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X-WR-CALNAME:Predictive Querying for Autoregressive Neural Sequence Models
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UID:2022-10-31-alex-boyd@cml.ics.uci.edu
DTSTAMP:20221031T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20221031T130000
DTEND;TZID=America/Los_Angeles:20221031T140000
SUMMARY:[CML Seminar] Alex Boyd: Predictive Querying for Autoregressive Neu
ral Sequence Models
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Alex Boyd\, PhD Student\, Department of Statistics\, University
of California\, Irvine\n\nTitle: Predictive Querying for Autoregressive N
eural Sequence Models\n\nAbstract: In reasoning about sequential events it
is natural to pose probabilistic queries such as when will event A occur
next or what is the probability of A occurring before B. However\, with ma
chine learning shifting towards neural autoregressive models such as RNNs
and transformers\, probabilistic querying has been largely restricted to s
imple cases such as next-event prediction\, in part because future queryin
g involves marginalization over large path spaces. In this talk\, we descr
ibe a novel representation of querying for discrete sequential models\, al
ong with approximation and search techniques to estimate these probabilist
ic queries\, and touch on extensions to continuous-time events.\n\nhttps:/
/cml.ics.uci.edu/seminars/2022-10-31-alex-boyd
X-ALT-DESC;FMTTYPE=text/html:Alex Boyd\, PhD Student\, D
epartment of Statistics\, University of California\, Irvine
Titl
e: Predictive Querying for Autoregressive Neural Sequence Models
Abstract: In reasoning about sequential events it is natural to p
ose probabilistic queries such as when will event A occur next or what is
the probability of A occurring before B. However\, with machine learning s
hifting towards neural autoregressive models such as RNNs and transformers
\, probabilistic querying has been largely restricted to simple cases such
as next-event prediction\, in part because future querying involves margi
nalization over large path spaces. In this talk\, we describe a novel repr
esentation of querying for discrete sequential models\, along with approxi
mation and search techniques to estimate these probabilistic queries\, and
touch on extensions to continuous-time events.
https://cml.ics.uci.edu/semin
ars/2022-10-31-alex-boyd
URL:https://cml.ics.uci.edu/seminars/2022-10-31-alex-boyd
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