Neural Architecture Search with Reinforcement Learning
Citations
310 citations
303 citations
Cites background from "Neural Architecture Search with Rei..."
...Neural Architecture Search: Recent research works in neural architecture search (NAS) are powerful on finding high-accuracy neural network architectures [42, 24, 43, 19, 34] as well as hardware-aware efficient network architectures [2, 27, 30]....
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300 citations
Cites background or methods from "Neural Architecture Search with Rei..."
...In Section 2.3, the search gradient of SNAS is connected to the policy gradient in reinforcement-learning-based NAS (Zoph & Le, 2016; Pham et al., 2018), interpreting SNAS’s credit assignment with contribution analysis....
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...The trend to seek for state-of-the-art neural network architecture automatically has been growing since Zoph & Le (2016), given the enormous effort needed in scientific research....
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...Following the setting in Zoph & Le (2016), the objective of SNAS is also EZ∼pα(Z)[R(Z)]....
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293 citations
291 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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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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