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Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization.
On the evolution of random graphs
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Heuristic algorithms for scheduling independent tasks on nonidentical processors
Oscar H Ibarra and Chul E Kim · 1977
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Leslie G Valiant · 1990
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Logp: Towards a realistic model of parallel computation
David Culler, Richard Karp, David Patterson, Abhijit Sahay, Klaus Erik Schauser, Eunice Santos, Ramesh Subramonian, and Thorsten Von Eicken · 1993
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Roger W Hockney · 1994
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A genetic algorithm for multiprocessor scheduling
Edwin SH Hou, Nirwan Ansari, and Hong Ren · 1994
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Giovanni De Micheli · 1994
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Combinatorial optimization: algorithms and complexity
Christos H Papadimitriou and Kenneth Steiglitz · 1998
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Collective dynamics of ‘small-world’networks
Duncan J Watts and Steven H Strogatz · 1998
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Static scheduling algorithms for allocating directed task graphs to multiprocessors
Yu-Kwong Kwok and Ishfaq Ahmad · 1999
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Statistical mechanics of complex networks
Réka Albert and Albert-László Barabási · 2002
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Biased random-key genetic algorithms for combinatorial optimization
José Fernando Gonçalves and Mauricio GC Resende · 2011
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Computer architecture: a quantitative approach
John L Hennessy and David A Patterson · 2011
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Scheduling , volume 29
Michael L Pinedo · 2012
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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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, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al · 2018
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Film: Visual reasoning with a general conditioning layer
Reinforced genetic algorithm learning for optimizing computation graphs
Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, and Oriol Vinyals · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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How neural networks extrapolate: From feedforward to graph neural networks
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2020
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Learning to dispatch for job shop scheduling via deep reinforcement learning
Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, and Xu Chi · 2020
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Transferable graph optimizers for ml compilers
Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong, Peter Ma, Qiumin Xu, Hanxiao Liu, Phitchaya Phothilimtha, Shen Wang, Anna Goldie, et al · 2020
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Observe and look further: Achieving consistent performance on atari
Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado Van Hasselt, John Quan, Mel Večerík, et al · 2018
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Machine learning in compiler optimization
Zheng Wang and Michael O’Boyle · 2018
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Placeto: Learning generalizable device placement algorithms for distributed machine learning
Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, and Mohammad Alizadeh · 2019
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Understanding the impact of entropy on policy optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, and Dale Schuurmans · 2019
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Learning scheduling algorithms for data processing clusters
Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh · 2019
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pymoo: Multi-objective optimization in python
J. Blank and K. Deb · 2020
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Optimizing memory placement using evolutionary graph reinforcement learning
Shauharda Khadka, Estelle Aflalo, Mattias Mardar, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, and Somdeb Majumdar · 2021
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Ai and compute
Dario Amodei and Danny Hernandez · 2022
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Bayesian structure learning with generative flow networks
Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio · 2022
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Neural topological ordering for computation graphs
Mukul Gagrani, Corrado Rainone, Yang Yang, Harris Teague, Wonseok Jeon, Herke Van Hoof, Weiliang Will Zeng, Piero Zappi, Christopher Lott, and Roberto Bondesan · 2022
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Biological sequence design with gflownets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al · 2022
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Trajectory balance: Improved credit assignment in gflownets
Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2022
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Evaluating generalization in gflownets for molecule design
Andrei Cristian Nica, Moksh Jain, Emmanuel Bengio, Cheng-Hao Liu, Maksym Korablyov, Michael M Bronstein, and Yoshua Bengio · 2022
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Generative flow networks for discrete probabilistic modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, and Yoshua Bengio · 2022
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