Julius Berner - RankMe

59 papers A* 14Journal 44Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching. CoRR Denis Blessing, Lorenz Richter, Julius Berner, Egor Malitskiy, Gerhard Neumann
2026	J	`jnl`	Decoupled Diffusion Sampling for Inverse Problems on Function Spaces. CoRR Thomas Y. L. Lin, Jiachen Yao, Lufang Chiang, Julius Berner, Anima Anandkumar
2026	J	`jnl`	From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training. Trans. Mach. Learn. Res. Julius Berner, Lorenz Richter, Marcin Sendera, Jarrid Rector-Brooks, Nikolay Malkin
2026	J	`jnl`	Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction. Trans. Mach. Learn. Res. Hrishikesh Viswanath, Yue Chang, Aleksey Panas, Julius Berner, Peter Yichen Chen, Aniket Bera
2026	J	`jnl`	Mode Seeking meets Mean Seeking for Fast Long Video Generation. CoRR Shengqu Cai, Weili Nie, Chao Liu, Julius Berner, Lvmin Zhang, Nanye Ma, Hansheng Chen, Maneesh Agrawala, Leonidas J. Guibas, Gordon Wetzstein, Arash Vahdat
2026	J	`jnl`	Operator Learning Using Weak Supervision from Walk-on-Spheres. CoRR Hrishikesh Viswanath, Hong Chul Nam, Xi Deng, Julius Berner, Anima Anandkumar, Aniket Bera
2026	J	`jnl`	Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data. CoRR Duy Nguyen, Jiachen Yao, Jiayun Wang, Julius Berner, Animashree Anandkumar
2026	J	`jnl`	Transition Matching Distillation for Fast Video Generation. CoRR Weili Nie, Julius Berner, Nanye Ma, Chao Liu, Saining Xie, Arash Vahdat
2026	J	`jnl`	Variational Flow Maps: Make Some Noise for One-Step Conditional Generation. CoRR Abbas Mammadov, So Takao, Bohan Chen, Ricardo Baptista, Morteza Mardani, Yee Whye Teh, Julius Berner
2025	J	`jnl`	Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators. CoRR Jiayun Wang, Yousuf Aborahama, Arya Khokhar, Yang Zhang, Chuwei Wang, Karteekeya Sastry, Julius Berner, Yilin Luo, Boris Bonev, Zongyi Li, Kamyar Azizzadenesheli, Lihong V. Wang, Anima Anandkumar
2025	J	`jnl`	Advancing End-to-End Pixel Space Generative Modeling via Self-supervised Pre-training. CoRR Jiachen Lei, Keli Liu, Julius Berner, Haiming Yu, Hongkai Zheng, Jiahong Wu, Xiangxiang Chu
2025	J	`jnl`	Enabling Automatic Differentiation with Mollified Graph Neural Operators. CoRR Ryan Y. Lin, Julius Berner, Valentin Duruisseaux, David Pitt, Daniel V. Leibovici, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar
2025	J	`jnl`	Enabling Automatic Differentiation with Mollified Graph Neural Operators. Trans. Mach. Learn. Res. Ryan Y. Lin, Julius Berner, Valentin Duruisseaux, David Pitt, Daniel V. Leibovici, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar
2025	J	`jnl`	From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training. CoRR Julius Berner, Lorenz Richter, Marcin Sendera, Jarrid Rector-Brooks, Nikolay Malkin
2025	J	`jnl`	Guided Diffusion Sampling on Function Spaces with Applications to PDEs. CoRR Jiachen Yao, Abbas Mammadov, Julius Berner, Gavin Kerrigan, Jong Chul Ye, Kamyar Azizzadenesheli, Anima Anandkumar
2025	A*	`conf`	Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing. CVPR Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song
2025	J	`jnl`	Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning. CoRR Julius Berner, Miguel Liu-Schiaffini, Jean Kossaifi, Valentin Duruisseaux, Boris Bonev, Kamyar Azizzadenesheli, Anima Anandkumar
2025	A*	`conf`	Robust Representation Consistency Model via Contrastive Denoising. ICLR Jiachen Lei, Julius Berner, Jiongxiao Wang, Zhongzhu Chen, Chaowei Xiao, Zhongjie Ba, Kui Ren, Jun Zhu, Anima Anandkumar
2025	J	`jnl`	Robust Representation Consistency Model via Contrastive Denoising. CoRR Jiachen Lei, Julius Berner, Jiongxiao Wang, Zhongzhu Chen, Zhongjia Ba, Kui Ren, Jun Zhu, Anima Anandkumar
2025	A*	`conf`	Sequential Controlled Langevin Diffusions. ICLR Junhua Chen, Lorenz Richter, Julius Berner, Denis Blessing, Gerhard Neumann, Anima Anandkumar
2025	J	`jnl`	Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference. CoRR Denis Blessing, Julius Berner, Lorenz Richter, Carles Domingo-Enrich, Yuanqi Du, Arash Vahdat, Gerhard Neumann
2025	A*	`conf`	Underdamped Diffusion Bridges with Applications to Sampling. ICLR Denis Blessing, Julius Berner, Lorenz Richter, Gerhard Neumann
2025	J	`jnl`	Underdamped Diffusion Bridges with Applications to Sampling. CoRR Denis Blessing, Julius Berner, Lorenz Richter, Gerhard Neumann
2024	J	`jnl`	A Library for Learning Neural Operators. CoRR Jean Kossaifi, Nikola B. Kovachki, Zongyi Li, David Pitt, Miguel Liu-Schiaffini, Robert Joseph George, Boris Bonev, Kamyar Azizzadenesheli, Julius Berner, Anima Anandkumar
2024	J	`jnl`	An optimal control perspective on diffusion-based generative modeling. Trans. Mach. Learn. Res. Julius Berner, Lorenz Richter, Karen Ullrich
