Workshop proposal: Deep Learning in Computational Cognitive Science

Abstract

A new generation of deep neural network architectures has driven rapid advances in AI over the last ten years. These architectures include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and many variants and extensions. Computational cognitive scientists and neuroscientists have now begun to explore these techniques, and how they might combine with other computational tools such as Bayesian models, symbolic grammars and rule-systems, probabilistic programs, and reinforcement learning. The goal of this workshop is to bring together some of the leading researchers working at this interface, for short talks and an integrative discussion of open questions and promising directions.


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