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Hierarchical Policy Learning is Sensitive to Goal Space Design

May 04, 2019
Zach Dwiel, Madhavun Candadai, Mariano J. Phielipp, Arjun K. Bansal

* Accepted to be presented at Task-Agnostic Reinforcement Learning (TARL) workshop at ICLR'19 

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Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

Jan 30, 2018
Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, Will Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, Tristan J. Webb

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Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks

Dec 02, 2017
Urs Köster, Tristan J. Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William H. Constable, Oğuz H. Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, Naveen Rao

* 14 pages, 5 figures, accepted in Neural Information Processing Systems 2017 

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Discovering Hidden Factors of Variation in Deep Networks

Jun 17, 2015
Brian Cheung, Jesse A. Livezey, Arjun K. Bansal, Bruno A. Olshausen

* Presented at International Conference on Learning Representations 2015 Workshop 

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