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• Batch Normalization Preconditioning for Stochastic Gradient Langevin Dynamics

  Susanna Lange, Wei Deng, Qiang Ye, Guang Lin

  2024-03-21

  33725 4576 Pages:65-82 Open-access

• On the Existence of Global Minima and Convergence Analyses for Gradient Descent Methods in the Training of Deep Neural Networks

  Arnulf Jentzen, Adrian Riekert

  2022-07-06

  41726 4348 Pages:141-246 Open-access

• Convergence of Stochastic Gradient Descent under a Local Łojasiewicz Condition for Deep Neural Networks

  Jing An, Jianfeng Lu

  2025-06-03

  8758 1118 Pages:89-107 Open-access

• Convergence of Stochastic Gradient Descent Schemes for Łojasiewicz-Landscapes

  Steffen Dereich, Sebastian Kassing

  2024-09-21

  20357 2471 Pages:245-281 Open-access

• A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization with Non-Isolated Local Minima

  Taehee Ko, Xiantao Li

  2023-06-06

  26413 2750 Pages:138-160 Open-access

• A Deep Uzawa-Lagrange Multiplier Approach for Boundary Conditions in PINNs and Deep Ritz Methods

  Charalambos G. Makridakis, Aaron Pim, Tristan Pryer

  2025-09-12

  44378 808 Pages:166-191 Open-access

• Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow

  Tjeerd Jan Heeringa, Tim Roith, Christoph Brune, Martin Burger

  2025-03-12

  11289 1334 Pages:48-88 Open-access

• Spectral Neural Networks: Approximation Theory and Optimization Landscape

  Chenghui Li, Rishi Sonthalia, Nicolás García Trillos

  2026-01-06

  459 483 Pages:19-70

• Convergence Analysis of Discrete Diffusion Model: Exact Implementation Through Uniformization

  Hongrui Chen, Lexing Ying

  2025-06-03

  8788 1063 Pages:108-127 Open-access

• Approximation Results for Gradient Flow Trained Neural Networks

  Gerrit Welper

  2024-06-27

  25372 2725 Pages:107-175 Open-access