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CATEGORIES:id366's list
SUMMARY:Sign and Basis Invariant Networks for Spectral Gra
ph Representation Learning - Joshua Robinson\, MIT
DTSTART;TZID=Europe/London:20221012T163000
DTEND;TZID=Europe/London:20221012T173000
UID:TALK180422AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/180422
DESCRIPTION:Eigenvectors computed from data arise in various s
cenarios including principal component analysis\,
and matrix factorizations. Another key example is
the eigenvectors of the graph Laplacian\, which en
code information about the structure of a graph or
manifold. An important recent application of Lapl
acian eigenvector is to graph positional encodings
\, which have been used to develop more powerful g
raph architectures. However\, eigenvectors have sy
mmetries that should be respected by models taking
eigenvector inputs: (i) sign flips\, since if v i
s an eigenvector then so is -v\; and (ii) more gen
eral basis symmetries\, which occur in higher dime
nsional eigenspaces with infinitely many choices o
f basis eigenvectors. We introduce SignNet and Bas
isNet---new neural network architectures that are
sign and basis invariant. We prove that our networ
ks are universal\, i.e.\, they can approximate any
continuous function of eigenvectors with the desi
red invariances. Moreover\, when used with Laplaci
an eigenvectors\, our architectures are provably e
xpressive for graph representation learning: they
can approximate—and go beyond—any spectral graph c
onvolution\, and can compute spectral invariants t
hat go beyond message passing neural networks. Exp
eriments show the strength of our networks for mol
ecular graph regression\, learning expressive grap
h representations\, and more.
LOCATION:Lecture Theater 1
CONTACT:Iulia Duta
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