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Events for December 01, 2022

  • NL Seminar -Prioritized training on points that are learnable, worth learning, and not yet learned

    Thu, Dec 01, 2022 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Sören Mindermann & Jan Brauner, University of Oxford

    Talk Title: Prioritized training on points that are learnable, worth learning, and not yet learned

    Series: NL Seminar

    Abstract: REMINDER
    Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.

    If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.

    In person attendance will be permitted for USC ISI faculty, staff, students only. Open to the public virtually via the zoom link and online.

    Training on web scale data can take months. But much computation and time is wasted on redundant and noisy points that are already learnt or not learnable. To accelerate training, we introduce Reducible Holdout Loss Selection RHO LOSS , a simple but principled technique which selects approximately those points for training that most reduce the models generalization loss.

    As a result, RHO LOSS mitigates the weaknesses of existing data selection methods techniques from the optimization literature typically select hard eg high loss points, but such points are often noisy not learnable or less task relevant. Conversely, curriculum learning prioritizes easy points, but such points need not be trained on once learned. In contrast, RHO LOSS selects points that are learnable, worth learning, and not yet learnt. RHO LOSS trains in far fewer steps than prior art, improves accuracy, and speeds up training on a wide range of datasets, hyperparameters, and architectures MLPs, CNNs, and BERT. On the large web scraped image dataset Clothing 1M, RHO LOSS trains in 18 times fewer steps and reaches 2 percent higher final accuracy than uniform data shuffling.


    Biography: Bio Soren Mindermann
    Soren is a final year PhD student in machine learning at the University of Oxford, supervised by Yarin Gal. My interests in machine learning include how it scales, causal inference and statistical modeling, as well as robustly aligning machine learning models to adopt human wishes and value.

    Bio Jan Brauner
    Jan is a PhD candidate in the Centre for Doctoral Training on Intelligent and Autonomous Machines and Systems AIMS CDT, supervised by Yarin Gal. His current research interests include AI safety and applications of AI in medicine biomedical research.


    Host: Jon May and Meryem M'hamdi

    More Info: https://nlg.isi.edu/nl-seminar/

    Webcast: https://usc.zoom.us/j/95231438820

    Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689

    WebCast Link: https://usc.zoom.us/j/95231438820

    Audiences: Everyone Is Invited

    Contact: Pete Zamar

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  • CEE Seminar Series

    Thu, Dec 01, 2022 @ 02:00 PM - 03:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Karen Daniels PhD, North Carolina State University

    Talk Title: Looking Inside Granular Materials

    Abstract: See attached

    Host: Dr Thomas Petersen

    More Info: https://usc.zoom.us/j/96591420807

    Webcast: https://usc.zoom.us/j/96591420807

    More Information: Daniels_Announcement.docx

    Location: ZOOM

    WebCast Link: https://usc.zoom.us/j/96591420807

    Audiences: Everyone Is Invited

    Contact: Salina Palacios

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  • Wind Down for Finals with KIUEL

    Thu, Dec 01, 2022 @ 05:00 PM - 06:00 PM

    USC Viterbi School of Engineering

    Conferences, Lectures, & Seminars


    Are nerves starting to kick in because of the upcoming finals season? If so, attend KIUEL's Wind Down for Finals and get some advice about how to tackle exams from Viterbi upperclassmen and professors, while enjoying sandwiches and hot chocolate.

    Location: Sign into EngageSC to View Location

    Audiences:

    Contact: Kevin Giang

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