Evan Racah

Publication Activity (10 Years)

Years Active: 2016-2022
Publications (10 Years): 21

Top Topics

Imitation Learning

Matrix Factorization

Top Venues

Publications

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
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving. CoRR (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
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving. IROS (2022)
Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. CoRR (2020)
Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. NeurIPS (2020)
Evan Racah, Sarath Chandar
Slot Contrastive Networks: A Contrastive Approach for Representing Objects. CoRR (2020)
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm
Unsupervised State Representation Learning in Atari. CoRR (2019)
Evan Racah, Christopher J. Pal
Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments. CoRR (2019)
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm
Unsupervised State Representation Learning in Atari. NeurIPS (2019)
Thorsten Kurth, Jian Zhang, Nadathur Satish, Evan Racah, Ioannis Mitliagkas, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey
Deep learning at 15PF: supervised and semi-supervised classification for scientific data. SC (2017)
Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Chris Pal
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. NIPS (2017)
Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data. CoRR (2017)
Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC. CoRR (2017)
Alex Gittens, Jey Kottalam, Jiyan Yang, Michael F. Ringenburg, Jatin Chhugani, Evan Racah, Mohitdeep Singh, Yushu Yao, Curt Fischer, Oliver Rübel, Benjamin P. Bowen, Norman G. Lewis, Michael W. Mahoney, Venkat Krishnamurthy, Prabhat
A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. IPDPS Workshops (2016)
Alex Gittens, Aditya Devarakonda, Evan Racah, Michael F. Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn J. Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat
Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. IEEE BigData (2016)
Yunjie Liu, Evan Racah, Prabhat, Joaquin Correa, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael F. Wehner, William D. Collins
Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. CoRR (2016)
Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter J. Sadowski, Evan Racah, Surendra Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. CoRR (2016)
Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter J. Sadowski, Evan Racah, Surendra Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. IPDPS (2016)
Evan Racah, Christopher Beckham, Tegan Maharaj, Prabhat, Christopher J. Pal
Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets. CoRR (2016)
Alex Gittens, Aditya Devarakonda, Evan Racah, Michael F. Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn J. Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies. CoRR (2016)
Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. ICMLA (2016)
Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks. CoRR (2016)