no code implementations • NeurIPS 2023 • David Lüdke, Marin Biloš, Oleksandr Shchur, Marten Lienen, Stephan Günnemann
Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data.
no code implementations • 6 Oct 2023 • Marcel Kollovieh, Lukas Gosch, Yan Scholten, Marten Lienen, Stephan Günnemann
In this work, we introduce Score-Based Adversarial Generation (ScoreAG), a novel framework that leverages the advancements in score-based generative models to generate adversarial examples beyond $\ell_p$-norm constraints, so-called unrestricted adversarial examples, overcoming their limitations.
1 code implementation • 29 May 2023 • Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann
On this dataset, we show that our generative model captures the distribution of turbulent flows caused by unseen objects and generates high-quality, realistic samples amenable for downstream applications without access to any initial state.
1 code implementation • 22 Oct 2022 • Marten Lienen, Stephan Günnemann
We introduce an ODE solver for the PyTorch ecosystem that can solve multiple ODEs in parallel independently from each other while achieving significant performance gains.
1 code implementation • ICLR 2022 • Marten Lienen, Stephan Günnemann
We propose a new method for spatio-temporal forecasting on arbitrarily distributed points.
Interpretability Techniques for Deep Learning Interpretable Machine Learning +2
no code implementations • 14 Jul 2021 • Johannes Gasteiger, Marten Lienen, Stephan Günnemann
The current best practice for computing optimal transport (OT) is via entropy regularization and Sinkhorn iterations.
no code implementations • 1 Jan 2021 • Johannes Klicpera, Marten Lienen, Stephan Günnemann
Optimal transport (OT) is a cornerstone of many machine learning tasks.