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Poster
Powerset Convolutional Neural Networks
Chris Wendler · Markus Püschel · Dan Alistarh

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #148
in Deep Learning -- CNN Architectures »

We present a novel class of convolutional neural networks (CNNs) for set functions, i.e., data indexed with the powerset of a finite set. The convolutions are derived as linear, shift-equivariant functions for various notions of shifts on set functions. The framework is fundamentally different from graph convolutions based on the Laplacian, as it provides not one but several basic shifts, one for each element in the ground set. Prototypical experiments with several set function classification tasks on synthetic datasets and on datasets derived from real-world hypergraphs demonstrate the potential of our new powerset CNNs.

Keywords: Deep Learning · Algorithms -> Classification; Applications -> Signal Processing; Deep Learning -> CNN Architectures; Deep Learning · Embedding
[ Paper]  [ Poster] 

Author Information

Chris Wendler (ETH Zurich)
Markus Püschel (ETH Zurich)
Dan Alistarh (IST Austria & NeuralMagic)

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