Vanilla feedforward neural networks as a discretization of dynamical systems
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Publication:6645925
DOI10.1007/S10915-024-02710-2MaRDI QIDQ6645925
Guanghua Ji, Yifei Duan, Yongqiang Cai, Liang Li
Publication date: 29 November 2024
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Approximation by other special function classes (41A30) Numerical problems in dynamical systems (65P99)
Cites Work
- Splitting methods for partial differential equations with rough solutions. Analysis and Matlab programs
- Multilayer feedforward networks are universal approximators
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- DGM: a deep learning algorithm for solving partial differential equations
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A proposal on machine learning via dynamical systems
- Stable architectures for deep neural networks
- Solving high-dimensional partial differential equations using deep learning
- Approximation by superpositions of a sigmoidal function
- Universal Approximation Power of Deep Residual Neural Networks Through the Lens of Control
- Deep learning via dynamical systems: an approximation perspective
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