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101. Proximal Residual Flows for Bayesian Inverse Problems

102. Compressive Learning of Deep Regularization for Denoising

103. Graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet

104. Resolution-Invariant Image Classification Based on Fourier Neural Operators

105. Graph Laplacian for Semi-supervised Learning

106. Learning Posterior Distributions in Underdetermined Inverse Problems

107. Limited Electrodes Models in Electrical Impedance Tomography Reconstruction

108. EmNeF: Neural Fields for Embedded Variational Problems in Imaging

109. Prony-Based Super-Resolution Phase Retrieval of Sparse, Multidimensional Signals

110. Regularized Material Decomposition for K-edge Separation in Hyperspectral Computed Tomography

111. Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting

112. Explicit Diffusion of Gaussian Mixture Model Based Image Priors

113. On Trainable Multiplicative Noise Removal Models

114. Theoretical Foundations for Pseudo-Inversion of Nonlinear Operators

115. A Frame Decomposition of the Funk-Radon Transform

116. Quaternary Image Decomposition with Cross-Correlation-Based Multi-parameter Selection

117. Surface Reconstruction from Noisy Point Cloud Using Directional G-norm

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