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INI Colloquium: Talk by Dr. Jörg Bornschein (Deep Mind London)

Title: "Memory Augmented Generative Models for Few-shot Learning"
Time: Wednesday, October 18th, 12pm
Venue: NB 3/57
Over the last couple of years there has been significant progress in building better generative models that learn to generate complex and high dimensional data like images or sound. Much of this progress has been driven by advances in deep learning and by applying variational inference techniques. One of the most important algorithms in deep learning is stochastic gradient descent and its variants: slowly adapting the models parameters one mini-batch at a time. But we sometimes face situations where we would like to rapidly adapt our models based on only very few training examples. Here I will talk about recent approaches to augment variational autoencoder based models with memory subsystems, how they add few-shot learning capabilities to these models, and how to generate new samples based on very few training examples.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science.

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