Mathew Monfort
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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
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
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 Conference on Computer Vision and Pattern …, 2019
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
Goal-Predictive Robotic Teleoperation from Noisy Sensors
C Schultz, S Gaurav, M Monfort, L Zhang, BD Ziebart
ICRA, 2017
Asynchronous data aggregation for training end to end visual control networks
M Monfort, M Johnson, A Oliva, K Hofmann
Proceedings of the 16th Conference on Autonomous Agents and MultiAgent …, 2017
Softstar: Heuristic-guided probabilistic inference
M Monfort, BM Lake, B Ziebart, P Lucey, J Tenenbaum
Advances in Neural Information Processing Systems, 2764-2772, 2015
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
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 International Conference on Computer Vision, 3919-3928, 2019
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
Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer
X Chen, M Monfort, BD Ziebart, P Carr
UAI, 2016
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
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
Methods in Large Scale Inverse Optimal Control
M Monfort
University of Illinois at Chicago, 2016
A cognitively-aligned representational space for DNNs
K Ramakrishnan, Y Mohsenzadeh, M Monfort, A Oliva
Journal of Vision 19 (10), 61-61, 2019
Examining Class Dependant Sub-Paths in Deep Neural Networks
M Monfort, K Ramakrishnan, A Andonian, A Oliva
Journal of Vision 19 (10), 28b-28b, 2019
Examining Interpretable Feature Relationships in Deep Networks for Action recognition
M Monfort, K Ramakrishnan, BA McNamara, A Lascelles, D Gutfreund, ...
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