Mathematics, Physics & Machine Learning Seminar   RSS

Next session

Gunnar Carlsson 30/09/2020, 18:00 — 19:00 Europe/Lisbon — Instituto Superior Técnico
, Stanford University

Topological Data Analysis and Deep Learning

Deep Learning is a powerful collection of techniques for statistical learning, which has shown dramatic applications in many different directions, including including the study of data sets of images, text, and time series. It uses neural networks, specifically convolutional neural networks (CNN's), to produce these results. What we have observed recently is that methods of topology can contribute to this effort, in diagnosing behavior within the CNN's, in the design of neural networks with excellent computational properties, and in improving generalization, i.e. the transfer of results of one neural network from one data set to another of similar type. We'll discuss topological methods in data science, as well as there application to this interesting set of techniques.

The IST seminar series Mathematics, Physics & Machine Learning aims at bringing together mathematicians and physicists interested in machine learning (ML) with  ML and AI experts interested in mathematics and physics, with the goals of introducing innovative mathematics and physics-inspired techniques in ML and, reciprocally, applying ML to problems in mathematics and physics.

Organizers: Cláudia Nunes (DM and CEMAT), Cláudia Soares (DEEC and ISR), Francisco Melo (DEI and INESC-ID), João Seixas (DF and CEFEMA), João Xavier (DEEC and ISR), José Mourão (DM and CAMGSD), Mário Figueiredo (DEEC and IT), Pedro Alexandre Santos (DM and INESC-ID)  and Yasser Omar (DM and IT).
Zoom password: distributed with email announcements or send an email to the organizers asking for it.