Search Results for author: Evan Racah

Found 10 papers, 6 papers with code

Slot Contrastive Networks: A Contrastive Approach for Representing Objects

no code implementations18 Jul 2020 Evan Racah, Sarath Chandar

Unsupervised extraction of objects from low-level visual data is an important goal for further progress in machine learning.

Atari Games

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

2 code implementations NeurIPS 2020 Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar

For example, the common single-task sample-efficiency metric conflates improvements due to model-based learning with various other aspects, such as representation learning, making it difficult to assess true progress on model-based RL.

Model-based Reinforcement Learning reinforcement-learning +1

Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments

2 code implementations27 Jun 2019 Evan Racah, Christopher Pal

Self-supervised methods, wherein an agent learns representations solely by observing the results of its actions, become crucial in environments which do not provide a dense reward signal or have labels.

Imitation Learning

Unsupervised State Representation Learning in Atari

3 code implementations NeurIPS 2019 Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm

State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks.

Atari Games Representation Learning

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

5 code implementations9 Nov 2017 Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah

There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments.

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

1 code implementation NeurIPS 2017 Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Christopher Pal

We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change.

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

1 code implementation5 Jul 2016 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, Prabhat

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms.

Distributed, Parallel, and Cluster Computing G.1.3; C.2.4

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

no code implementations28 Jan 2016 Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists.

General Classification

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