Date: Friday 6th March 2020, 3.30 pm – 4.30 pm in the Large Lecture Theatre, Department of Statistics followed by a Drinks Reception in the ground floor social area
Speaker: Max Welling, Professor of Computer Science, Institute of Informatics, University of Amsterdam
Title: Neural Augmentation
Abstract: Deep learning has boosted the performance of many applications tremendously, such as object classification and detection in images, speech recognition and understanding, machine translation, game play such as chess and go etc. However, these all constitute reasonably narrowly and well defined tasks for which it is relatively easy to collect very large datasets. For artificial general intelligence (AGI) we will need to learn from a small number of samples, generalize to entirely new domains, and reason about a problem. What do we need in order to make progress to AGI? I will argue that we need to combine the data generating process, such as the physics of the domain and the causal relationships between objects, with the tools of deep learning. In this talk I will present a first attempt to integrate the theory of graphical models, which arguably was the dominating modeling machine learning paradigm around the turn of the twenty-first century, with deep learning. Graphical models express the relations between random variables in an interpretable way, while probabilistic inference in such networks can be used to reason about these variables. We will propose a new hybrid paradigm where probabilistic message passing in such networks is augmented with graph convolutional neural networks on factor graphs to improve the ability of such systems to reason and make predictions. We show that this modeling paradigm achieves state of the art on MRI Reconstruction and LDPC decoding.