Learning to learn and compositionality with deep recurrent neural networks




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Published on Sep 1, 2016

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

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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