Neural Architecture Search with Reinforcement Learning
Citations
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Cites background from "Neural Architecture Search with Rei..."
...The network architectures automatically searched in [11, 12, 13] have achieved highly competitive performance in computer vision tasks, such as image classification and object detection....
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3 citations
Cites methods from "Neural Architecture Search with Rei..."
...The resulting trained models achieved 2.60± .13% error on the CIFAR-10 validation dataset....
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...We use RAPDARTS to identify a neural architecture achieving 2.68% test error on CIFAR-10....
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...A reinforcement learning-based (RL) approach was the first postAlexNet NAS method with state-of-the-art performance on CIFAR-10 [7], [8]....
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...In an effort to simulate a real-world constraint, we restrict ourselves such that discovered CIFAR-10 architectures must have less than 3× 106 parameters....
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...We use RAPDARTS to search for CIFAR-10 neural architectures....
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3 citations
3 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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