Neural Architecture Search with Reinforcement Learning
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Cites background or methods from "Neural Architecture Search with Rei..."
...Recently, RL has emerged as a powerful and general approach in various domains from complex games [5, 41, 45, 47] to simulated robotics tasks [2, 18, 37], all the way to neural architecture search [7, 35, 58]....
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...To the best of our knowledge, this is the first work that performs generalizable and transferable channel pruning with (either a random search or) a RL-based approach, even though there are some prior works that train RL-based policies for pruning a particular network [3, 6, 14,58]....
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References
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"Neural Architecture Search with Rei..." refers methods in this paper
...Along with this success is a paradigm shift from feature designing to architecture designing, i.e., from SIFT (Lowe, 1999), and HOG (Dalal & Triggs, 2005), to AlexNet (Krizhevsky et al., 2012), VGGNet (Simonyan & Zisserman, 2014), GoogleNet (Szegedy et al., 2015), and ResNet (He et al., 2016a)....
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"Neural Architecture Search with Rei..." refers methods in this paper
...Along with this success is a paradigm shift from feature designing to architecture designing, i.e., from SIFT (Lowe, 1999), and HOG (Dalal & Triggs, 2005), to AlexNet (Krizhevsky et al., 2012), VGGNet (Simonyan & Zisserman, 2014), GoogleNet (Szegedy et al., 2015), and ResNet (He et al., 2016a)....
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