Layered hybrid inverse optimal control for learning robot manipulation from demonstration
Arunkumar Byravan , Mathew Montfort , Brian Ziebart , Byron Boots
Smpte Journal
9
2014
End to End Learning for Self-Driving Cars. CoRR abs/1604.07316 (2016)
Bojarski Mariusz , Daniel Dworakowski Del Testa Davide , Bernhard Firner , Beat Flepp
arXiv preprint arXiv:1604.07316
2
2016
Softstar: heuristic-guided probabilistic inference
Brian D. Ziebart , Patrick Lucey , Brenden M. Lake , Joshua B. Tenenbaum
neural information processing systems 28 2764 -2772
5
2015
"Quality vs Quantity": Improved Shot Prediction in Soccer using Strategic Features from Spatiotemporal Data
Patrick Lucey , Alina Bialkowski , Peter Carr , Iain Matthews
65
2015
Graph-based inverse optimal control for robot manipulation
Brian Ziebart , Byron Boots , Arunkumar Byravan , Dieter Fox
international conference on artificial intelligence 1874 -1890
32
2015
End to End Learning for Self-Driving Cars
Beat Flepp , Lawrence D. Jackel , Davide Del Testa , Karol Zieba
arXiv: Computer Vision and Pattern Recognition
4,003
2016
Robust Covariate Shift Regression
Brian D. Ziebart , Anqi Liu , Mathew Monfort , Xiangli Chen
international conference on artificial intelligence and statistics 1270 -1279
29
2016
Adversarial inverse optimal control for general imitation learning losses and embodiment transfer
Brian D. Ziebart , Peter Carr , Mathew Monfort , Xiangli Chen
uncertainty in artificial intelligence 102 -111
7
2016
Asynchronous Data Aggregation for Training End to End Visual Control Networks
Katja Hofmann , Mathew Monfort , Aude Oliva , Matthew Johnson
adaptive agents and multi agents systems 530 -537
4
2017
Examining Interpretable Feature Relationships in Deep Networks for Action recognition
Rogerio Feris , Kandan Ramakrishnan , Mathew Monfort , Aude Oliva
2019
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
Yizhou Wang , Wongun Choi , Ying Nian Wu , Mathew Monfort
arXiv: Computer Vision and Pattern Recognition
296
2019
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding.
Mathew Monfort , Bowen Pan , Kandan Ramakrishnan , Alex Andonian
arXiv: Computer Vision and Pattern Recognition
42
2019
Goal-predictive robotic teleoperation from noisy sensors
Christopher Schultz , Sanket Gaurav , Mathew Monfort , Lingfei Zhang
international conference on robotics and automation 5377 -5383
6
2017
Moments in Time Dataset: One Million Videos for Event Understanding
Mathew Monfort , Carl Vondrick , Aude Oliva , Alex Andonian
IEEE Transactions on Pattern Analysis and Machine Intelligence 42 ( 2) 502 -508
437
2020
Examining Class Dependant Sub-Paths in Deep Neural Networks
Mathew Monfort , Kandan Ramakrishnan , Alex Andonian , Aude Oliva
Journal of Vision 19 ( 10)
2019
A Large Scale Video Dataset for Event Recognition
Mathew Monfort , Bolei Zhou , Sarah Bargal , Alex Andonian
Journal of Vision 18 ( 10) 753 -753
2018
A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields
Mathew Monfort , Timothy Luciani , Jonathan Komperda , Brian Ziebart
Mathematics and Visualization 375 -392
10
2017
A cognitively-aligned representational space for DNNs
Kandan Ramakrishnan , Yalda Mohsenzadeh , Mathew Monfort , Aude Oliva
Journal of Vision 19 ( 10) 61 -61
2019
Reasoning About Human-Object Interactions Through Dual Attention Networks
Tete Xiao , Quanfu Fan , Danny Gutfreund , Mathew Monfort
international conference on computer vision 3919 -3928
13
2019
Model ChangeLists: Characterizing Changes in ML Prediction APIs
Sabri Eyuboglu , Karan Goel , Arjun D Desai , Lingjiao Chen
2022