2021 Fiscal Year Annual Research Report
Deep learning with evolutionary synaptic pruning
| Project/Area Number |
21F21768
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| Allocation Type | Single-year Grants |
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| Research Institution | Ochanomizu University |
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Principal Investigator |
オベル加藤 ナタナエル お茶の水女子大学, 基幹研究院, 講師 (10749659)
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| Co-Investigator(Kenkyū-buntansha) |
DA ROLD FEDERICO お茶の水女子大学, 基幹研究院, 外国人特別研究員
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| Project Period (FY) |
2021-11-18 – 2024-03-31
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| Keywords | Evolutionary Strategy / Synaptic Pruning / Quality-Diversity |
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| Outline of Annual Research Achievements |
During the fiscal year, we implemented a framework to reduce the number of parameters in deep learning models through evolutionary optimization. Results were submitted and published at GECCO 2022, a top-tier international conference. The proposed approach combines quality diversity algorithms and pruning operators that achieve a model compression equivalent to state-of-the-art gradient-based pruning techniques. This work thus suggests the validity of gradient-free optimization methods as an alternative to machine learning pruning approaches to reinforcement learning problems.
The technique utilized in the article published in GECCO 2022 was further refined by testing different genetic algorithm operators, such as selection criteria, mutation, and quality discriminators. Furthermore, the algorithm is applied to a larger set of reinforcement learning scenarios.
To further understand the interaction between pruning and parameters optimization, we devised an experiment implementing a population-based genetic algorithm. The initial population is initialized with randomly pruned neural models that successfully learn the task in the experiments described above.
We are attempting to find correlations between information-theoretic measures and sub-network that rapidly converge to a solution to the given task.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The project implementation complies with the research plan with no significant delays for the development of the algorithm, which defined the workflow of the first year of the fellowship. The model selection and ES-GA training was completed within the allotted time and a paper based on the results was submitted to a top international conference.
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| Strategy for Future Research Activity |
The implementation of the framework will continue with the application of existing and novel benchmarks to test the energy efficiency of our method. Applications will focus on a robotic scenario to further assess the performance of our approach to mode compression via synaptic pruning.
The first experiment will be based on a robotic arm performing simple tasks such as reaching and avoiding objects. This scenario aims at porting the model used in the experiments utilized in the first Work Package to an agent with a more complex kinematic. To facilitate the learning process, vision will not be implemented, and the robot will perceive the environment with Cartesian coordinates. The second robotic experiment will use a generative model to extrapolate information from a camera. Furthermore, the experimental setup will include challenging tasks such as grasping and placing objects in a target area.
The expected outcome is a publication in a top-tier international conference and a submission to a highly reputed robotic journal.
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Research Products
(2 results)
