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Existing combinatorial search methods are often complex and require some level of expertise.
A formal basis for the heuristic determination of minimum cost paths
Peter E Hart, Nils J Nilsson, and Bertram Raphael · 1968
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Heuristic search viewed as path finding in a graph
Ira Pohl · 1970
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Updating quasi-newton matrices with limited storage
Jorge Nocedal · 1980
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Depth-first iterative deepening: An optimal admissible tree search
Richard E Korf · 1985
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Finding optimal solutions to Rubik’s Cube using pattern databases
Richard E Korf · 1997
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Combinatorial Optimization: Algorithms and Complexity
Christos H Papadimitriou and Kenneth Steiglitz · 1998
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The parallel search bench ZRAM and its applications
Adrian Brüngger, Ambros Marzetta, Komei Fukuda, and Jürg Nievergelt · 1999
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Twenty-six moves suffice for Rubik’s Cube
Daniel Kunkle and Gene Cooperman · 2007
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Linear-time disk-based implicit graph search
Richard E Korf · 2008
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Weighted a* search–unifying view and application
Rüdiger Ebendt and Rolf Drechsler · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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The traveling salesman problem
David L Applegate, Robert E Bixby, Vašek Chvátal, and William J Cook · 2011
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Learning heuristic functions for large state spaces
Shahab Jabbari Arfaee, Sandra Zilles, and Robert C Holte · 2011
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Traveling salesman problem heuristics: Leading methods, implementations and latest advances
César Rego, Dorabela Gamboa, Fred Glover, and Colin Osterman · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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The diameter of the Rubik’s Cube group is twenty
Tomas Rokicki, Herbert Kociemba, Morley Davidson, and John Dethridge · 2014
Solving the Rubik’s Cube with deep reinforcement learning and search
Forest Agostinelli, Stephen McAleer, Alexander Shmakov, and Pierre Baldi · 2019
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A constructive prediction of the generalization error across scales
Jonathan S Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit · 2019
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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A historical note on the 3/2-approximation algorithm for the metric traveling salesman problem
René van Bevern and Viktoriia A. Slugina · 2020
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Learning combinatorial optimization on graphs: A survey with applications to networking
Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez, and Magnus Boman · 2020
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Solving the Rubik’s Cube optimally is NP-complete
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Solving the Rubik’s Cube with approximate policy iteration
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Reinforcement learning for combinatorial optimization: A survey
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Artificial Intelligence: A Modern Approach. 4th
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Learning the travelling salesperson problem requires rethinking generalization
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