Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning
Deep learning systems tend to be very sensitive to the specific network architecture both in terms of learning ability and performance of the learned solution. This, together with the difficulty of tuning neural network architectures leads to a need for automatic network optimization. Previous work...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133380 |
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| author | Raghav Vadhera Manfred Huber |
| author_facet | Raghav Vadhera Manfred Huber |
| author_sort | Raghav Vadhera |
| collection | DOAJ |
| description | Deep learning systems tend to be very sensitive to the specific network architecture both in terms of learning ability and performance of the learned solution. This, together with the difficulty of tuning neural network architectures leads to a need for automatic network optimization. Previous work largely optimizes a network for one specific problem using architecture search, requiring significant amounts of time training different architectures during optimization. To address this and to open up the potential for transfer across tasks, this paper presents a novel approach that uses Reinforcement Learning to learn a policy for network optimization in a derived architecture embedding space that incrementally optimizes the network for the given problem. By utilizing policy learning and an abstract problem embedding, this approach brings the promise of transfer of the policy across problems and thus the potential optimization of networks for new problems without the need for excessive additional training. For an initial evaluation of the base capabilities, experiments for a standard classification problem are performed in this paper, showing the ability of the approach to optimize the architecture for a specific problem within a given rang of fully connected networks, and indicating its potential for learning effective policies to automatically improve network architectures. |
| format | Article |
| id | doaj-art-ab1d101e29734ddca8e91635ca4a1fe9 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-ab1d101e29734ddca8e91635ca4a1fe92025-08-20T03:07:44ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13338069686Learning Policies for Neural Network Architecture Optimization Using Reinforcement LearningRaghav Vadhera0Manfred Huber1https://orcid.org/0009-0007-0294-9147RAYTHEONUniversity of Texas at ArlingtonDeep learning systems tend to be very sensitive to the specific network architecture both in terms of learning ability and performance of the learned solution. This, together with the difficulty of tuning neural network architectures leads to a need for automatic network optimization. Previous work largely optimizes a network for one specific problem using architecture search, requiring significant amounts of time training different architectures during optimization. To address this and to open up the potential for transfer across tasks, this paper presents a novel approach that uses Reinforcement Learning to learn a policy for network optimization in a derived architecture embedding space that incrementally optimizes the network for the given problem. By utilizing policy learning and an abstract problem embedding, this approach brings the promise of transfer of the policy across problems and thus the potential optimization of networks for new problems without the need for excessive additional training. For an initial evaluation of the base capabilities, experiments for a standard classification problem are performed in this paper, showing the ability of the approach to optimize the architecture for a specific problem within a given rang of fully connected networks, and indicating its potential for learning effective policies to automatically improve network architectures.https://journals.flvc.org/FLAIRS/article/view/133380 |
| spellingShingle | Raghav Vadhera Manfred Huber Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning |
| title_full | Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning |
| title_fullStr | Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning |
| title_full_unstemmed | Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning |
| title_short | Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning |
| title_sort | learning policies for neural network architecture optimization using reinforcement learning |
| url | https://journals.flvc.org/FLAIRS/article/view/133380 |
| work_keys_str_mv | AT raghavvadhera learningpoliciesforneuralnetworkarchitectureoptimizationusingreinforcementlearning AT manfredhuber learningpoliciesforneuralnetworkarchitectureoptimizationusingreinforcementlearning |