Search Results for author: Bernhard Schäfl

Found 5 papers, 3 papers with code

G-Signatures: Global Graph Propagation With Randomized Signatures

no code implementations17 Feb 2023 Bernhard Schäfl, Lukas Gruber, Johannes Brandstetter, Sepp Hochreiter

Graph neural networks (GNNs) have evolved into one of the most popular deep learning architectures.

Graph Learning

Hopular: Modern Hopfield Networks for Tabular Data

1 code implementation1 Jun 2022 Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, Sepp Hochreiter

In experiments on small-sized tabular datasets with less than 1, 000 samples, Hopular surpasses Gradient Boosting, Random Forests, SVMs, and in particular several Deep Learning methods.

General Classification

A GAN based solver of black-box inverse problems

no code implementations NeurIPS Workshop Deep_Invers 2019 Michael Gillhofer, Hubert Ramsauer, Johannes Brandstetter, Bernhard Schäfl, Sepp Hochreiter

We propose a GAN based approach to solve inverse problems which have non-differential or non-continuous forward relations.

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