Venue: TEO-H1 1.820-AUD1
Monday January 8th:
10-13 Tutorial: Innovative Uses of Synthetic Data Tutorial (Mihaela van der Schaar)
One of the biggest barriers to AI adoption is the difficulty to access high quality training data. Synthetic data has been widely recognised as a viable solution to this problem. It allows sharing, augmenting and de-biasing data for building performant and socially responsible AI algorithms. However, despite the significant progress in the theory and algorithm, the community still lacks a unified software that enables practical data sharing and access with synthetic data. This lab aims to bridge this gap by introducing synthcity, an open source Python library that implements an array of cutting edge synthetic data generators to address the problems of data generation due to its commonality in various applications.
14-16 Tutorial: Generative Modeling with Variational Autoencoders (Rogelio A. Mancisidor)
Variational Autoencoders (VAEs) are probabilistic generative models used in different learning tasks, such as clustering, classification, representation learning or learning conditional distributions to generate new data. This tutorial presents different ways to derive the evidence lower bound (ELBO) to gain a deeper understanding of the advantages (e.g., representation learning and generative modeling in a single model) and disadvantages (e.g., posterior collapse or lack of flexibility in the latent space) of optimizing the ELBO. In addition, we will discuss how we can extend VAEs to the multimodality domain to learn conditional distributions, which enables cross-modal generation. By the end of this tutorial, you will have a good understanding of the main advantages of VAEs and why VAEs are a popular choice of generative models, as well as insight into some of the challenges posed by the probabilistic aspect of VAEs.
16:30-18:00 Poster Presentations
January 9-11th: Main Conference Program
Friday January 12th:
09-11 Tutorial: Coding a Diffusion model from scratch (Filippo Maria Bianchi)
Renowned for their exceptional capabilities in synthesizing realistic images, audio, and video, DDPMs have marked a new era in generative modeling. In this introductory tutorial, we will dive into the world of Denoising Diffusion Probabilistic Models (DDPM) by unraveling the theory behind these groundbreaking generative models. In particular, we will closely look at the vanilla DDPM by implementing it from scratch using Pytorch and then using it to generate images from the popular Fashion MNIST dataset. At the end of the tutorial, you will have gained a robust understanding of DDPMs and also acquired the practical skills to build and apply these models to real data.
12-14 Tutorial: Accelerated Deep Learning via High-performance Computing (Sigma2/NRIS)
Link to Material: https://training.pages.sigma2.no/tutorials/hpc-intro/
Speakers: Sabry Razick and Hicham Agueny
Part 1: Introduction
Part 2: Accelerated Deep Learning on the Supercomputer LUMI-G
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM). Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.
Rogelio A. Mancisidor received his B.Sc. and M.Sc. in Finance from BI Norwegian Business School and his Ph.D. in Machine Learning from UiT The Arctic University of Norway in 2021. During his Ph.D., Rogelio developed different unsupervised, supervised and semi-supervised models using variational autoencoders. He is currently an assistant professor in the Department of Data Science and Analytics at BI Norwegian Business School, and his current research interests include deep generative models, Bayesian modeling, variational inference, multimodal learning and text analytics.
Filippo Maria Bianchi is an Associate Professor at the Department of Mathematics and Statistics at UiT the Arctic University of Norway and holds a senior researcher position at NORCE, the Norwegian Research Centre. He serves as the vice-chair of the IEEE Task Force on Learning for Structured Data and is an active member of multiple groups, including the Graph Machine Learning Group in Lugano, the Northernmost Graph Machine Learning group, and the IEEE Task Force on Reservoir Computing. His research is centered at the intersection of machine learning, dynamical systems, and complex networks, with a particular focus on the application of his expertise in the fields of energy analytics and remote sensing.
Winter School Chair: Puneet Sharma (email@example.com)
For other inquiries: Robert Jenssen (firstname.lastname@example.org) and Michael Kampffmeyer (email@example.com).