Search Results for author: Roy H. Campbell

Found 9 papers, 4 papers with code

Model Based Reinforcement Learning for Atari

no code implementations ICLR 2020 Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Model-based Reinforcement Learning +3

Model-Based Reinforcement Learning for Atari

2 code implementations1 Mar 2019 Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Atari Games 100k +4

TicTac: Accelerating Distributed Deep Learning with Communication Scheduling

1 code implementation8 Mar 2018 Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy H. Campbell

We develop a system, TicTac, to improve the iteration time by fixing this issue in distributed deep learning with Parameter Servers while guaranteeing near-optimal overlap of communication and computation.

Scheduling

Stochastic Variational Video Prediction

3 code implementations ICLR 2018 Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine

We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods.

Video Generation Video Prediction

Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case

no code implementations29 Sep 2017 Faraz Faghri, Sayed Hadi Hashemi, Mohammad Babaeizadeh, Mike A. Nalls, Saurabh Sinha, Roy H. Campbell

In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most influential algorithms that have been widely used in the bioinformatics community.

BIG-bench Machine Learning Clustering +3

NoiseOut: A Simple Way to Prune Neural Networks

no code implementations18 Nov 2016 Mohammad Babaeizadeh, Paris Smaragdis, Roy H. Campbell

In this paper, we propose NoiseOut, a fully automated pruning algorithm based on the correlation between activations of neurons in the hidden layers.

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