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In deep learning, performance is strongly affected by the choice of architecture and hyperparameters.
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A survey of Monte Carlo tree search methods
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ImageNet classification with deep convolutional neural networks
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Deep visual-semantic alignments for generating image descriptions
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Selecting near-optimal learners via incremental data allocation
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Going deeper with convolutions
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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I. Sutskever, O. Vinyals, and Q. Le · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Deep residual learning for image recognition
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Mastering the game of Go with deep neural networks and tree search
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Large-scale evolution of image classifiers
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Neural architecture search with reinforcement learning
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