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Learning to learn and compositionality with deep recurrent neural networks

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Published on Sep 1, 2016

Author:
Nando de Freitas, Department of Computer Science, University of Oxford

Abstract:
Deep neural network representations play an important role in computer vision, speech, computational linguistics, robotics, reinforcement learning and many other data-rich domains. In this talk I will show that learning-to-learn and compositionality are key ingredients for dealing with knowledge transfer so as to solve a wide range of tasks, for dealing with small-data regimes, and for continual learning. I will demonstrate this with three examples: learning learning algorithms, neural programmers and interpreters, and learning communication.

More on http://www.kdd.org/kdd2016/

KDD2016 Conference is published on http://videolectures.net/

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