22 citations · 33 across the 13 of their papers we have counts for
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cs.LG2023★ 1 cited
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs
Lorenz Richter, Leon Sallandt, Nikolas Nüsken
The numerical approximation of partial differential equations (PDEs) poses formidable challenges in high dimensions since classical grid-based methods suffer from the so-called cur…
cs.LG2023
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness
Carsten Hartmann, Lorenz Richter
The recent advances in machine learning in various fields of applications can be largely attributed to the rise of deep learning (DL) methods and architectures. Despite being a key…
cs.LG2023
Improved sampling via learned diffusions
Lorenz Richter, Julius Berner
Recently, a series of papers proposed deep learning-based approaches to sample from target distributions using controlled diffusion processes, being trained only on the unnormalize…