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X-WR-CALNAME:Towards Interpretable Deep Learning - Lily Weng\, Assistant Pr
 ofessor\, UC San Diego
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TZID:America/Vancouver
TZUNTIL:20251102T090000Z
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DTSTART:20231105T020000
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RDATE:20241103T020000
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DTSTAMP:20251220T054815Z
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CREATED:20240213T001830Z
DESCRIPTION:Abstract: Deep neural networks (DNNs) have achieved unprecedent
 ed success across many scientific and engineering fields in the last decad
 es. Despite its empirical success\, however\, they are notoriously black-b
 ox models that are difficult to understand their decision process. Lacking
  interpretability is one critical issue that may seriously hinder the depl
 oyment of DNNs in high-stake applications\, which need interpretability to
  trust the prediction\, to understand potential failures\, and to be able 
 to mitigate harms and eliminate biases in the model. In this talk\, I'll s
 hare some exciting…
DTSTART;TZID=America/Vancouver:20240226T150000
DTEND;TZID=America/Vancouver:20240226T160000
LAST-MODIFIED:20240220T155800Z
LOCATION:UBC Vancouver Campus\, MCLD 3038
SUMMARY:Towards Interpretable Deep Learning - Lily Weng\, Assistant Profess
 or\, UC San Diego
TRANSP:OPAQUE
URL:https://caida.ubc.ca/index.php/event/towards-interpretable-deep-learnin
 g-lily-weng-assistant-professor-uc-san-diego
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