BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Connecting Variational Autoencoders Back to the Brain X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2021-02-01-joe-marino@cml.ics.uci.edu DTSTAMP:20210201T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20210201T130000 DTEND;TZID=America/Los_Angeles:20210201T140000 SUMMARY:[CML Seminar] Joe Marino: Connecting Variational Autoencoders Back to the Brain LOCATION:Online (live stream) DESCRIPTION:Joe Marino\, PhD Student\, Computation and Neural Systems\, Cal ifornia Institute of Technology\n\nTitle: Connecting Variational Autoencod ers Back to the Brain\n\nAbstract: Unsupervised machine learning has recen tly dramatically improved our ability to model and extract structure from data. One such approach is deep latent variable models\, including variati onal autoencoders (VAEs)\, which can be traced back to the Helmholtz machi ne and\, in turn\, ideas from theoretical neuroscience. Neuroscientists ha ve further developed these ideas into a popular theory: predictive coding. Yet the machine learning community remains largely unaware of these conne ctions. In this talk\, I discuss the links between modern deep latent vari able models and predictive coding\, yielding several implications for corr espondences between machine learning and neuroscience\, including the sear ch for backpropagation in the brain.\n\nhttps://cml.ics.uci.edu/seminars/2 021-02-01-joe-marino X-ALT-DESC;FMTTYPE=text/html:Joe Marino\, PhD Student\, Computation and Neural Systems\, California Institute of Technology

Title: Connecting Variational Autoencoders Back to the Brain
Abstract: Unsupervised machine learning has recently dramatically improved our ability to model and extract structure from data. One such a pproach is deep latent variable models\, including variational autoencoder s (VAEs)\, which can be traced back to the Helmholtz machine and\, in turn \, ideas from theoretical neuroscience. Neuroscientists have further devel oped these ideas into a popular theory: predictive coding. Yet the machine learning community remains largely unaware of these connections. In this talk\, I discuss the links between modern deep latent variable models and predictive coding\, yielding several implications for correspondences betw een machine learning and neuroscience\, including the search for backpropa gation in the brain.

https://cml.ics.uci.edu/seminars/2021-02-01-joe-marino< /a> URL:https://cml.ics.uci.edu/seminars/2021-02-01-joe-marino END:VEVENT END:VCALENDAR