Neural Architecture Search with Reinforcement Learning
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Cites methods from "Neural Architecture Search with Rei..."
...Reinforcement learning for automatic tuning: RL-based methods have attracted much attention within neural architecture search (NAS) after obtaining the competitive performance on the CIFAR-10 dataset employing RL as the search strategy.(9) Different RL approaches differ in how they represent the agent’s policy....
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20 citations
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Cites methods from "Neural Architecture Search with Rei..."
...109] or dense prediction tasks [17, 75]. We brie y summarize related work here and refer to the survey by Elsken et al. [31] for a more thorough literature overview. Reinforcement learning techniques [5, 108, 107, 109] or evolutionary methods [87, 64, 73, 74] were employed to search for well performing architectures. As early work required vast amount of computational resources, often in the range of hundreds or ev...
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20 citations
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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