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We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler.
Dynamic control flow in large-scale machine learning
Yuan Yu, Martín Abadi, Paul Barham, Eugene Brevdo, Mike Burrows, Andy Davis, Jeff Dean, Sanjay Ghemawat, Tim Harley, Peter Hawkins, Michael Isard, Manjunath Kudlur, Rajat Monga, Derek Gordon Murray, and Xiaoqiang Zheng · 1905
Earlier work this paper cites.
On the evolution of random graphs
Paul Erdos and Alfréd Rényi · 1960
Earlier work this paper cites.
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Earlier work this paper cites.
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Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt · 1983
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Emergence of scaling in random networks
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Earlier work this paper cites.
Static scheduling algorithms for allocating directed task graphs to multiprocessors
Yu-Kwong Kwok and Ishfaq Ahmad · 1999
Earlier work this paper cites.
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Earlier work this paper cites.
The graph neural network model
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Earlier work this paper cites.
Biased random-key genetic algorithms for combinatorial optimization
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Earlier work this paper cites.
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
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Parallel scheduling of task trees with limited memory
Lionel Eyraud-Dubois, Loris Marchal, Oliver Sinnen, and Frédéric Vivien · 2015
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
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Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2018
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Tvm: An automated end-to-end optimizing compiler for deep learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy · 2018
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Learning to optimize tensor programs
Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy · 2018
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