Lorenz Richter

According to our database, Lorenz Richter authored at least 20 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

An optimal control perspective on diffusion-based generative modeling.

Julius Berner, Lorenz Richter, Karen Ullrich

Trans. Mach. Learn. Res., 2024

Improving Control Based Importance Sampling Strategies for Metastable Diffusions via Adapted Metadynamics.

Enric Ribera Borrell, Jannes Quer, Lorenz Richter, Christof Schütte

SIAM J. Sci. Comput., 2024

Nonasymptotic Bounds for Suboptimal Importance Sampling.

Carsten Hartmann, Lorenz Richter

SIAM/ASA J. Uncertain. Quantification, 2024

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

Lorenz Richter, Leon Sallandt, Nikolas Nüsken

J. Mach. Learn. Res., 2024

EuroCropsML: A Time Series Benchmark Dataset For Few-Shot Crop Type Classification.

Joana Reuss, Jan MacDonald, Simon Becker, Lorenz Richter, Marco Körner

CoRR, 2024

Dynamical Measure Transport and Neural PDE Solvers for Sampling.

Jingtong Sun, Julius Berner, Lorenz Richter, Marius Zeinhofer, Johannes Müller, Kamyar Azizzadenesheli, Anima Anandkumar

CoRR, 2024

Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models.

Ludwig Winkler, Lorenz Richter, Manfred Opper

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Fast and unified path gradient estimators for normalizing flows.

Lorenz Vaitl, Ludwig Winkler, Lorenz Richter, Pan Kessel

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Improved sampling via learned diffusions.

Lorenz Richter, Julius Berner

Proceedings of the Twelfth International Conference on Learning Representations, 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

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

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

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