no code implementations • 4 Mar 2021 • Mary B. Makarious, Hampton L. Leonard, Dan Vitale, Hirotaka Iwaki, David Saffo, Lana Sargent, Anant Dadu, Eduardo Salmerón Castaño, John F. Carter, Melina Maleknia, Juan A. Botia, Cornelis Blauwendraat, Roy H. Campbell, Sayed Hadi Hashemi, Andrew B. Singleton, Mike A. Nalls, Faraz Faghri
GenoML is a Python package automating machine learning workflows for genomics (genetics and multi-omics) with an open science philosophy.
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.
2 code implementations • 1 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.
Ranked #12 on Atari Games 100k on Atari 100k
no code implementations • 3 Dec 2018 • Vipul Satone, Rachneet Kaur, Faraz Faghri, Mike A. Nalls, Andrew B Singleton, Roy H. Campbell
The proposed work will enable early detection and characterization of distinct disease subtypes based on clinical heterogeneity.
1 code implementation • 8 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.
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.
Ranked #5 on Video Prediction on KTH
no code implementations • 29 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.
1 code implementation • 20 Apr 2017 • Prajit Ramachandran, Tom Le Paine, Pooya Khorrami, Mohammad Babaeizadeh, Shiyu Chang, Yang Zhang, Mark A. Hasegawa-Johnson, Roy H. Campbell, Thomas S. Huang
In this work, we describe a method to speed up generation in convolutional autoregressive models.
no code implementations • 18 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.