| 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 | 2553 | 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 | 194 | 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 | 119 | 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/CVF Conference on Computer Vision and Pattern …, 2019 | 114 | 2019 |
| Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation. M Monfort, A Liu, BD Ziebart AAAI, 3672-3678, 2015 | 47 | 2015 |
| Robust covariate shift regression X Chen, M Monfort, A Liu, BD Ziebart Artificial Intelligence and Statistics, 1270-1279, 2016 | 34 | 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 | 26 | 2015 |
| Goal-Predictive Robotic Teleoperation from Noisy Sensors C Schultz, S Gaurav, M Monfort, L Zhang, BD Ziebart ICRA, 2017 | 11 | 2017 |
| 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 | 10 | 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 | 9 | 2017 |
| Softstar: Heuristic-guided probabilistic inference M Monfort, BM Lake, B Ziebart, P Lucey, J Tenenbaum Neural Information Processing Systems Foundation, Inc., 2015 | 8 | 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 | 8 | 2014 |
| 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 | 7 | 2019 |
| Asynchronous Data Aggregation for Training End to End Visual Control Networks. M Monfort, M Johnson, A Oliva, K Hofmann AAMAS, 530-537, 2017 | 7 | 2017 |
| 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 arXiv preprint arXiv:2008.05596, 2020 | 3 | 2020 |
| Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer X Chen, M Monfort, BD Ziebart, P Carr UAI, 2016 | 3 | 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 | 2 | 2013 |
| Methods in Large Scale Inverse Optimal Control M Monfort University of Illinois at Chicago, 2016 | 1 | 2016 |
| Spoken Moments: A Large Scale Dataset of Audio Descriptions of Dynamic Events in Video M Monfort, SY Jin, D Harwath, R Feris, J Glass, A Oliva Journal of Vision 20 (11), 1447-1447, 2020 | | 2020 |
| A cognitively-aligned representational space for DNNs K Ramakrishnan, Y Mohsenzadeh, M Monfort, A Oliva Journal of Vision 19 (10), 61-61, 2019 | | 2019 |
Mariusz BojarskiDeep Learning Research and Development Engineer at NVIDIAVerified email at nyu.edu
