Neural architecture search: Difference between revisions

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{{Short description|Machine learning-powered structure design}}

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'''Neural architecture search''' ('''NAS''')<ref name="survey">{{Cite journal|url=http://jmlr.org/papers/v20/18-598.html|title=Neural Architecture Search: A Survey|first1=Thomas|last1=Elsken|first2=Jan Hendrik|last2=Metzen|first3=Frank|last3=Hutter|date=August 8, 2019|journal=Journal of Machine Learning Research|volume=20|issue=55|pages=1–21|arxiv=1808.05377}}</ref><ref name="survey2">{{cite arXiv|last1=Wistuba|first1=Martin|last2=Rawat|first2=Ambrish|last3=Pedapati|first3=Tejaswini|date=2019-05-04|title=A Survey on Neural Architecture Search|eprint=1905.01392|class=cs.LG}}</ref> is a technique for automating the design of [[artificial neural network]]s (ANN), a widely used model in the field of [[machine learning]]. NAS has been used to design networks that are on par with or outperform hand-designed architectures.<ref name="Zoph 2016" /><ref name="Zoph 2017">{{cite arXiv|last1=Zoph|first1=Barret|last2=Vasudevan|first2=Vijay|last3=Shlens|first3=Jonathon|last4=Le|first4=Quoc V.|date=2017-07-21|title=Learning Transferable Architectures for Scalable Image Recognition|eprint=1707.07012|class=cs.CV}}</ref> Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used:<ref name="survey" />

* The ''search space'' defines the type(s) of ANN that can be designed and optimized.

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* The ''performance estimation strategy'' evaluates the performance of a possible ANN from its design (without constructing and training it).

NAS is closely related to [[hyperparameter optimization]]<ref>Matthias Feurer and Frank Hutter. [https://link.springer.com/content/pdf/10.1007%2F978-3-030-05318-5_1.pdf Hyperparameter optimization]. In: ''AutoML: Methods, Systems, Challenges'', pages 3–38.</ref> and [[meta-learning (computer science)|meta-learning]]<ref>{{Cite book|chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-05318-5_2|doi = 10.1007/978-3-030-05318-5_2|chapter = Meta-Learning|title = Automated Machine Learning|series = The Springer Series on Challenges in Machine Learning|year = 2019|last1 = Vanschoren|first1 = Joaquin|pages = 35–61|isbn = 978-3-030-05317-8|s2cid = 239362577}}</ref> and is a subfield of [[automated machine learning]] (AutoML).<ref>{{Cite journal |lastlast1=Salehin |firstfirst1=Imrus |last2=Islam |first2=Md. Shamiul |last3=Saha |first3=Pritom |last4=Noman |first4=S. M. |last5=Tuni |first5=Azra |last6=Hasan |first6=Md. Mehedi |last7=Baten |first7=Md. Abu |date=2024-01-01 |title=AutoML: A systematic review on automated machine learning with neural architecture search |url=https://www.sciencedirect.com/science/article/pii/S2949715923000604 |journal=Journal of Information and Intelligence |volume=2 |issue=1 |pages=52–81 |doi=10.1016/j.jiixd.2023.10.002 |issn=2949-7159|doi-access=free }}</ref>

==Reinforcement learning==

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== Evolution ==

An alternative approach to NAS is based on [[evolutionary algorithm]]s, which has been employed by several groups.<ref>{{cite arXiv|last1=Real|first1=Esteban|last2=Moore|first2=Sherry|last3=Selle|first3=Andrew|last4=Saxena|first4=Saurabh|last5=Suematsu|first5=Yutaka Leon|last6=Tan|first6=Jie|last7=Le|first7=Quoc|last8=Kurakin|first8=Alex|date=2017-03-03|title=Large-Scale Evolution of Image Classifiers|eprint=1703.01041|class=cs.NE}}</ref><ref>{{Cite arXiv|last1=Suganuma|first1=Masanori|last2=Shirakawa|first2=Shinichi|last3=Nagao|first3=Tomoharu|date=2017-04-03|title=A Genetic Programming Approach to Designing Convolutional Neural Network Architectures|class=cs.NE|eprint=1704.00764v2|language=en}}</ref><ref name=":0">{{Cite arXiv|last1=Liu|first1=Hanxiao|last2=Simonyan|first2=Karen|last3=Vinyals|first3=Oriol|last4=Fernando|first4=Chrisantha|last5=Kavukcuoglu|first5=Koray|date=2017-11-01|title=Hierarchical Representations for Efficient Architecture Search|class=cs.LG|eprint=1711.00436v2|language=en}}</ref><ref name="Real 2018">{{cite arXiv|last1=Real|first1=Esteban|last2=Aggarwal|first2=Alok|last3=Huang|first3=Yanping|last4=Le|first4=Quoc V.|date=2018-02-05|title=Regularized Evolution for Image Classifier Architecture Search|eprint=1802.01548|class=cs.NE}}</ref><ref>{{cite arXiv|last1=Miikkulainen|first1=Risto|last2=Liang|first2=Jason|last3=Meyerson|first3=Elliot|last4=Rawal|first4=Aditya|last5=Fink|first5=Dan|last6=Francon|first6=Olivier|last7=Raju|first7=Bala|last8=Shahrzad|first8=Hormoz|last9=Navruzyan|first9=Arshak|last10=Duffy|first10=Nigel|last11=Hodjat|first11=Babak|date=2017-03-04|title=Evolving Deep Neural Networks|class=cs.NE|eprint=1703.00548}}</ref><ref>{{Cite book|last1=Xie|first1=Lingxi|last2=Yuille|first2=Alan|title=2017 IEEE International Conference on Computer Vision (ICCV) |chapter=Genetic CNN |chapter-url=https://ieeexplore.ieee.org/document/8237416|year=2017|pages=1388–1397|doi=10.1109/ICCV.2017.154|arxiv=1703.01513|isbn=978-1-5386-1032-9|s2cid=206770867}}</ref><ref name="Elsken 2018" /> An Evolutionary Algorithm for Neural Architecture Search generally performs the following procedure.<ref name="liu2021survey">{{cite journal|last1=Liu|first1=Yuqiao|last2=Sun|first2=Yanan|last3=Xue|first3=Bing|last4=Zhang|first4=Mengjie|last5=Yen|first5=Gary G|last6=Tan|first6=Kay Chen|title=A Survey on Evolutionary Neural Architecture Search|journal=IEEE Transactions on Neural Networks and Learning Systems|year=2021|volume= 34|issue=2 |pages=1–21|doi=10.1109/TNNLS.2021.3100554|pmid=34357870|arxiv=2008.10937|s2cid=221293236}}</ref> First a pool consisting of different candidate architectures along with their validation scores (fitness) is initialised. At each step the architectures in the candidate pool are mutated (e.g.: 3x3 convolution instead of a 5x5 convolution). Next the new architectures are trained from scratch for a few epochs and their validation scores are obtained. This is followed by replacing the lowest scoring architectures in the candidate pool with the better, newer architectures. This procedure is repeated multiple times and thus the candidate pool is refined over time. Mutations in the context of evolving ANNs are operations such as adding or removing a layer, which include changing the type of a layer (e.g., from convolution to pooling), changing the hyperparameters of a layer, or changing the training hyperparameters. On [[CIFAR-10]] and [[ImageNet]], evolution and RL performed comparably, while both slightly outperformed [[random search]].<ref name="Real 2018" /><ref name=":0" />

== Bayesian optimization ==

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{{Differentiable computing}}

[[Category:Artificial intelligence engineering]]

[[Category:Artificial intelligence]]