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Speaker:

Dr Lech Szymanski, Department of Computer Science, University of Otago

Title:

Switched linear projections for deep neural network interpretability | Recording

Date:

Friday 3rd April

Modalities:

Zoom streaming (full invitation below)

Abstract:

How to collapse the computation of a deep neural network to a single linear projection?  How to disentangle the distributed representation inside a hidden layer?  What can the switched off neurons tell us about the network's internal representation?  Lately, I have been developing methods capable of answering these questions and in this talk I will present my latest results.

Biography:

My research interests are machine learning, deep representation and connectionist models. Our state of the art computation models can learn from examples, but not without the heavy involvement of an expert user, making critical decisions about system architecture, parameters, and appropriate representation. My ultimate objective is to make machine learning into a tool that is easier to use by an average user. However, in order for that to happen, we need to develop truly autonomous machine learning algorithms, capable of forming an appropriate model for the task at hand, completely on their own.

Zoom Invitation

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