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UID:394@lincs.fr
DTSTART;TZID=Europe/Paris:20180802T140000
DTEND;TZID=Europe/Paris:20180802T150000
DTSTAMP:20180920T082944Z
URL:https://www.lincs.fr/events/on-network-connectivity-for-distributed-ma
 chine-learning/
SUMMARY:On Network Connectivity for Distributed Machine Learning
DESCRIPTION:Abstract:\nMany learning problems are formulated as
 minimization of some loss function on a training set of examples.
 Distributed gradient methods on a cluster are often used to this purpose.
 In this talk we discuss how the variability of task execution times at
 cluster nodes affects the system throughput. In particular\, a simple but
 accurate model allows us to quantify how the time to solve the minimization
 problem depends on the network of information exchanges among the nodes.
 Interestingly\, we show that\, even when communication overhead may be
 neglected\, the clique is not necessarily the most effective topology\, as
 commonly assumed in previous works.\n\nBiography:\nGiovanni Neglia received
 a Ph.D. degree in Computer Science\, Electronics and Telecommunications
 from the University of Palermo\, Italy\, respectively in 2005. In 2005 he
 was a research scholar at the University of Massachusetts Amherst\,
 visiting &nbsp\;the Computer Networks Research Group. Since 2006 he works
 at Inria Sophia Antipolis\, France. He is associate editor for Elsevier
 Computer Communications journal and recipient of several best paper awards
 (ITC\, Greencomm\, NetSciCom\, VTC\, Valuetools\, Bionetics). His current
 research interests include cache networks and distributed optimization.
CATEGORIES:Seminars
LOCATION:Paris-Rennes Room (EIT Digital)\, 23 avenue d'Italie\, 75013
 Paris\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=23 avenue d'Italie\, 75013
 Paris\, France;X-APPLE-RADIUS=100;X-TITLE=Paris-Rennes Room (EIT
 Digital):geo:0,0
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TZID:Europe/Paris
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DTSTART:20180325T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
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