Search Results for author: Sebastian Lehner

Found 7 papers, 6 papers with code

Variational Annealing on Graphs for Combinatorial Optimization

1 code implementation NeurIPS 2023 Sebastian Sanokowski, Wilhelm Berghammer, Sepp Hochreiter, Sebastian Lehner

Several recent unsupervised learning methods use probabilistic approaches to solve combinatorial optimization (CO) problems based on the assumption of statistically independent solution variables.

Combinatorial Optimization

Residual Neural Networks for the Prediction of Planetary Collision Outcomes

1 code implementation9 Oct 2022 Philip M. Winter, Christoph Burger, Sebastian Lehner, Johannes Kofler, Thomas I. Maindl, Christoph M. Schäfer

We formulate the ML task as a multi-task regression problem, allowing simple, yet efficient training of ML models for collision treatment in an end-to-end manner.

Out-of-Distribution Generalization

Few-Shot Learning by Dimensionality Reduction in Gradient Space

1 code implementation7 Jun 2022 Martin Gauch, Maximilian Beck, Thomas Adler, Dmytro Kotsur, Stefan Fiel, Hamid Eghbal-zadeh, Johannes Brandstetter, Johannes Kofler, Markus Holzleitner, Werner Zellinger, Daniel Klotz, Sepp Hochreiter, Sebastian Lehner

We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace.

Dimensionality Reduction Few-Shot Learning

Boundary Graph Neural Networks for 3D Simulations

1 code implementation21 Jun 2021 Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter

However, it is notoriously difficult to integrate them into machine learning approaches due to their heterogeneity with respect to size and orientation.

Computational Efficiency

Learning 3D Granular Flow Simulations

1 code implementation4 May 2021 Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter

Recently, the application of machine learning models has gained momentum in natural sciences and engineering, which is a natural fit due to the abundance of data in these fields.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.