Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning

1 Jan 2021 Anonymous

Sampling-based neural architecture search (NAS) always guarantees better convergence yet suffers from huge computational resources compared with gradient-based approaches, due to the rollout bottleneck -- exhaustive training for each sampled generation on proxy tasks. This work provides a general pipeline to accelerate the convergence of the rollout process as well as the RL learning process in sampling-based NAS... (read more)

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Methods used in the Paper


METHOD TYPE
Entropy Regularization
Regularization
PPO
Policy Gradient Methods