Designing rotationally invariant neural networks from PDEs and variational methods
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Publication:2168880
DOI10.1007/s40687-022-00339-xOpenAlexW3198150903WikidataQ114218936 ScholiaQ114218936MaRDI QIDQ2168880
Matthias Augustin, Tobias Alt, Karl Schrader, Joachim Weickert, Pascal Peter
Publication date: 26 August 2022
Published in: Research in the Mathematical Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.13993
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- The Little Engine that Could: Regularization by Denoising (RED)
- Parseval proximal neural networks
- Equivariant deep learning via morphological and linear scale space PDEs on the space of positions and orientations
- Diffusion, pre-smoothing and gradient descent
- Translating numerical concepts for PDEs into neural architectures
- CLIP: cheap Lipschitz training of neural networks
- Multi-frame super-resolution from noisy data
- Properties of higher order nonlinear diffusion filtering
- Integrodifferential equations for continuous multiscale wavelet shrinkage
- Relations between regularization and diffusion filtering
- Forward stability of ResNet and its variants
- Deep neural networks motivated by partial differential equations
- Residual networks as flows of diffeomorphisms
- A theoretical analysis of deep neural networks and parametric PDEs
- Nonlinear approximation and (deep) ReLU networks
- Approximation spaces of deep neural networks
- Deep neural network structures solving variational inequalities
- PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Fourth-order partial differential equations for noise removal
- A note on the gradient of a multi-image
- Image Denoising via Multiscale Nonlinear Diffusion Models
- Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time
- Solving ill-posed inverse problems using iterative deep neural networks
- Stable architectures for deep neural networks
- Learning partial differential equations via data discovery and sparse optimization
- Solving high-dimensional partial differential equations using deep learning
- Solving inverse problems using data-driven models
- Image Processing and Analysis
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