Events Calendar

PhD Thesis Defense - Iordanis Fostiropoulos

PhD Thesis Defense - Iordanis Fostiropoulos
 
Committee Members: Laurent Itti, Mohammad Soleymani, Stefanos Nikolaidis, Nicolas Schweighofer
 
Title: Towards Efficient Task Generalization
 
Abstract: Current practices in Machine Learning (ML) require a model to be trained iteratively on novel examples and tasks. The same model generalizes poorly on previously learned data, where we empirically observe 'Catastrophic Forgetting'. Generalizing across tasks can be trivially solved when there is no restriction on the computational resources. We find that current state-of-the-art fails catastrophically to perform robustly when presented with a large sequence of tasks with large domain gaps. Additionally, simpler methods have improved generalization compared to state-of-the-art methods. While current methods suffer in computational performance. In this talk, we present our work that introduces a framework for efficiently learning a large sequence of tasks by utilizing several experts under strict computational constraints. Last, we discuss future improvements of our method and industrial applications, for example, to self-driving cars

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 110

Audiences: Everyone Is Invited

Contact: Melissa Ochoa