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ORCIDMathew Monfort
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2020 – today
- 2024
[c15]Riccardo Fogliato, Pratik Patil, Mathew Monfort, Pietro Perona:
A Framework for Efficient Model Evaluation Through Stratification, Sampling, and Estimation. ECCV (88) 2024: 140-158- [c14]Riccardo Fogliato, Pratik Patil, Nil-Jana Akpinar, Mathew Monfort:
Precise Model Benchmarking with Only a Few Observations. EMNLP 2024: 9563-9575 - [c13]Sabri Eyuboglu
, Karan Goel, Arjun D. Desai, Lingjiao Chen, Mathew Monfort, Christopher Ré, James Zou:
Model ChangeLists: Characterizing Updates to ML Models. FAccT 2024: 2432-2453 - [i9]Riccardo Fogliato, Pratik Patil, Mathew Monfort, Pietro Perona:
A Framework for Efficient Model Evaluation through Stratification, Sampling, and Estimation. CoRR abs/2406.07320 (2024) - [i8]Riccardo Fogliato, Pratik Patil, Nil-Jana Akpinar, Mathew Monfort:
Precise Model Benchmarking with Only a Few Observations. CoRR abs/2410.05222 (2024) - 2022
- [j2]Mathew Monfort, Bowen Pan, Kandan Ramakrishnan, Alex Andonian, Barry A. McNamara, Alex Lascelles, Quanfu Fan, Dan Gutfreund, Rogério Schmidt Feris, Aude Oliva:
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9434-9445 (2022) - 2021
- [c12]Mathew Monfort, SouYoung Jin, Alexander H. Liu, David Harwath, Rogério Feris, James R. Glass, Aude Oliva:
Spoken Moments: Learning Joint Audio-Visual Representations From Video Descriptions. CVPR 2021: 14871-14881 - [i7]Mathew Monfort, SouYoung Jin, Alexander H. Liu, David Harwath, Rogério Feris, James R. Glass, Aude Oliva:
Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions. CoRR abs/2105.04489 (2021) - 2020
- [j1]Mathew Monfort, Carl Vondrick, Aude Oliva, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa M. Brown, Quanfu Fan, Dan Gutfreund:
Moments in Time Dataset: One Million Videos for Event Understanding. IEEE Trans. Pattern Anal. Mach. Intell. 42(2): 502-508 (2020) - [c11]Alex Andonian, Camilo Fosco, Mathew Monfort, Allen Lee, Rogério Feris, Carl Vondrick, Aude Oliva:
We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos. ECCV (18) 2020: 18-34 - [i6]Alex Andonian, Camilo Fosco, Mathew Monfort, Allen Lee, Rogério Feris, Carl Vondrick, Aude Oliva:
We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos. CoRR abs/2008.05596 (2020)
2010 – 2019
- 2019
- [c10]Kandan Ramakrishnan, Mathew Monfort, Barry A. McNamara, Alex Lascelles, Dan Gutfreund, Rogério Schmidt Feris, Aude Oliva:
Identifying Interpretable Action Concepts in Deep Networks. CVPR Workshops 2019: 12-15 - [c9]Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris L. Baker, Yibiao Zhao, Yizhou Wang, Ying Nian Wu:
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. CVPR 2019: 12126-12134 - [c8]Tete Xiao, Quanfu Fan, Danny Gutfreund, Mathew Monfort, Aude Oliva, Bolei Zhou:
Reasoning About Human-Object Interactions Through Dual Attention Networks. ICCV 2019: 3918-3927 - [i5]Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris L. Baker, Yibiao Zhao, Yizhou Wang, Ying Nian Wu:
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. CoRR abs/1904.04776 (2019) - [i4]Tete Xiao, Quanfu Fan, Dan Gutfreund, Mathew Monfort, Aude Oliva, Bolei Zhou:
Reasoning About Human-Object Interactions Through Dual Attention Networks. CoRR abs/1909.04743 (2019) - [i3]Mathew Monfort, Kandan Ramakrishnan, Alex Andonian, Barry A. McNamara, Alex Lascelles, Bowen Pan, Quanfu Fan, Dan Gutfreund, Rogério Schmidt Feris, Aude Oliva:
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding. CoRR abs/1911.00232 (2019) - 2018
- [i2]Mathew Monfort, Bolei Zhou, Sarah Adel Bargal, Alex Andonian, Tom Yan, Kandan Ramakrishnan, Lisa M. Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, Aude Oliva:
Moments in Time Dataset: one million videos for event understanding. CoRR abs/1801.03150 (2018) - 2017
- [c7]Mathew Monfort, Matthew Johnson, Aude Oliva, Katja Hofmann:
Asynchronous Data Aggregation for Training End to End Visual Control Networks. AAMAS 2017: 530-537 - [c6]Christopher Schultz, Sanket Gaurav, Mathew Monfort, Lingfei Zhang, Brian D. Ziebart:
Goal-predictive robotic teleoperation from noisy sensors. ICRA 2017: 5377-5383 - 2016
- [c5]Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart:
Robust Covariate Shift Regression. AISTATS 2016: 1270-1279 - [c4]Xiangli Chen, Mathew Monfort, Brian D. Ziebart, Peter Carr:
Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer. UAI 2016 - [i1]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:
End to End Learning for Self-Driving Cars. CoRR abs/1604.07316 (2016) - 2015
- [c3]Mathew Monfort, Anqi Liu, Brian D. Ziebart:
Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation. AAAI 2015: 3672-3678 - [c2]Arunkumar Byravan, Mathew Monfort, Brian D. Ziebart, Byron Boots, Dieter Fox:
Graph-Based Inverse Optimal Control for Robot Manipulation. IJCAI 2015: 1874-1880 - [c1]Mathew Monfort, Brenden M. Lake, Brian D. Ziebart, Patrick Lucey, Joshua B. Tenenbaum:
Softstar: Heuristic-Guided Probabilistic Inference. NIPS 2015: 2764-2772
Coauthor Index
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