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BEGIN:VEVENT
DTSTAMP:20260616T121844Z
DTSTART;VALUE=DATE-TIME:20160112T123000
DTEND;VALUE=DATE-TIME:20160112T133000
SUMMARY:Andrew Blake\, special lecture at Warwick
TZID:Europe/London
UID:20160112-094d434551ee71e2015212998aec220c@warwick.ac.uk
CREATED:20160105T162318Z
DESCRIPTION:Professor Andrew Blake (Director\, Alan Turing Institute) Mac
 hines that learn: big data or explanatory models? A leading question abo
 ut machines that learn is whether they will turn out to depend more on p
 robabilistic models that explain the data\, or on networks that react to
  data and are trained on data at ever greater scale? In machine vision s
 ystems\, for instance\, this boils down to the comparative roles of two 
 paradigms: analysis-by-synthesis versus empirical recognisers. Each appr
 oach has its strengths\, and empirical recognisers especially have made 
 great strides in performance in the last few years\, through deep learni
 ng. One can speculate about how deeply the two approaches may eventually
  be integrated\, and on the progress that has already been made with suc
 h integration. http://www2.warwick.ac.uk/newsandevents/events/distinguis
 hedlecture
LOCATION:TBA
CATEGORIES:ATI
LAST-MODIFIED:20160105T162623Z
ORGANIZER;CN=David Firth:
END:VEVENT
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