Neural Architecture Search with Reinforcement Learning
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52 citations
Cites background from "Neural Architecture Search with Rei..."
...Another category of strategies is to further optimize a DLM with the original training dataset by, for example, changing parameters progressively [9][25][58], exploring different weight assignments [1][7][17][19][20][34][52][56], producing variants of the DNN underlying the DLM [2][60], or a mix of them [40] to generate evolved DLMs before training them....
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51 citations
Cites background from "Neural Architecture Search with Rei..."
...Moreover, they can be seen as a form of Neural Architecture Search (Zoph & Le, 2016) to find more efficient network topologies....
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50 citations
50 citations
Cites background from "Neural Architecture Search with Rei..."
...Works in the field are usually divided into two categories: Reinforcement Learning (RL) based approaches (see e.g., Zoph & Le (2016); Baker et al. (2016); Zoph et al. (2017); Zhong et al. (2017)) and Genetic Algorithm (GA) based approaches (see e.g., Real et al. (2017); Xie & Yuille (2017); Liu et…...
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50 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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