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Deep Transfer Learning and Multi-task Learning + Hands-On Workshop

This is diiP's second seminar delivered by Dr. Shen Liang, one of diiP's research associate. More information and material is available on our website: https://u-paris.fr/diip/diip-seminars/ abstract: This tutorial provides an overview of two important and correlated (in many cases intersectional) topics in deep learning: transfer learning, and multi-task learning. Transfer learning focuses on transferring knowledge learned in one problem to a different yet related problem, while multi-task learning attempts to solve multiple tasks in one stroke by exploiting the commonalities across the multiple tasks. In this tutorial, I will present an overview of the motivations, methodologies, as well as applications of these two learning paradigms in fields such as natural language processing and computer vision. I will also provide two concrete examples of these paradigms in a hands-on workshop. short bio: Shen Liang is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université Paris Cité. He has worked on a variety of data management and mining problems including time series analysis, semi-supervised learning, knowledge-guided deep learning and GPU-accelerated computation within various fields such as healthcare, manufacturing, geosciences and astrophysics. He holds a PhD in software engineering from Fudan University, China.