Lorenz Richter

According to our database, Lorenz Richter authored at least 15 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Fast and Unified Path Gradient Estimators for Normalizing Flows.

Lorenz Vaitl, Ludwig Winkler, Lorenz Richter, Pan Kessel

CoRR, 2024

2023

Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention.

Frank Weilandt, Robert Behling, Romulo Goncalves, Arash Madadi, Lorenz Richter, Tiago Sanona, Daniel Spengler, Jona Welsch

Remote. Sens., February, 2023

From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs.

Lorenz Richter, Leon Sallandt, Nikolas Nüsken

CoRR, 2023

Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness.

Carsten Hartmann, Lorenz Richter

CoRR, 2023

Improved sampling via learned diffusions.

Lorenz Richter, Julius Berner, Guan-Horng Liu

CoRR, 2023

2022

An optimal control perspective on diffusion-based generative modeling.

Julius Berner, Lorenz Richter, Karen Ullrich

CoRR, 2022

Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning.

Lorenz Richter, Julius Berner

Proceedings of the International Conference on Machine Learning, 2022

2021

Interpolating between BSDEs and PINNs - deep learning for elliptic and parabolic boundary value problems.

Nikolas Nüsken, Lorenz Richter

CoRR, 2021

Nonasymptotic bounds for suboptimal importance sampling.

Carsten Hartmann, Lorenz Richter

CoRR, 2021

Solving high-dimensional parabolic PDEs using the tensor train format.

Lorenz Richter, Leon Sallandt, Nikolas Nüsken

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Model Order Reduction for (Stochastic-) Delay Equations With Error Bounds.

Simon Becker, Lorenz Richter

CoRR, 2020

Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space.

Nikolas Nüsken, Lorenz Richter

CoRR, 2020

VarGrad: A Low-Variance Gradient Estimator for Variational Inference.

Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Feedback control theory & Model order reduction for stochastic equations.

Simon Becker, Carsten Hartmann, Martin Redmann, Lorenz Richter

CoRR, 2019

2017

Variational Characterization of Free Energy: Theory and Algorithms.

Carsten Hartmann, Lorenz Richter, Christof Schütte, Wei Zhang

Entropy, 2017