Neural Architecture Search with Reinforcement Learning
Citations
12 citations
12 citations
12 citations
Cites background from "Neural Architecture Search with Rei..."
...The traditional approach of training each architecture for a large number of epochs and evaluating it on validation data (full evaluation) provides a reliable performance measure, but requires prohibitively high computational resources on the order of thousands of GPU days [2, 3, 4, 5, 1]....
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12 citations
Cites background from "Neural Architecture Search with Rei..."
...Starting from [187], recent works adopt neural architecture search (NAS) to explore the design space automatically....
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...Many previous works [187, 188, 141] use reinforcement learning (RL) to guide the search and a typical flow is illustrated in Figure 8....
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...Neural Architecture Search: [187, 188] first propose to use reinforcement learning (RL) to search for neural architectures to achieve competitive accuracy with low FLOPs....
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...Most of the previous works [187, 188, 110, 92, 93] search for cell level architectures....
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...Previous works [187, 188, 141] have demonstrated the effectiveness of such methods in finding accurate and efficient networks....
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12 citations
Cites background from "Neural Architecture Search with Rei..."
...But in fact, the Minor LSTM is essentially different from existing shortcut connections [27], [30], [31], [34], [35]....
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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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42,067 citations
31,952 citations
"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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