Grammars for Games: A Gradient-Based, Game-Theoretic Framework for Optimization in Deep Learning-Reference-Cited by-同舟云学术

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Grammars for Games: A Gradient-Based, Game-Theoretic Framework for Optimization in Deep Learning

Published:2016-01-18 Issue: Volume:2 Page:
ISSN:2296-9144
Container-title:Frontiers in Robotics and AI
language:
Short-container-title:Front. Robot. AI

Author:

Balduzzi David

Funder

Victoria University of Wellington

Publisher

Frontiers Media SA

Subject

Artificial Intelligence,Computer Science Applications

Reference106 articles.

1. A collaborative mechanism for crowdsourcing prediction problems;Abernethy,2011

2. Information-theoretic lower bounds on the oracle complexity of convex optimization;Agarwal,2009

3. On Lower and Upper Bounds for Smooth and Strongly Convex Optimization Problems;Arjevani,2016

4. Residual algorithms: reinforcement learning with function approximation;Baird,1995

5. Falsification and future performance;Balduzzi,2011

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