Julius Berner

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 27

Top Topics

Activation Function

Differential Equations

Top Venues

SIAM J. Math. Data Sci.

Publications

Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song
Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing. CoRR (2024)
Hrishikesh Viswanath, Yue Chang, Julius Berner, Peter Yichen Chen, Aniket Bera
Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs. CoRR (2024)
Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy, Daniel V. Leibovici, Zongyi Li, Boris Bonev, Colin White, Julius Berner, Raymond A. Yeh, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs. CoRR (2024)
Simon Frieder, Mirek Olsák, Julius Berner, Thomas Lukasiewicz
The IMO Small Challenge: Not-Too-Hard Olympiad Math Datasets for LLMs. Tiny Papers @ ICLR (2024)
Lorenz Richter, Julius Berner
Improved sampling via learned diffusions. ICLR (2024)
Julius Berner, Lorenz Richter, Karen Ullrich
An optimal control perspective on diffusion-based generative modeling. Trans. Mach. Learn. Res. 2024 (2024)
Jingtong Sun, Julius Berner, Lorenz Richter, Marius Zeinhofer, Johannes Müller, Kamyar Azizzadenesheli, Anima Anandkumar
Dynamical Measure Transport and Neural PDE Solvers for Sampling. CoRR (2024)
Hong Chul Nam, Julius Berner, Anima Anandkumar
Solving Poisson Equations using Neural Walk-on-Spheres. CoRR (2024)
Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training. CoRR (2024)
Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar
Neural Operators with Localized Integral and Differential Kernels. CoRR (2024)
Lorenz Richter, Julius Berner, Guan-Horng Liu
Improved sampling via learned diffusions. CoRR (2023)
Simon Frieder, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Alexis Chevalier, Julius Berner
Mathematical Capabilities of ChatGPT. CoRR (2023)
Julius Berner, Philipp Grohs, Felix Voigtländer
Learning ReLU networks to high uniform accuracy is intractable. ICLR (2023)
Simon Frieder, Luca Pinchetti, Alexis Chevalier, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Petersen, Julius Berner
Mathematical Capabilities of ChatGPT. NeurIPS (2023)
Simon Frieder, Julius Berner, Philipp Petersen, Thomas Lukasiewicz
Large Language Models for Mathematicians. CoRR (2023)
Julius Berner, Philipp Grohs, Felix Voigtländer
Training ReLU networks to high uniform accuracy is intractable. CoRR (2022)
Julius Berner, Lorenz Richter, Karen Ullrich
An optimal control perspective on diffusion-based generative modeling. CoRR (2022)
Lorenz Richter, Julius Berner
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. ICML (2022)
Lorenz Richter, Julius Berner
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. CoRR (2022)
Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
The Modern Mathematics of Deep Learning. CoRR (2021)
Julius Berner, Philipp Grohs, Arnulf Jentzen
Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black-Scholes Partial Differential Equations. SIAM J. Math. Data Sci. 2 (3) (2020)
Julius Berner, Markus Dablander, Philipp Grohs
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. NeurIPS (2020)
Julius Berner, Markus Dablander, Philipp Grohs
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. CoRR (2020)
Julius Berner, Dennis Elbrächter, Philipp Grohs
How degenerate is the parametrization of neural networks with the ReLU activation function? CoRR (2019)
Dennis Elbrächter, Julius Berner, Philipp Grohs
How degenerate is the parametrization of neural networks with the ReLU activation function? NeurIPS (2019)
Julius Berner, Dennis Elbrächter, Philipp Grohs, Arnulf Jentzen
Towards a regularity theory for ReLU networks - chain rule and global error estimates. CoRR (2019)
Julius Berner, Philipp Grohs, Arnulf Jentzen
Analysis of the generalization error: Empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations. CoRR (2018)