Evan Racah

According to our database, Evan Racah authored at least 15 papers between 2016 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving.

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

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

2020

Slot Contrastive Networks: A Contrastive Approach for Representing Objects.

Evan Racah, Sarath Chandar

CoRR, 2020

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

Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments.

Evan Racah, Christopher J. Pal

CoRR, 2019

Unsupervised State Representation Learning in Atari.

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

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

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

CoRR, 2017

Deep learning at 15PF: supervised and semi-supervised classification for scientific data.

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

Proceedings of the International Conference for High Performance Computing, 2017

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

Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Chris Pal

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets.

Evan Racah, Christopher Beckham, Tegan Maharaj, Prabhat, Christopher J. Pal

CoRR, 2016

Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets.

Yunjie Liu, Evan Racah, Prabhat, Joaquin Correa, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael F. Wehner, William D. Collins

CoRR, 2016

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies.

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

CoRR, 2016

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.

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

Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium, 2016

A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark.

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

Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, 2016

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

Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat

Proceedings of the 15th IEEE International Conference on Machine Learning and Applications, 2016

Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies.

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

Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016