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X-WR-CALNAME:A Gentle Introduction to Neural Network Verification
X-WR-TIMEZONE:America/Los_Angeles
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DTSTART:20070311T020000
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BEGIN:VEVENT
UID:2025-08-28-daniel-neider@cml.ics.uci.edu
DTSTAMP:20250828T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20250828T130000
DTEND;TZID=America/Los_Angeles:20250828T140000
SUMMARY:[CML Seminar] Daniel Neider: A Gentle Introduction to Neural Networ
k Verification
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Daniel Neider\, Professor of Verification and Formal Guarantees
of Machine Learning\, Department of Computer Science\, TU Dortmund\n\nTit
le: A Gentle Introduction to Neural Network Verification\n\nAbstract: Arti
ficial Intelligence has become ubiquitous in modern life. This Cambrian ex
plosion of intelligent systems has been made possible by extraordinary adv
ances in machine learning\, especially in training deep neural networks an
d their ingenious architectures. However\, like traditional hardware and s
oftware\, neural networks often have defects\, which are notoriously diffi
cult to detect and correct. Consequently\, deploying them in safety-critic
al settings remains a substantial challenge. Motivated by the success of f
ormal methods in establishing the reliability of safety-critical hardware
and software\, numerous formal verification techniques for deep neural net
works have emerged recently. As a guide through the vibrant and rapidly ev
olving field of neural network verification\, this talk will give an overv
iew of the fundamentals and core concepts of the field\, discuss prototypi
cal examples of various existing verification approaches\, and showcase ho
w generative AI can improve verification results.\n\nhttps://cml.ics.uci.e
du/seminars/2025-08-28-daniel-neider
X-ALT-DESC;FMTTYPE=text/html:Daniel Neider\, Professor o
f Verification and Formal Guarantees of Machine Learning\, Department of C
omputer Science\, TU Dortmund
Title: A Gentle Introduction t
o Neural Network Verification
Abstract: Artificial Intellige
nce has become ubiquitous in modern life. This Cambrian explosion of intel
ligent systems has been made possible by extraordinary advances in machine
learning\, especially in training deep neural networks and their ingeniou
s architectures. However\, like traditional hardware and software\, neural
networks often have defects\, which are notoriously difficult to detect a
nd correct. Consequently\, deploying them in safety-critical settings rema
ins a substantial challenge. Motivated by the success of formal methods in
establishing the reliability of safety-critical hardware and software\, n
umerous formal verification techniques for deep neural networks have emerg
ed recently. As a guide through the vibrant and rapidly evolving field of
neural network verification\, this talk will give an overview of the funda
mentals and core concepts of the field\, discuss prototypical examples of
various existing verification approaches\, and showcase how generative AI
can improve verification results.
https://cml.ics.uci.edu/seminars/2025-0
8-28-daniel-neider
URL:https://cml.ics.uci.edu/seminars/2025-08-28-daniel-neider
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