BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:End-to-end Learnable Particle Filters and Smoothers 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:2024-12-02-ali-younis@cml.ics.uci.edu DTSTAMP:20241202T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20241202T130000 DTEND;TZID=America/Los_Angeles:20241202T140000 SUMMARY:[CML Seminar] Ali Younis: End-to-end Learnable Particle Filters and Smoothers LOCATION:Donald Bren Hall 4011 DESCRIPTION:Ali Younis\, PhD Student\, Department of Computer Science\, Uni versity of California\, Irvine\n\nTitle: End-to-end Learnable Particle Fil ters and Smoothers\n\nAbstract: Estimating the temporal state of a system from image sequences is an important task for many vision and robotics app lications. A number of classical frameworks for state estimation have been proposed\, but often these methods require human experts to specify the s ystem dynamics and measurement model\, requiring simplifying assumptions t hat hurt performance. In this presentation\, I will develop end-to-end lea rnable particle filters and particle smoothers\, and show how to bring cla ssic state estimation methods into the age of deep learning. We first crea te an end-to-end learnable particle filter that uses flexible neural netwo rks to propagate multimodal\, particle-based representations of state unce rtainty. We then expand on our particle filtering method to create the fir st end-to-end learnable particle smoother\, which incorporates information from future as well as past observations\, and apply this particle smooth er to the real-world task of city-scale geo-localization using camera and planimetric map data.\n\nhttps://cml.ics.uci.edu/seminars/2024-12-02-ali-y ounis X-ALT-DESC;FMTTYPE=text/html:Ali Younis\, PhD Student\, Department of Computer Science\, University of California\, Irvine

Title: End-to-end Learnable Particle Filters and Smoothers

< b>Abstract: Estimating the temporal state of a system from image seque nces is an important task for many vision and robotics applications. A num ber of classical frameworks for state estimation have been proposed\, but often these methods require human experts to specify the system dynamics a nd measurement model\, requiring simplifying assumptions that hurt perform ance. In this presentation\, I will develop end-to-end learnable particle filters and particle smoothers\, and show how to bring classic state estim ation methods into the age of deep learning. We first create an end-to-end learnable particle filter that uses flexible neural networks to propagate multimodal\, particle-based representations of state uncertainty. We then expand on our particle filtering method to create the first end-to-end le arnable particle smoother\, which incorporates information from future as well as past observations\, and apply this particle smoother to the real-w orld task of city-scale geo-localization using camera and planimetric map data.

https://cml.ics.uci.edu/seminars/2024-12-02-ali-younis URL:https://cml.ics.uci.edu/seminars/2024-12-02-ali-younis END:VEVENT END:VCALENDAR