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X-WR-CALNAME:Deep Anomaly Detection
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UID:2023-10-16-marius-kloft@cml.ics.uci.edu
DTSTAMP:20231016T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20231016T130000
DTEND;TZID=America/Los_Angeles:20231016T140000
SUMMARY:[CML Seminar] Marius Kloft: Deep Anomaly Detection
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Marius Kloft\, Professor of Computer Science\, RPTU Kaiserslaut
ern-Landau\, Germany\n\nTitle: Deep Anomaly Detection\n\nAbstract: Anomaly
detection is one of the fundamental topics in machine learning and artifi
cial intelligence. The aim is to find instances deviating from the norm
— so-called anomalies. Anomalies can be observed in various scenarios\,
from attacks on computer or energy networks to critical faults in a chemic
al factory or rare tumors in cancer imaging data. In my talk\, I will firs
t introduce the field of anomaly detection\, with an emphasis on deep anom
aly detection. Then\, I will present recent algorithms and theory for deep
anomaly detection\, with images as primary data type. I will demonstrate
how these methods can be better understood using explainable AI methods. I
will show new algorithms for deep anomaly detection on other data types\,
such as time series\, graphs\, tabular data\, and contaminated data. Fina
lly\, I will close my talk with an outlook on exciting future research dir
ections in anomaly detection and beyond.\n\nhttps://cml.ics.uci.edu/semina
rs/2023-10-16-marius-kloft
X-ALT-DESC;FMTTYPE=text/html:Marius Kloft\, Professor of
Computer Science\, RPTU Kaiserslautern-Landau\, Germany
Title:<
/b> Deep Anomaly Detection
Abstract: Anomaly detection is on
e of the fundamental topics in machine learning and artificial intelligenc
e. The aim is to find instances deviating from the norm — so-called anom
alies. Anomalies can be observed in various scenarios\, from attacks on co
mputer or energy networks to critical faults in a chemical factory or rare
tumors in cancer imaging data. In my talk\, I will first introduce the fi
eld of anomaly detection\, with an emphasis on deep anomaly detection. The
n\, I will present recent algorithms and theory for deep anomaly detection
\, with images as primary data type. I will demonstrate how these methods
can be better understood using explainable AI methods. I will show new alg
orithms for deep anomaly detection on other data types\, such as time seri
es\, graphs\, tabular data\, and contaminated data. Finally\, I will close
my talk with an outlook on exciting future research directions in anomaly
detection and beyond.
https://cml.ics.uci.edu/seminars/2023-10-16-marius-
kloft
URL:https://cml.ics.uci.edu/seminars/2023-10-16-marius-kloft
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