebart
ICRA, 2017
A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields
M Monfort, T Luciani, J Komperda, B Ziebart, F Mashayek, GE Marai
Modeling, Analysis, and Visualization of Anisotropy, 2017
Softstar: Heuristic-guided probabilistic inference
M Monfort, BM Lake, B Ziebart, P Lucey, J Tenenbaum
Neural Information Processing Systems Foundation, Inc., 2015
Asynchronous Data Aggregation for Training End to End Visual Control Networks.
M Monfort, M Johnson, A Oliva, K Hofmann
AAMAS, 530-537, 2017
Layered hybrid inverse optimal control for learning robot manipulation from demonstration
A Byravan, M Montfort, B Ziebart, B Boots, D Fox
NIPS workshop on autonomous learning robots. Citeseer, 2014
Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions
M Monfort, SY Jin, A Liu, D Harwath, R Feris, J Glass, A Oliva
CVPR, 2021
We have so much in common: Modeling semantic relational set abstractions in videos
A Andonian, C Fosco, M Monfort, A Lee, R Feris, C Vondrick, A Oliva
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer
X Chen, M Monfort, BD Ziebart, P Carr
UAI, 2016
Identifying Interpretable Action Concepts in Deep Networks.
K Ramakrishnan, M Monfort, BA McNamara, A Lascelles, D Gutfreund, ...
CVPR Workshops, 12-15, 2019
Predictive inverse optimal control in large decision processes via heuristic-based search
M Monfort, BM Lake, BD Ziebart, JB Tenenbaum
ICML Workshop on Robot Learning, 2013
Methods in Large Scale Inverse Optimal Control
M Monfort
University of Illinois at Chicago, 2016
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