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  • CS Colloquium: Xuezhe Ma (USC ISI) - Towards Structured-Infused and Disentangled Representation Learning

    Tue, Nov 10, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Xuezhe Ma, USC

    Talk Title: Towards Structured-Infused and Disentangled Representation Learning

    Abstract: One of the keys to the empirical successes of deep neural networks in many domains, such as natural language processing and computer vision, is their ability to automatically extract salient features for downstream tasks via the end-to-end learning paradigm.
    In this talk, I will present two of our recent work. First, I will introduce how to encode structured dependencies into learned representations to achieve efficient non-autoregressive machine translation models. Second, I will present our work on learning representations to decouple global and local information from/for image generation. I will conclude by laying out future research directions towards interpretable and controllable representation learning.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Join Zoom Meeting

    Meeting ID: 917 4361 3540
    Passcode: 296867

    Biography: Xuezhe Ma joined ISI as a computer scientist in Fall 2020.
    Xuezhe received his PhD degree in Language Technologies Institute at Carnegie Mellon University, advised by Eduard Hovy.
    Before that, he received his B.E and M.S from Shanghai Jiao Tong University. His research interests fall in areas of natural language processing and machine learning, particularly in deep learning and representation learning with applications to linguistic structured prediction and deep generative models. Xuezhe has interned at Allen Institute for Artificial Intelligence (AI2) and earned the AI2 Outstanding Intern award. His research has been recognized with outstanding paper award at ACL 2016 and best demo paper nomination at ACL 2019.

    Host: Xiang Ren

    Audiences: Everyone Is Invited

    Posted By: Cherie Carter


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