Pacific Institute for the Mathematical Sciences - PIMS

Transfer Learning for Machine Learning with Applications

Abstract

Machine Learning including deep learning techniques have been successfully used in many big-data fields. However, a limitation of many machine learning tools is that one needs to have a very large sample size to train a model with many parameters. This may prevent the broader use of machine learning in sample-sparse domains. For instance, in medical genetics, the number of patients of a particular disease available for a research project may be at the level of hundreds or even dozens, which is way lower than the requirement of many machine learning techniques that are sample-hungry. Towards this line, researchers have developed a technique called “transfer-learning”, which can re-task an established general model (that are usually trained by very large sample) to a specific target using tailored samples of limited size. Such transfer-learning models open the door of developing many tools tailored to specific tasks using small samples with nimble training procedure. In this talk, I will first explain the basic theory of transfer-learning, followed by an introduction of its use in computer science including natural language process. I will also present a project using transfer-learning to characterize genetic basis of complex diseases by retasking a large general model.

Biography

Quan Long is an Associate Professor at University of Calgary, hosted by the Dept. of Biochemistry and Molecular Biology. Additionally, with a joint appointment in the Dept. of Medical Genetics and an adjunct appointment in the Dept. of Math and Stat. He is a member of Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute. Quan Long graduated from Peking University with a PhD in Applied Mathematics (majoring in software engineering). After graduation he worked in IBM Research as a Staff R&D Engineer for a year on reparation of programming errors leading to memory leak. Then he entered the wonderful biological world, starting by serving for path-finding genomics projects including human 1000 Genomes Project at the Wellcome Trust Sanger Institute, plant 1001 Genomes Project at the Gregor Mendel Institute, and Genotype-Tissue Expression Project at Icahn School of Medicine at Mount Sinai. During the course, he has developed various computational and statistical tools for genetics and genomics. Currently he is leading a research group to develop computational and statistical tools, focusing on genomic problems with high-dimensional features and low sample sizes. He is also interested in theoretical problems in machine learning. Quan Long's works have been published in leading journals, attracting 30,000+ citations. His group is funded by federal agencies including NSERC, CIHR, and NFRF.

Thursday October 20th, 2022
12:00 - 1:00 pm
Room 416, 906- 8 Ave SW
University of Calgary- Downtown