arXiv:2108.08474
Trends in Neural Architecture Search: Towards the Acceleration of Search
원문 초록
In modern deep learning research, finding optimal (or near optimal) neural network models is one of major research directions and it is widely studied in many applications. In this paper, the main research trends of neural architecture search (NAS) are classified as neuro-evolutionary algorithms, reinforcement learning based algorithms, and one-shot architecture search approaches. Furthermore, each research trend is introduced and finally all the major three trends are compared. Lastly, the future research directions of NAS research trends are discussed.
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Improving the Efficient Neural Architecture Search via Rewarding Modifications
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
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OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
토론
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