Neural Architecture Search with Reinforcement Learning
Citations
7 citations
Cites background from "Neural Architecture Search with Rei..."
...NAS is known to be computation-intensive (e.g., thousands of GPU-hrs [35]), given the large number of model candidates to be explored....
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..., thousands of GPU-hrs [35]), given the large number of model candidates to be explored....
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7 citations
Cites background from "Neural Architecture Search with Rei..."
...Similar to hyper parameters, the best architecture for a specific problem is not known a priori, and hence architectural search techniques are actively investigated [7][8]....
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7 citations
Cites methods from "Neural Architecture Search with Rei..."
...There are some exceptions, however: Methods such as NEAT [67] use evolutionary methods to allow network architectures to adapt in response to a problem, neural architecture search [78] uses reinforcement learning to learn a probabilistic policy for constructing new architectures, and adaptive neural trees [70] recursively and dynamically generate a neural network architecture on the fly as “they” learn....
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...…and Miikkulainen, 2002) use evolutionary methods to allow network architectures to adapt in response to a problem, neural architecture search (Zoph and Le, 2016) uses reinforcement learning to learn a probabilistic policy for constructing new architectures, and adaptive neural trees (Tanno…...
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7 citations
7 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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