Aims and Objectives
Most of the patterns we see in nature can be elegantly reasoned about using spatial symmetries—transformations that leave underlying objects unchanged. This observation has had direct implications for the development of modern deep learning architectures that are seemingly able to escape the “curse of dimensionality” and fit complex high-dimensional tasks from noisy real-world data. Beyond this, one of the most generic symmetries—the permutation—will prove remarkably powerful in building models that reason over graph structured-data, which is an excellent abstraction to reason about naturally-occurring, irregularly-structured data. Prominent examples include molecules (represented as graphs of atoms and bonds, with three-dimensional coordinates provided), social networks and transportation networks. Several already-impacted application areas include traffic forecasting, drug discovery, social network analysis and recommender systems.
The module will provide the students with the capability to analyse irregularly- and non-trivially-structured data in an effective way, and position geometric deep learning in a proper context with related fields. The main aim of the course is to enable students to make direct contributions to the field, thoroughly assimilate the key concepts in the area, and draw relevant connections to various other fields (such as NLP, Fourier Analysis and Probabilistic Graphical Models). We assume only a basic background in machine learning with deep neural networks.
This module is taught by the Department of Computer Science and Technology.