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  • Ομιλία Καθηγητή Δ. Παπαηλιόπουλου (Univ. of Wisconsin-Madison): Τρίτη 31/7, 12:00 στο Αμφιθέατρο του Κτ. Επιστημών

Ομιλία Καθηγητή Δ. Παπαηλιόπουλου (Univ. of Wisconsin-Madison): Τρίτη 31/7, 12:00 στο Αμφιθέατρο του Κτ. Επιστημών

  • Συντάχθηκε 27-07-2018 10:02 Πληροφορίες σύνταξης

    Ενημερώθηκε: -

    Ημερομηνία: Τρίτη 31/7/2018
    Ώρα: 12:00
    Αίθουσα: Αμφιθέατρο του Κτ. Επιστημών
    
    Title: Overcoming the Challenges of Distributed Learning
    
    
    Speaker:  Prof. Dimitris Papailiopoulos
              University of Wisconsin-Madison
              http://papail.io/
    
    
    Abstract:
    
    In this talk, I will highlight a few key challenges that limit our
    capacity to effectively deploy machine learning solutions in real
    distributed systems. I will first focus on communication bottlenecks
    during model training, and discuss how they lead to poor speedup gains
    when scaling out to hundreds of compute nodes.  I will present theoretical
    insights which suggest that we can only overcome these challenges either
    by building new classes of neural networks, or by designing novel training
    algorithms that require far less communication. We will then focus on
    issues of robustness and discuss how model training is susceptible to
    hardware failures and adversarial attacks. I will explain how simple
    algebraic ideas, borrowed from coding theory, can be used to enable robust
    distributed training. I will conclude with several open problems that lie
    in the intersection of machine learning, optimization, and distributed
    systems.
    
    
    Short CV:
    
    Dimitris Papailiopoulos is an Assistant Professor of Electrical and
    Computer Engineering and Computer Sciences (by courtesy) at the University
    of Wisconsin-Madison, a faculty fellow of the Grainger Institute for
    Engineering, and a faculty affiliate at the Wisconsin Institute for
    Discovery. Between 2014 and 2016, Dimitris was a postdoctoral researcher
    at UC Berkeley and a member of the AMPLab. His research interests span
    machine learning, information theory, and distributed systems, with a
    current focus on communication-avoiding training algorithms and
    coding-theoretic techniques for robust large-scale machine learning.
    Dimitris earned his Ph.D. in ECE from UT Austin in 2014, under the
    supervision of Alex Dimakis. In 2007, he received his ECE Diploma and in
    2009 his M.Sc. degree from the Technical University of Crete, in Greece.
    In 2015, Dimitris received the IEEE Signal Processing Society, Young
    Author Best Paper Award. In 2018, he co-founded and was Program co-Chair
    for SysML, a new conference that targets research at the intersection of
    machine learning and systems.

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