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VERSION:2.0
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CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
UID:bmh2f88gjtsavb7uon7hd692fc@google.com
CATEGORIES:RTportal.ru
CREATED:20170606T092329
SUMMARY:Структурные модели и глубинное обучение: Acceleration of Convolutional Neural Networks through Elimination of Redundant Convolutions
LOCATION:ИППИ, 6 этаж, 615 аудитория
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:Очередное заседание семинара "Структурные модели и глубинное обучение" сост
 оится 21 марта (понедельник), в 18.30, ИППИ, 6 этаж, 615 аудитория.\nОхранн
 ик на проходной будет предупрежден, проблем с проходом не будет.\n\nНазвани
 е: Acceleration of Convolutional Neural Networks through Elimination of Red
 undant Convolutions\n\nДокладчик: Михаил Фигурнов\n\nАннотация: We propose 
 a novel approach to reduce the computational cost of evaluation of convolut
 ional neural networks, a factor that has hindered their deployment in low-p
 ower devices such as mobile phones. Inspired by the loop perforation techni
 que from source code optimization, we speed up the bottleneck convolutional
  layers by skipping their evaluation in some of the spatial positions. We p
 ropose and analyze several strategies of choosing these positions. Our meth
 od allows to reduce the evaluation time of modern convolutional neural netw
 orks by 50% with a small decrease in accuracy. This event was imported from
 : https://rtportal.ru/index.php/kalendar-sobytij/eventdetail/105/-/struktur
 nye-modeli-i-glubinnoe-obuchenie-acceleration-of-convolutional-neural-netwo
 rks-through-elimination-of-redundant-convolutions?tmpl=component
DTSTAMP:20260812T201256Z
DTSTART;TZID=Europe/Moscow:20160321T183000
DTEND;TZID=Europe/Moscow:20160321T200000
SEQUENCE:0
TRANSP:OPAQUE
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