BEGIN:VCALENDAR
PRODID:-//AddEvent Inc//AddEvent.com v1.7//EN
VERSION:2.0
BEGIN:VTIMEZONE
TZID:America/Toronto
BEGIN:STANDARD
DTSTART:20261101T010000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20260308T030000
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
DESCRIPTION:We will delve into the question: How do we enhance the robustness of Deep Neural Networks amidst shifting data distributions in online learning systems? Jehanzeb will present several approaches for test-time tuning for various domains such as images\, videos\, and point clouds. He will also touch upon methods developed for adapting vision-language models to improve their zero-shot classification abilities\, bringing them closer to dedicated (closed category set) classifiers trained with supervised fine-tuning. \n\nBio: "I am a third year Computer Vision PhD. student advised by Professor Horst Bischof\, at TU Graz\, Austria. My PhD. research is mainly focused on designing self-supervised and unsupervised representation learning techniques for making deep neural networks robust to distribution shifts on-the-fly\, at test-time. During numerous research projects\, I have worked with different types of data including images\, point clouds\, videos\, radar signals and most recently natural language. Along with the main research focus on online learning\, recently I have also started working with large language models (LLMs) and I am particularly interested in multi-modal (vision-language) models."\n\n------\n\nPowered by addevent.com \nShare your next event with us!\n
X-ALT-DESC;FMTTYPE=text/html:We will delve into the question: How do we enhance the robustness of Deep Neural Networks amidst shifting data distributions in online learning systems? Jehanzeb will present several approaches for test-time tuning for various domains such as images, videos, and point clouds. He will also touch upon methods developed for adapting vision-language models to improve their zero-shot classification abilities, bringing them closer to dedicated (closed category set) classifiers trained with supervised fine-tuning. <br><br>Bio: "I am a third year Computer Vision PhD. student advised by Professor Horst Bischof, at TU Graz, Austria. My PhD. research is mainly focused on designing self-supervised and unsupervised representation learning techniques for making deep neural networks robust to distribution shifts on-the-fly, at test-time. During numerous research projects, I have worked with different types of data including images, point clouds, videos, radar signals and most recently natural language. Along with the main research focus on online learning, recently I have also started working with large language models (LLMs) and I am particularly interested in multi-modal (vision-language) models."<br /><br />------<br /><br />Powered by addevent.com <br>Share your next event with us!<br>
UID:9b5777a7bfd04ea4b5d82d1ddd6fc252addeventcom
SUMMARY:[C4AI] Adaptation to Distribution Shifts in an Unsupervised Manner with Jehanzeb Mirza
DTSTART;TZID=America/Toronto:20231018T110000
DTEND;TZID=America/Toronto:20231018T120000
DTSTAMP:20260810T054621Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
LOCATION:meet.google.com/zro-qtya-ysn
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
BEGIN:VALARM
TRIGGER:-PT30M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
END:VCALENDAR