The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Reinforcement learning for neural architecture search: A review
Open accessAbstract
Abstract Deep neural networks are efficient and flexible models that perform well for a variety of tasks such as image, speech recognition and natural language understanding. In particular, convolutional neural networks (CNN) generate a keen interest among researchers in computer vision and more specifically in classification tasks. CNN architecture and related hyperparameters are generally correlated to the nature of the processed task as the network extracts complex and relevant characteristics allowing the optimal convergence. Designing such architectures requires significant human expertise, substantial computation time and does not always lead to the optimal network. Reinforcement learning (RL) has been extensively used in automating CNN models design generating notable advances and interesting results in the field. This work aims at reviewing and discussing the recent progress of RL methods in Neural Architecture Search task and the current challenges that still require further consideration.
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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215
Neural Architecture Search with Reinforcement Learning
Zoph, B., & Le, Q. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01578 · Cited by 3,974
Cited by
Neural Architecture Search for Transformers: A Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3212767 · Cited by 118
Neural Architecture Search Benchmarks: Insights and Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · IEEE Access · doi:10.1109/access.2023.3253818 · Cited by 64
LLMatic: Neural Architecture Search Via Large Language Models And Quality Diversity Optimization
Nasir, M. U., Earle, S., Togelius, J., James, S., & Cleghorn, C. (2024) · Proceedings of the Genetic and Evolutionary Computation Conference · doi:10.1145/3638529.3654017 · Cited by 52
Metadata
- Authors
- Jaafra, Y., Luc Laurent, J., Deruyver, A., & Saber Naceur, M.
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 89
- Pages
- 57-66
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W2959334635
- Cited by
- 183
- References
- 14
- FWCI
- 7.60
- Type
- journal article
- Language
- en
- Open access
- bronze
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