Learning to learn and compositionality with deep recurrent neural networks

author: Nando de Freitas, Department of Computer Science, University of Oxford
published: Aug. 31, 2016,   recorded: August 2016,   views: 7097

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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.

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