Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm

ICLR 2018 Chelsea FinnSergey Levine

Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs... (read more)

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