Pages that link to "Item:Q5230520"

The following pages link to Solving inverse problems using data-driven models (Q5230520):

Displaying 50 items.

• High-resolution signal recovery via generalized sampling and functional principal component analysis (Q824321) (← links)
• Adversarially learned iterative reconstruction for imaging inverse problems (Q826227) (← links)
• Regularization by architecture: a deep prior approach for inverse problems (Q1988362) (← links)
• Inexact derivative-free optimization for bilevel learning (Q2036198) (← links)
• Constructive deep ReLU neural network approximation (Q2067309) (← links)
• Infinite-dimensional inverse problems with finite measurements (Q2069717) (← links)
• Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors (Q2080374) (← links)
• Estimating adsorption isotherm parameters in chromatography via a virtual injection promoting double feed-forward neural network (Q2082130) (← links)
• Purity assessment of pellets using deep learning (Q2086298) (← links)
• Compressive sensing and neural networks from a statistical learning perspective (Q2106482) (← links)
• Constrained and unconstrained deep image prior optimization models with automatic regularization (Q2111471) (← links)
• Deep learning architectures for nonlinear operator functions and nonlinear inverse problems (Q2113263) (← links)
• Fast Bayesian inversion for high dimensional inverse problems (Q2128074) (← links)
• Cauchy Markov random field priors for Bayesian inversion (Q2128078) (← links)
• Deep learning for inverse problems. Abstracts from the workshop held March 7--13, 2021 (hybrid meeting) (Q2131206) (← links)
• Deep microlocal reconstruction for limited-angle tomography (Q2134111) (← links)
• A new hybrid regularization scheme for removing salt and pepper noise (Q2140817) (← links)
• Model order reduction method based on (r)POD-ANNs for parameterized time-dependent partial differential equations (Q2158140) (← links)
• Neural networks for classification of strokes in electrical impedance tomography on a 3D head model (Q2167606) (← links)
• Designing rotationally invariant neural networks from PDEs and variational methods (Q2168880) (← links)
• An introduction to finite element methods for inverse coefficient problems in elliptic PDEs (Q2232366) (← links)
• Deep learning for inverse problems with unknown operator (Q2689599) (← links)
• A novel physics-informed framework for reconstruction of structural defects (Q2690004) (← links)
• Applied harmonic analysis and data science. Abstracts from the workshop held November 28 -- December 4, 2021 (hybrid meeting) (Q2693052) (← links)
• Data-consistent neural networks for solving nonlinear inverse problems (Q2697358) (← links)
• NESTANets: stable, accurate and efficient neural networks for analysis-sparse inverse problems (Q2700171) (← links)
• Accelerating Proximal Markov Chain Monte Carlo by Using an Explicit Stabilized Method (Q3296473) (← links)
• Multilevel Markov Chain Monte Carlo for Bayesian Inversion of Parabolic Partial Differential Equations under Gaussian Prior (Q4995109) (← links)
• The Gap between Theory and Practice in Function Approximation with Deep Neural Networks (Q4999396) (← links)
• Structure-preserving deep learning (Q5014474) (← links)
• High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion (Q5019933) (← links)
• Optimization with learning-informed differential equation constraints and its applications (Q5024338) (← links)
• Regularization theory of the analytic deep prior approach (Q5043664) (← links)
• WARPd: A Linearly Convergent First-Order Primal-Dual Algorithm for Inverse Problems with Approximate Sharpness Conditions (Q5043741) (← links)
• Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint (Q5052899) (← links)
• (Q5054608) (← links)
• Two-Layer Neural Networks with Values in a Banach Space (Q5055293) (← links)
• Deep Learning--Based Dictionary Learning and Tomographic Image Reconstruction (Q5056920) (← links)
• Discretization of parameter identification in PDEs using neural networks (Q5058109) (← links)
• Inverse problems on low-dimensional manifolds (Q5061379) (← links)
• Consistency of Bayesian inference with Gaussian process priors for a parabolic inverse problem (Q5062121) (← links)
• Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion (Q5062131) (← links)
• A Generative Variational Model for Inverse Problems in Imaging (Q5065477) (← links)
• A probabilistic approach to tomography and adjoint state methods, with an application to full waveform inversion in medical ultrasound (Q5071170) (← links)
• Task adapted reconstruction for inverse problems (Q5081802) (← links)
• An efficient algorithm to compute the X-ray transform (Q5093051) (← links)
• Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie (Q5094615) (← links)
• Bayesian Imaging with Data-Driven Priors Encoded by Neural Networks (Q5094622) (← links)
• Inverse medium scattering problems with Kalman filter techniques (Q5097568) (← links)
• Stein Variational Gradient Descent on Infinite-Dimensional Space and Applications to Statistical Inverse Problems (Q5102236) (← links)