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Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning.
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B. Baker, O. Gupta, R. Raskar, and N. Naik · 2017
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SMASH: one-shot model architecture search through hypernetworks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2017
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C. Cortes, X. Gonzalvo, V. Kuznetsov, M. Mohri, and S. Yang · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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C. Finn and S. Levine · 2017
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
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Prototypical networks for few-shot learning
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Progressive neural architecture search
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Hierarchical representations for efficient architecture search
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A simple neural attentive meta-learner
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On first-order meta-learning algorithms
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Regularized evolution for image classifier architecture search
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Meta-learning with latent embedding optimization
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