On June 25th, 2020 from 9.00 am to 1.00 pm, the “Machine Learning And Deep Learning Models For Handling Graphs” seminar will take place online, within the PhD Course on Machine Learning for Non-Matrix Data, organized by profs. Giacomo Boracchi, Cesare Alippi, Matteo Matteucci.

Deep learning methods have achieved unprecedented performance in computer vision, natural language processing and speech analysis, enabling many industry-first applications. Autonomous driving, image synthesis and deep reinforcement learning are just few examples of what is now possible on grid structured data with deep learning at scale on GPUs and dedicated hardware.

However, tasks for which data comes arranged on grids and sequences cover only a small fraction of the fundamental problems of interest. Most of the interesting problems have, in fact, to deal with data that lie on non-Euclidean domains for which deep learning methods were not originally designed. The need to operate powerful non-linear data driven models on this data led to the creation of Geometric Deep Learning, a new and rapidly growing area of research that focuses on methods and applications for graph and manifold structured data.

Despite the field being still in its infancy, it can already list numerous breakthroughs on classic graph theory problems such as graph matching, 3D shape analysis and registration, fMRI and structural connectivity networks, scene reconstruction and parsing, drug design and protein synthesis. At the core of this new wave of deep learning successes is the ability of models to directly deal with non-Euclidean data through generalization of convolution and sub sampling operators and, more generally, thanks to models that can use structure to induce computation in what I call the Structured Computation Model.

The lecture will start with a pragmatic introduction to graph convolutions, both in the spectral and spatial domain, and to the message passing framework. Applications and recent achievements in the field will then follow, starting with node and graph classification in the inductive and transductive setting, and progressing to finally realize that popular methods in meta-learning, one-shot and few-shot learning, structured latent space models are particular cases of the Structured Computational Model. I will show how such a general and unified framework can help the cross-fertilization of different disciplines to achieve better results, faster.

The lecture will finally give an outlook of where the field is going and of new and exiting research directions and industrial applications that are waiting to be revolutionized.

This event will be online and organized via Microsoft Teams.

In order to attend the seminar, please, go to the following link: Join Microsoft Teams Meeting.