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X-WR-CALNAME:Modeling Irregular Time Series with Continuous Recurrent Units
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UID:2022-02-07-maja-rudolph@cml.ics.uci.edu
DTSTAMP:20220207T000000Z
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DTSTART;TZID=America/Los_Angeles:20220207T130000
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SUMMARY:[CML Seminar] Maja Rudolph: Modeling Irregular Time Series with Con
tinuous Recurrent Units
LOCATION:Online (live stream)
DESCRIPTION:Maja Rudolph\, Senior Research Scientist\, Bosch Center for AI\
n\nTitle: Modeling Irregular Time Series with Continuous Recurrent Units\n
\nAbstract: Recurrent neural networks (RNNs) are a popular choice for mode
ling sequential data but assume constant time-intervals between observatio
ns. In many datasets (e.g. medical records) observation times are irregula
r and can carry important information. We propose continuous recurrent uni
ts (CRUs) — a neural architecture that naturally handles irregular inter
vals. The CRU assumes a hidden state that evolves according to a linear st
ochastic differential equation\, integrated into an encoder-decoder framew
ork via the continuous-discrete Kalman filter in closed form. We find that
the CRU can interpolate irregular time series better than methods based o
n neural ODEs.\n\nhttps://cml.ics.uci.edu/seminars/2022-02-07-maja-rudolph
X-ALT-DESC;FMTTYPE=text/html:Maja Rudolph\, Senior Resea
rch Scientist\, Bosch Center for AI
Title: Modeling Irregula
r Time Series with Continuous Recurrent Units
Abstract: Recu
rrent neural networks (RNNs) are a popular choice for modeling sequential
data but assume constant time-intervals between observations. In many data
sets (e.g. medical records) observation times are irregular and can carry
important information. We propose continuous recurrent units (CRUs) — a
neural architecture that naturally handles irregular intervals. The CRU as
sumes a hidden state that evolves according to a linear stochastic differe
ntial equation\, integrated into an encoder-decoder framework via the cont
inuous-discrete Kalman filter in closed form. We find that the CRU can int
erpolate irregular time series better than methods based on neural ODEs.
h
ttps://cml.ics.uci.edu/seminars/2022-02-07-maja-rudolph
URL:https://cml.ics.uci.edu/seminars/2022-02-07-maja-rudolph
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