2024	J	`jnl`	Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators. CoRR Chuwei Wang, Julius Berner, Zongyi Li, Di Zhou, Jiayun Wang, Jane Bae, Anima Anandkumar
2024	A*	`conf`	DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training. ICML Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu
2024	J	`jnl`	DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training. CoRR Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu
2024	J	`jnl`	Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning. CoRR Simon Frieder, Jonas Bayer, Katherine M. Collins, Julius Berner, Jacob Loader, András Juhász, Fabian Ruehle, Sean Welleck, Gabriel Poesia, Ryan-Rhys Griffiths, Adrian Weller, Anirudh Goyal, Thomas Lukasiewicz, Timothy Gowers
2024	J	`jnl`	Dynamical Measure Transport and Neural PDE Solvers for Sampling. CoRR Jingtong Sun, Julius Berner, Lorenz Richter, Marius Zeinhofer, Johannes Müller, Kamyar Azizzadenesheli, Anima Anandkumar
2024	J	`jnl`	Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems. CoRR Freya Shah, Taylor L. Patti, Julius Berner, Bahareh Tolooshams, Jean Kossaifi, Anima Anandkumar
2024	A*	`conf`	Improved sampling via learned diffusions. ICLR Lorenz Richter, Julius Berner
2024	J	`jnl`	Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing. CoRR Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song
2024	A*	`conf`	Neural Operators with Localized Integral and Differential Kernels. ICML Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar
2024	J	`jnl`	Neural Operators with Localized Integral and Differential Kernels. CoRR Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar
2024	A*	`conf`	Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs. NeurIPS 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, Animashree Anandkumar
2024	J	`jnl`	Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs. CoRR 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
2024	J	`jnl`	Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs. CoRR Hrishikesh Viswanath, Yue Chang, Julius Berner, Peter Yichen Chen, Aniket Bera
2024	J	`jnl`	Sequential Controlled Langevin Diffusions. CoRR Junhua Chen, Lorenz Richter, Julius Berner, Denis Blessing, Gerhard Neumann, Anima Anandkumar
2024	A*	`conf`	Solving Poisson Equations using Neural Walk-on-Spheres. ICML Hong Chul Nam, Julius Berner, Anima Anandkumar
2024	J	`jnl`	Solving Poisson Equations using Neural Walk-on-Spheres. CoRR Hong Chul Nam, Julius Berner, Anima Anandkumar
2024	—	`conf`	The IMO Small Challenge: Not-Too-Hard Olympiad Math Datasets for LLMs. Tiny Papers @ ICLR Simon Frieder, Mirek Olsák, Julius Berner, Thomas Lukasiewicz
2023	J	`jnl`	Improved sampling via learned diffusions. CoRR Lorenz Richter, Julius Berner
2023	J	`jnl`	Large Language Models for Mathematicians. CoRR Simon Frieder, Julius Berner, Philipp Petersen, Thomas Lukasiewicz
2023	A*	`conf`	Learning ReLU networks to high uniform accuracy is intractable. ICLR Julius Berner, Philipp Grohs, Felix Voigtländer
2023	A*	`conf`	Mathematical Capabilities of ChatGPT. NeurIPS Simon Frieder, Luca Pinchetti, Alexis Chevalier, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Petersen, Julius Berner
2023	J	`jnl`	Mathematical Capabilities of ChatGPT. CoRR Simon Frieder, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Alexis Chevalier, Julius Berner
2022	J	`jnl`	An optimal control perspective on diffusion-based generative modeling. CoRR Julius Berner, Lorenz Richter, Karen Ullrich
2022	A*	`conf`	Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. ICML Lorenz Richter, Julius Berner
2022	J	`jnl`	Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. CoRR Lorenz Richter, Julius Berner
2022	J	`jnl`	Training ReLU networks to high uniform accuracy is intractable. CoRR Julius Berner, Philipp Grohs, Felix Voigtländer
2021	J	`jnl`	The Modern Mathematics of Deep Learning. CoRR Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
2020	J	`jnl`	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. Julius Berner, Philipp Grohs, Arnulf Jentzen
2020	A*	`conf`	Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. NeurIPS Julius Berner, Markus Dablander, Philipp Grohs
2020	J	`jnl`	Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. CoRR Julius Berner, Markus Dablander, Philipp Grohs
2019	A*	`conf`	How degenerate is the parametrization of neural networks with the ReLU activation function? NeurIPS Dennis Elbrächter, Julius Berner, Philipp Grohs
2019	J	`jnl`	How degenerate is the parametrization of neural networks with the ReLU activation function? CoRR Julius Berner, Dennis Elbrächter, Philipp Grohs
2019	J	`jnl`	Towards a regularity theory for ReLU networks - chain rule and global error estimates. CoRR Julius Berner, Dennis Elbrächter, Philipp Grohs, Arnulf Jentzen
2018	J	`jnl`	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 Julius Berner, Philipp Grohs, Arnulf Jentzen