A graph-attention RL agent with a binary channel-level action space and a self-competition reward prunes CNNs at fixed FLOPs budgets, giving competitive but not uniformly state-of-the-art accuracy.
Automated machine learning: past, present and future,
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Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning
A graph-attention RL agent with a binary channel-level action space and a self-competition reward prunes CNNs at fixed FLOPs budgets, giving competitive but not uniformly state-of-the-art accuracy.