Quantifying the generalization error in deep learning in terms of data
distribution and neural network smoothness

Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

27 May 2019

George Karniadakis

Papers citing "Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness"

14 / 14 papers shown

Title
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes S. Lehmler Muhammad Saif-ur-Rehman Tobias Glasmachers Ioannis Iossifidis 27 0 0 01 Aug 2023
PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion Yige Yuan Bingbing Xu Bo Lin Liang Hou Fei Sun Huawei Shen Xueqi Cheng DiffM 26 4 0 25 May 2023
Learning Functional Transduction Mathieu Chalvidal Thomas Serre Rufin VanRullen AI4CE 38 2 0 01 Feb 2023
Reliable extrapolation of deep neural operators informed by physics or sparse observations Min Zhu Handi Zhang Anran Jiao George Karniadakis Lu Lu 50 91 0 13 Dec 2022
Protecting Split Learning by Potential Energy Loss Fei Zheng Chaochao Chen Lingjuan Lyu Xinyi Fu Xing Fu Weiqiang Wang Xiaolin Zheng Jianwei Yin 49 4 0 18 Oct 2022
Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs Birgit Hillebrecht B. Unger PINN 18 5 0 07 Oct 2022
Overview frequency principle/spectral bias in deep learning Z. Xu Yaoyu Zhang Tao Luo FaML 33 66 0 19 Jan 2022
Embedding Principle: a hierarchical structure of loss landscape of deep neural networks Yaoyu Zhang Yuqing Li Zhongwang Zhang Tao Luo Z. Xu 29 22 0 30 Nov 2021
Approximation capabilities of measure-preserving neural networks Aiqing Zhu Pengzhan Jin Yifa Tang 29 8 0 21 Jun 2021
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE Juntang Zhuang Nicha Dvornek Xiaoxiao Li S. Tatikonda X. Papademetris James Duncan BDL 66 110 0 03 Jun 2020
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems Pengzhan Jin Zhen Zhang Aiqing Zhu Yifa Tang George Karniadakis 21 21 0 11 Jan 2020
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations E. Kharazmi Z. Zhang George Karniadakis 24 237 0 27 Nov 2019
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 52 1,489 0 10 Jul 2019
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 308 2,892 0 15 Sep 2016