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Neural architecture search (NAS) has seen a steep rise in interest over the last few years.
Handbook of mathematical functions with formulas, graphs, and mathematical tables , volume 55
Milton Abramowitz and Irene A Stegun · 1948
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An algorithm for solving travelling-salesman and related network optimization problems
Frederick Bock · 1958
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A method for solving traveling-salesman problems
Georges A Croes · 1958
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An approach to the scheduling of jobs on machines
ES Page · 1961
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An efficient heuristic procedure for partitioning graphs
Brian W Kernighan and Shen Lin · 1970
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How easy is local search?
David S Johnson, Christos H Papadimitriou, and Mihalis Yannakakis · 1988
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Designing neural networks using genetic algorithms with graph generation system
Hiroaki Kitano · 1990
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Correlated and uncorrelated fitness landscapes and how to tell the difference
Edward Weinberger · 1990
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Fast algorithms for geometric traveling salesman problems
Jon Jouis Bentley · 1992
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Landscapes and their correlation functions
Peter F Stadler · 1996
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Local search in combinatorial optimization
E Aarts and JK Lenstra · 1997
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The traveling salesman problem: A case study in local optimization
David S Johnson and Lyle A McGeoch · 1997
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Optimization using surrogate objectives on a helicopter test example
Andrew J Booker, JE Dennis, Paul D Frank, David B Serafini, and Virginia Torczon · 1998
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Early stopping-but when?
Lutz Prechelt · 1998
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Evolving artificial neural networks
Xin Yao · 1999
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Evolving neural networks through augmenting topologies
Kenneth O Stanley and Risto Miikkulainen · 2002
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Theoretical aspects of local search
Wil Michiels, Emile Aarts, and Jan Korst · 2007
Earlier work this paper cites.
An algorithm for the use of surrogate models in modular flowsheet optimization
José A Caballero and Ignacio E Grossmann · 2008
Cited alongside, same era.
Understanding dropout
Pierre Baldi and Peter J Sadowski · 2013
Cited alongside, same era.
Analysis of runtime of optimization algorithms for noisy functions over discrete codomains
Youhei Akimoto, Sandra Astete-Morales, and Olivier Teytaud · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Cited alongside, same era.
Local search yields approximation schemes for k-means and k-median in euclidean and minor-free metrics
Vincent Cohen-Addad, Philip N Klein, and Claire Mathieu · 2016
Searching for a robust neural architecture in four gpu hours
Xuanyi Dong and Yi Yang · 2019
Later among the works it cites.
Local search yields a ptas for k-means in doubling metrics
Zachary Friggstad, Mohsen Rezapour, and Mohammad R Salavatipour · 2019
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
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Best practices for scientific research on neural architecture search
Marius Lindauer and Frank Hutter · 2019
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Probo: a framework for using probabilistic programming in bayesian optimization
Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, and Eric Xing · 2019
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Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Cited alongside, same era.
Simple and efficient architecture search for convolutional neural networks
Thomas Elsken, Jan-Hendrik Metzen, and Frank Hutter · 2017
Cited alongside, same era.
Noisy evolutionary optimization algorithms–a comprehensive survey
Pratyusha Rakshit, Amit Konar, and Swagatam Das · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
Cited alongside, same era.
Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
Cited alongside, same era.
Auto-keras: Efficient neural architecture search with network morphism
Haifeng Jin, Qingquan Song, and Xia Hu · 2018
Cited alongside, same era.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Later among the works it cites.
Multi-objective neural architecture search via predictive network performance optimization
Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T Kwok, and Tong Zhang · 2019
Later among the works it cites.
Neural predictor for neural architecture search
Wei Wen, Hanxiao Liu, Hai Li, Yiran Chen, Gabriel Bender, and Pieter-Jan Kindermans · 2019
Later among the works it cites.
Nas-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, and Frank Hutter · 2019
Later among the works it cites.
K-center clustering under perturbation resilience
Maria-Florina Balcan, Nika Haghtalab, and Colin White · 2020
Closest in time.
Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Closest in time.
Local search is a remarkably strong baseline for neural architecture search
T Den Ottelander, A Dushatskiy, M Virgolin, and Peter AN Bosman · 2020
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Nas-bench-301 and the case for surrogate benchmarks for neural architecture search
Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, and Frank Hutter · 2020
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A study on encodings for neural architecture search
Colin White, Willie Neiswanger, Sam Nolen, and Yash Savani · 2020
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Does unsupervised architecture representation learning help neural architecture search?
Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, and Mi Zhang · 2020
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Neural ensemble search for performant and calibrated predictions
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter, and Yee Whye Teh · 2020
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Cate: Computation-aware neural architecture encoding with transformers
Shen Yan, Kaiqiang Song, Fei Liu, and Mi Zhang · 2021
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