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Evan Racah

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Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving

Oct 18, 2022
Eli Bronstein, Mark Palatucci, Dominik Notz, Brandyn White, Alex Kuefler, Yiren Lu, Supratik Paul, Payam Nikdel, Paul Mougin, Hongge Chen, Justin Fu, Austin Abrams, Punit Shah, Evan Racah, Benjamin Frenkel, Shimon Whiteson, Dragomir Anguelov

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Slot Contrastive Networks: A Contrastive Approach for Representing Objects

Jul 18, 2020
Evan Racah, Sarath Chandar

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The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

Jul 07, 2020
Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar

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Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments

Jun 27, 2019
Evan Racah, Christopher Pal

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Unsupervised State Representation Learning in Atari

Jun 26, 2019
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm

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Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC

Nov 29, 2017
Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah

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ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events

Nov 25, 2017
Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Christopher Pal

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Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

Aug 17, 2017
Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Mostofa Ali Patwary, Tareq Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey

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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

Dec 06, 2016
Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat

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