BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:US/Eastern
BEGIN:DAYLIGHT
DTSTART:20200308T030000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20201101T010000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20200526T231322Z
UID:D5BD9785-4E26-43AF-8F02-F89A1A093382
DTSTART;TZID=US/Eastern:20200526T173000
DTEND;TZID=US/Eastern:20200526T190000
DESCRIPTION:Artificial intelligence (AI) today is about developing the capa
 bility of a single node to make an inference or to respond to its surround
 ings with the appropriate action. Inevitably\, autonomous systems will evo
 lve to operate in cooperation\, as intelligent swarms. This talk will intr
 oduce peer to peer algorithms for distributed computation and inference. W
 e will start from the Average Consensus (AC) primitive\, its convergence p
 roperties over deterministic and random networks and then introduce the Di
 stributed Sub-Gradient (DSG) and the Alternating Direction Method of Multi
 pliers (ADMM) methods. The applications of these algorithms to distributed
  computation tasks such as hypothesis testing\, linear regression\, least 
 square approximations\, principal component analysis and dictionary learni
 ng will be highlighted throughout the talk.\n\nSpeaker(s): Dr. Anna Scagli
 one\, \n\nAgenda: \nTechnical support set-up: 5:30pm EST\nIntroductions 6p
 m-6:05pm EST\nDL: 6:05pm-6:50pm EST\nQ&A: 6:50pm-7pm EST\n\nZoom Virtual M
 eeting\, Arizona\, United States
LOCATION:Zoom Virtual Meeting\, Arizona\, United States
ORGANIZER:Signal@ieee.li
SEQUENCE:10
SUMMARY:Distributed Learning and Signal Processing Algorithms
URL;VALUE=URI:https://events.vtools.ieee.org/m/230240
X-ALT-DESC:Description: <br /><p>Artificial intelligence (AI) today is abou
 t developing the capability of a single node to make an inference or to re
 spond to&nbsp\;its surroundings with the appropriate action. Inevitably\, 
 autonomous systems will evolve to operate in cooperation\, as&nbsp\;intell
 igent swarms. This talk will introduce peer to peer algorithms for distrib
 uted computation and inference. &nbsp\;We will&nbsp\;start from the Averag
 e Consensus (AC) primitive\, its convergence properties over deterministic
  and random networks&nbsp\;and then introduce the Distributed Sub-Gradient
  (DSG) and the Alternating Direction Method of Multipliers (ADMM)&nbsp\;me
 thods. The applications of these algorithms to distributed computation tas
 ks such as hypothesis testing\, linear&nbsp\;regression\, least square app
 roximations\, principal component analysis and dictionary learning will be
  highlighted&nbsp\;throughout the talk.</p><br /><br />Agenda: <br /><p>Te
 chnical support set-up: 5:30pm EST<br />Introductions 6pm-6:05pm EST<br />
 DL: 6:05pm-6:50pm EST<br />Q&amp\;A: 6:50pm-7pm EST</p>
END:VEVENT
END:VCALENDAR