Neural Architecture Search with Reinforcement Learning
Citations
17 citations
Cites methods from "Neural Architecture Search with Rei..."
...RL method [37] trained over 10,000 of neural architectures, requiring thousands of GPU days and another very efficient evolutionary search [13] still takes 56 GPU days to converge....
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...In RL methods [4,26,37], the reward of the RL agent is dependent on the validation performance of the trained architecture....
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...Popular approaches for Neural Architecture Search (NAS) use reinforcement learning [26,37] and evolutionary algorithms [23,27]....
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17 citations
17 citations
17 citations
Additional excerpts
...In terms of the NAS methods, reinforcement learning [9, 21, 22], sequential optimization [10, 23], evolutionary algorithms [24, 25, 26], random search [27, 28], and performance predictors [29, 30] tend to demand immense computational resources which probably not suitable for efficient search....
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...Recent years have witnessed the unprecedented success of neural architecture search (NAS) in the automated design of neural network architectures, surpassing human designs on various tasks [6, 7, 8, 9, 10, 11, 12]....
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17 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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