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Evolutionary-Neural Hybrid Agents for Architecture Search
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Neural Architecture Search has shown potential to automate the design of neural networks. Deep Reinforcement Learning based agents can learn complex architectural patterns, as well as explore a vast and compositional search space. On the other hand, evolutionary algorithms offer higher sample efficiency, which is critical for such a resource intensive application. In order to capture the best of both worlds, we propose a class of Evolutionary-Neural hybrid agents (Evo-NAS). We show that the Evo-NAS agent outperforms both neural and evolutionary agents when applied to architecture search for a suite of text and image classification benchmarks. On a high-complexity architecture search space for image classification, the Evo-NAS agent surpasses the accuracy achieved by commonly used agents with only 1/3 of the search cost.
Forward citations
Cited by 2 Pith papers
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Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research
Reinforcement-learning-based neural architecture search finds smaller and faster neural networks with accuracy comparable to, or better than, manually designed networks on three cancer drug-response benchmarks.
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AutoML: A Survey of the State-of-the-Art
A survey that organizes AutoML into a four-stage pipeline and reviews neural architecture search methods, their performance, and open problems.
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