Research Papers — Evan Racah

Research Papers

	Unsupervised State Representation Learning in Atari Evan Racah, Ankesh Anand, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm NeurIPS, 2019 arxiv / workshop version / slides / code / reproduction equal contribution A new method for unsupervised learning of state representations from visual RL environments, as well as a new benchmark for measuring these representations.
	Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments Evan Racah, Christopher Pal ICML Workshop on Self-Supervised Learning, 2019 arxiv / workshop version / code An qualitative and quantitative examination of the features learned by several popular self-supervised methods in video-game-like environments
	Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Evan Racah Journal of Physics: Conference Series, 2018 arxiv / code Classifying RPV-Supersymmetry events with deep neural networks
	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, Mr. Prabhat, Chris Pal NIPS (now NeurIPS), 2017 project page / code / arxiv A high dimensional spatiotemporal dataset for detection of extreme weather events
	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 Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Pradeep Dubey Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing), 2017 arxiv Scaling Deep Learning for climate and HEP on a large supercomputer
	Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat IEEE International Conference on Machine Learning and Applications (ICMLA), 2016 video demo / slides / code / arxiv Categorizing high energy physics phenomena in an unsupervised way
	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 Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W Mahoney 2016 IEEE International Conference on Big Data (Big Data), 2016 code / arxiv Comparing Apache Spark with C and MPI for scientific analysis workloads
	Application of deep convolutional neural networks for detecting extreme weather in climate datasets Yunjie Liu, Evan Racah, Joaquin Correa, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael Wehner, William Collins arXiv preprint arXiv:1605.01156 Classifying extreme weather events from simulation
	H5spark: bridging the I/O gap between spark and scientific data formats on Hpc systems Jialin Liu, Evan Racah, Quincey Koziol, Richard Shane Canon, Alex Gittens, Lisa Gerhardt, Suren Byna, Mike F. Ringenburg, Prabhat Cray User Group, 2016 slides / code A plugin to Apache Spark to read in HDF5 Files