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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1909.02253 |
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| _version_ | 1866910494502158336 |
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| author | Wendler, Chris Alistarh, Dan Püschel, Markus |
| author_facet | Wendler, Chris Alistarh, Dan Püschel, Markus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1909_02253 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Powerset Convolutional Neural Networks Wendler, Chris Alistarh, Dan Püschel, Markus Machine Learning 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. |
| title | Powerset Convolutional Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1909.02253 |