Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification | Paper Citation Record

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Unresolved cited work

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification SIAM, 2002

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification SIAM, 2010

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Deep convolutional neural network for inverse problems in imaging

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Image reconstruction by domain-transform manifold learning

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This paper cites Plug-and-play priors for model based reconstruction.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Plug-and-play priors for model based reconstruction

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This paper cites The little engine that could: Regularization by denoising (RED).

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification The little engine that could: Regularization by denoising (RED)

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This paper cites Learning regularization functionals—a supervised training approach.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Learning regularization functionals—a supervised training approach

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This paper cites Adversarial Regularizers in Inverse Problems.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Adversarial Regularizers in Inverse Problems

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This paper cites Learned Primal-Dual Reconstruction.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Learned Primal-Dual Reconstruction

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing

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This paper cites Good Things Come in Pairs: Paired Autoencoders for Inverse Problems.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Good Things Come in Pairs: Paired Autoencoders for Inverse Problems

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This paper cites Auto-Encoding Variational Bayes.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Auto-Encoding Variational Bayes

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Reducing the dimensionality of data with neural networks

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This paper cites Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

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This paper cites Generalized Denoising Auto-Encoders as Generative Models.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Generalized Denoising Auto-Encoders as Generative Models

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This paper cites Variational Autoencoder Inverse Mapper: An End-to-End Deep Learning Frame- work for Inverse Problems.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Variational Autoencoder Inverse Mapper: An End-to-End Deep Learning Frame- work for Inverse Problems

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification NETT: Solving Inverse Problems with Deep Neural Networks

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This paper cites Paired autoencoders for likelihood-free estimation in inverse problems.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Paired autoencoders for likelihood-free estimation in inverse problems

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This paper cites A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Latent Twins

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Diffusion posterior sampling for general noisy inverse problems

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This paper cites Solving inverse problems in medical imaging with score-based generative models.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Solving inverse problems in medical imaging with score-based generative models

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Denoising diffusion restoration models

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Inverse problems: a Bayesian perspective

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification The restricted isometry property and its implications for compressed sensing

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification A variable projection method for large-scale inverse prob- lems with l1 regularization

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Fast L1-regularized EEG source localization using variable projection

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Multilayer feedforward networks are universal approximators

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification SPARSE L1-AUTOENCODERS FOR SCIENTIFIC DATA COMPRESSION

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Murphy.Probabilistic Machine Learning: An introduction

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification An Introduction to Variational Autoencoders

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Fixing a Broken ELBO

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Variational Sparse Coding

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Boink and Christoph Brune.Learned SVD: solving inverse problems via hybrid autoencoding

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Bayesian Variable Selection in Linear Regression

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Wasserstein Auto-Encoders

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Flow-Based Models

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Multilayer feedforward networks are universal approximators

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Blitzstein and Jessica Hwang.Introduction to Probability Second Edition

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Chapter 6: Filtered Back-Projection

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification com / cetmann / pytorch - primaldual

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Learning Low-Rank Approximation for CNNs

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Uncertainty quantification in time-lapse seismic imaging: a full-waveform approach

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification VAE with a VampPrior

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification A review of uncertainty quantification in deep learning: Techniques, applications and challenges

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Multi-task Sparse Learning with Beta Process Prior for Action Recognition

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Bayesian and L1 Approaches to Sparse Unsupervised Learning

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 59

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Learning Sparse Neural Networks through $L_0$ Regularization

Reference 61

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Variational Sparse Coding with Learned Thresholding

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Unresolved cited work

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Discrete Variational Autoencoders

Reference 64

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification L-VAE: Variational Auto-Encoder with Learnable Beta for Disentangled Representation

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Challenging Common Assumptions in the Unsupervised Learning of Dis- entangled Representations

Reference 66

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Bishop.Pattern Recognition and Machine Learning (Information Science and Statis- tics)

Reference 67

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Understanding disentangling in $\beta$-VAE

Reference 68

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Adam: A Method for Stochastic Optimization

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Robert.Bayesian Essentials with R

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification This motivates structure in the latent representations

Reference 74

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Reference 75

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Reference 76

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Unresolved cited work

Reference 77

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Unresolved cited work

Reference 78

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification B Hyperparameter Selection The vsPAIR framework requires tuning several hyperparameters for training

Reference 79

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Unresolved cited work

Reference 700

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Neural Discrete Representation Learning

Reference 2018

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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification Learned SVD: solving inverse problems via hybrid autoencoding

Reference 2020

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