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SUMMARY:Emtiyaz Khan (RIKEN-AIP\, Tokyo and OIST\, Okinawa\, Japan)
DTSTART:20220428T090000Z
DTEND:20220428T100000Z
DTSTAMP:20260423T003253Z
UID:MPML/71
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/71/">Th
 e Bayesian Learning Rule for Adaptive AI</a>\nby Emtiyaz Khan (RIKEN-AIP\,
  Tokyo and OIST\, Okinawa\, Japan) as part of Mathematics\, Physics and Ma
 chine Learning (IST\, Lisbon)\n\n\nAbstract\nHumans and animals have a nat
 ural ability to autonomously learn and quickly adapt to their surroundings
 . How can we design AI systems that do the same? In this talk\, I will pre
 sent Bayesian principles to bridge such gaps between humans and AI. I will
  show that a wide variety of machine-learning algorithms are instances of 
 a single learning-rule called the Bayesian learning rule. The rule unravel
 s a dual perspective yielding new adaptive mechanisms for machine-learning
  based AI systems. My hope is to convince the audience that Bayesian princ
 iples are indispensable for an AI that learns as efficiently as we do.\n\n
 <p><strong>Reference: </strong>M.E. Khan\, H. Rue\, The Bayesian Learning 
 Rule [<a href="https://arxiv.org/abs/2107.04562" rel="noreferrer" target="
 _blank">arXiv</a>] [<a href="https://twitter.com/EmtiyazKhan/status/141449
 8922584711171?s=20" rel="noreferrer" target="_blank">Tweet</a>]</p>\n
LOCATION:https://researchseminars.org/talk/MPML/71/
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