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BEGIN:VEVENT
DTSTAMP:20260417T061522Z
DTSTART;TZID=America/New_York:20241018T120000
DTEND;TZID=America/New_York:20241018T130000
SUMMARY:Robert Batterman - Deep Learning\, Correlations\, and the Statistics of Natural Images
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TZID:America/New_York
LOCATION: University of Pittsburgh\, 4200 Fifth Avenue\, Pittsburgh\, United States\, 15260
DESCRIPTION:<p>The Center for Philosophy of Science at the University of Pittsburgh invites you to join us for our Lunch Time Talk.&nbsp\;Attend in person at 1117 Cathedral of Learning or visit our live stream on YouTube at&nbsp\;<a rel="noopenerdata-cke-saved-href="https://www.youtube.com/channel/UCrRp47ZMXD7NXO3a9Gyh2sg">https://www.youtube.com/channel/UCrRp47ZMXD7NXO3a9Gyh2sg</a>.</p>\n\n<p><strong>LTT:&nbsp\;&nbsp\;<a data-cke-saved-href="https://www.hps.pitt.edu/people/robert-batterman">Robert Batterman</a></strong></p>\n<p>Friday\, October 18 @ 12:00 pm&nbsp\;-&nbsp\;1:30 pm&nbsp\;EDT</p>\n\n<p><strong>Title:&nbsp\;Deep Learning\, Correlations\, and the Statistics of Natural Images</strong></p>\n<p><strong>Abstract:&nbsp\;</strong>In this paper I will highlight certain connections between Deep Neural Networks (DNNs) and &nbsp\;Renormalization Group methods in statistical physics. &nbsp\;I will suggest that some of these similarities can be exploited to understand why some DNNs can be so successful. &nbsp\;Specifically\, I will suggest that DNNs generalize as well as they do because their training allows them to discover/recover certain remarkably robust statistical regularities present in their (diverse) training data sets.&nbsp\;</p>\n\n<p><strong>Can&rsquo\;t make it in-person? This talk will available online through the following:</strong></p>\n<p>Zoom:&nbsp\;&nbsp\;<a rel="noopenerdata-cke-saved-href="https://pitt.zoom.us/s/94223992589">https://pitt.zoom.us/s/94223992589</a></p>\n<p>YouTube at&nbsp\;<a data-cke-saved-href="https://www.youtube.com/channel/UCrRp47ZMXD7NXO3a9Gyh2sg">https://www.youtube.com/channel/UCrRp47ZMXD7NXO3a9Gyh2sg</a>.</p>
ORGANIZER;CN=Edouard Machery:
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