Please note: This seminar will take place online.
Mojtaba
Valipour,
PhD
candidate
David
R.
Cheriton
School
of
Computer
Science
Supervisor: Professor Ali Ghodsi
Symbolic
regression
is
the
task
of
identifying
a
mathematical
expression
that
best
fits
a
provided
dataset
of
input
and
output
values.
Due
to
the
richness
of
the
space
of
mathematical
expressions,
symbolic
regression
is
generally
a
challenging
problem.
While
conventional
approaches
based
on
genetic
evolution
algorithms
have
been
used
for
decades,
deep
learning-based
methods
are
relatively
new
and
an
active
research
area.
In
this
work,
we
present
SymbolicGPT,
a
novel
transformer-based
language
model
for
symbolic
regression.
This
model
exploits
the
advantages
of
probabilistic
language
models
like
GPT,
including
strength
in
performance
and
flexibility.
Through
comprehensive
experiments,
we
show
that
our
model
performs
strongly
compared
to
competing
models
with
respect
to
the
accuracy,
running
time,
and
data
efficiency.
Link
to
the
paper: https://arxiv.org/abs/2106.14131
To attend this PhD seminar on Google Meet, please go https://meet.google.com/ask-fndk-khj.