Generalization Error Analysis of Neural Networks with Gradient Based Regularization
From MaRDI portal
Publication:5045671
DOI10.4208/cicp.OA-2021-0211MaRDI QIDQ5045671
Ling-Feng Li, Jiang Yang, Xue-Cheng Tai
Publication date: 7 November 2022
Published in: Communications in Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2107.02797
Uses Software
Cites Work
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- An algorithm for total variation minimization and applications
- Hidden physics models: machine learning of nonlinear partial differential equations
- Multilayer feedforward networks are universal approximators
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- A multiphase level set framework for image segmentation using the Mumford and Shah model
- DGM: a deep learning algorithm for solving partial differential equations
- Adversarial defense via the data-dependent activation, total variation minimization, and adversarial training
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- Universality of deep convolutional neural networks
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- The Split Bregman Method for L1-Regularized Problems
- Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models
- A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
- Universal approximation bounds for superpositions of a sigmoidal function
- Active contours without edges
- High-Dimensional Statistics
- Solving high-dimensional partial differential equations using deep learning
- Partial differential equation regularization for supervised machine learning
- Machine Learning and Computational Mathematics
- Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains
- Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains
- Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions
- Deep Learning: An Introduction for Applied Mathematicians
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Generalization Error Analysis of Neural Networks with Gradient Based Regularization
