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A majority of recent developments in neural architecture search (NAS) have been aimed at decreasing the computational cost of various techniques without affecting their final performance.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M.; and Le, Q. V. 2019 · 1905
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Laube, K. A.; and Zell, A. 2019 · 1906
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Darts+: Improved differentiable architecture search with early stopping
Liang, H.; Zhang, S.; Sun, J.; He, X.; Huang, W.; Zhuang, K.; and Li, Z. 2019 · 1909
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Such, F. P.; Rawal, A.; Lehman, J.; Stanley, K. O.; and Clune, J. 2019 · 1912
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Darwinian optimization of synthetic neural systems
Dress, W. 1987 · 1987
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Self organizing neural networks for the identification problem
Tenorio, M.; and Lee, W.-T. 1988 · 1988
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Designing Neural Networks using Genetic Algorithms
Miller, G. F.; Todd, P. M.; and Hegde, S. U. 1989 · 1989
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Designing neural networks using genetic algorithms with graph generation system
Kitano, H. 1990 · 1990
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An evolutionary algorithm that constructs recurrent neural networks
Angeline, P. J.; Saunders, G. M.; and Pollack, J. B. 1994 · 1994
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Storn, R.; and Price, K. 1997 · 1997
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Evolving neural networks through augmenting topologies
Stanley, K. O.; and Miikkulainen, R. 2002 · 2002
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Electra: Pre-training text encoders as discriminators rather than generators
Clark, K.; Luong, M.-T.; Le, Q. V.; and Manning, C. D. 2020 · 2003
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On coresets for k-means and k-median clustering
Har-Peled, S.; and Mazumdar, S. 2004 · 2004
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Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
Rawal, A.; Lehman, J.; Such, F. P.; Clune, J.; and Stanley, K. O. 2020 · 2005
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FBNetV3: Joint architecture-recipe search using neural acquisition function
Dai, X.; Wan, A.; Zhang, P.; Wu, B.; He, Z.; Wei, Z.; Chen, K.; Tian, Y.; Yu, M.; Vajda, P.; et al. 2020 · 2006
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Neural Architecture Search without Training
Mellor, J.; Turner, J.; Storkey, A.; and Crowley, E. J. 2020 · 2006
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Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Ru, B.; Lyle, C.; Schut, L.; van der Wilk, M.; and Gal, Y. 2020 · 2006
Cited alongside, same era.
Glister: Generalization based data subset selection for efficient and robust learning
Killamsetty, K.; Sivasubramanian, D.; Ramakrishnan, G.; and Iyer, R. 2020 · 2012
Cited alongside, same era.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Domhan, T.; Springenberg, J. T.; and Hutter, F. 2015 · 2015
Cited alongside, same era.
Neural Architecture Search with Reinforcement Learning
Zoph, B.; and Le, Q. V. 2017 · 2017
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
Bender, G.; Kindermans, P.-J.; Zoph, B.; Vasudevan, V.; and Le, Q. 2018 · 2018
Coresets for data-efficient training of machine learning models
Mirzasoleiman, B.; Bilmes, J.; and Leskovec, J. 2020 · 2020
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Understanding and Robustifying Differentiable Architecture Search
Zela, A.; Elsken, T.; Saikia, T.; Marrakchi, Y.; Brox, T.; and Hutter, F. 2020 · 2020
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Econas: Finding proxies for economical neural architecture search
Zhou, D.; Zhou, X.; Zhang, W.; Loy, C. C.; Yi, S.; Zhang, X.; and Ouyang, W. 2020 · 2020
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Zero-Cost Proxies for Lightweight {NAS}
Abdelfattah, M. S.; Mehrotra, A.; Dudziak, Ł.; and Lane, N. D. 2021 · 2021
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DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization
Awad, N.; Mallik, N.; and Hutter, F. 2021 · 2021
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Cited alongside, same era.
BOHB: Robust and efficient hyperparameter optimization at scale
Falkner, S.; Klein, A.; and Hutter, F. 2018 · 2018
Cited alongside, same era.
It’s time to do something: Mitigating the negative impacts of computing through a change to the peer review process
Hecht, B.; Wilcox, L.; Bigham, J. P.; Schöning, J.; Hoque, E.; Ernst, J.; Bisk, Y.; De Russis, L.; Yarosh, L.; Anjum, B.; Contractor, D.; and Wu, C. 2018 · 2018
Cited alongside, same era.
Neural architecture search with bayesian optimisation and optimal transport
Kandasamy, K.; Neiswanger, W.; Schneider, J.; Poczos, B.; and Xing, E. P. 2018 · 2018
Cited alongside, same era.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L.; Jamieson, K.; DeSalvo, G.; Rostamizadeh, A.; and Talwalkar, A. 2018 · 2018
Cited alongside, same era.
Efficient neural architecture search via parameters sharing
Pham, H.; Guan, M.; Zoph, B.; Le, Q.; and Dean, J. 2018 · 2018
Cited alongside, same era.
Neural architecture search: A survey
Elsken, T.; Metzen, J. H.; and Hutter, F. 2019 · 2019
Cited alongside, same era.
Darts: Differentiable architecture search
Liu, H.; Simonyan, K.; and Yang, Y. 2019 · 2019
Cited alongside, same era.
Killamsetty, K.; Durga, S.; Ramakrishnan, G.; De, A.; and Iyer, R. 2021 · 2021
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Geometry-Aware Gradient Algorithms for Neural Architecture Search
Li, L.; Khodak, M.; Balcan, M.-F.; and Talwalkar, A. 2021 · 2021
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Accelerating Neural Architecture Search via Proxy Data
Na, B.; Mok, J.; Choe, H.; and Yoon, S. 2021 · 2021
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Core-set Sampling for Efficient Neural Architecture Search
Shim, J.-h.; Kong, K.; and Kang, S.-J. 2021 · 2021
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Towards Green Automated Machine Learning: Status Quo and Future Directions
Tornede, T.; Tornede, A.; Hanselle, J.; Wever, M.; Mohr, F.; and Hüllermeier, E. 2021 · 2021
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Rethinking architecture selection in differentiable NAS
Wang, R.; Cheng, M.; Chen, X.; Tang, X.; and Hsieh, C.-J. 2021 · 2021
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BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
White, C.; Neiswanger, W.; and Savani, Y. 2021 · 2021
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NAS-Bench-x11 and the Power of Learning Curves
Yan, S.; White, C.; Savani, Y.; and Hutter, F. 2021 · 2021
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Submodlib: A Submodular Optimization Library
Kaushal, V.; Ramakrishnan, G.; and Iyer, R. 2022 · 2022
Closest in time.
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Killamsetty, K.; Abhishek, G. S.; Evfimievski, A. V.; Popa, L.; Ramakrishnan, G.; Iyer, R.; et al. 2022 · 2022
Closest in time.
An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
Vincent, A. M.; and Jidesh, P. 2022 · 2022
Closest in time.