Neural Architecture Search with Reinforcement Learning
Citations
6 citations
6 citations
Cites background or methods from "Neural Architecture Search with Rei..."
...it is a layer-level recurrence, not a hyperparameter-level, as in [14]....
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...In this paper, inspired by relevant recent research, which uses reinforcement learning in order to produce neural network architectures [2] [14], we implement a Synchronous Advantage Actor-Critic algorithm (A2C) [11], in order to train a Gated Recurrent Unit to design neural architectures....
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...Our research was inspired by both of these studies, although it is more related to [14], as we train a recurrent network in order to design the architectures....
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...The first, produced by Google researchers [14] utilizes a recursive neural network in order to generate text-based descriptions of the neural architecture, while using REINFORCE [13] in order to learn the best policy....
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6 citations
6 citations
6 citations
Cites methods from "Neural Architecture Search with Rei..."
...[8] follows the same framework as proposed in [7], but instead of using a RNN to predict the entire network architecture, the algorithm only predicts the optimal structure for one convolutional module (or “cell”)....
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...An alternative approach is to use automated neural architecture search (NAS) algorithms to find optimal models under hardware constraints [7]–[9]....
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...Early versions of NAS algorithms [7] employed recurrent neural networks (RNNs) to predict the architecture of a target CNN where the weights of the RNN are updated using reinforcement learning....
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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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