Fetching the paper…
Reading the bibliography…
Characterizing statistical properties of solutions of inverse problems is essential for decision making.
Approximating conditional distributions by the mixed Edgeworth-saddlepoint expansion
B. V. Pedersen · 1979
Earlier work this paper cites.
Inverse problems as statistics
S. N. Evans and P. B. Stark · 2002
Earlier work this paper cites.
Can one use total variation prior for edge-preserving Bayesian inversion?
M. Lassas and S. Siltanen · 2004
Earlier work this paper cites.
On the optimality of conditional expectation as a Bregman predictor
A. Banerjee, X. Guo, and H. Wang · 2005
Earlier work this paper cites.
Image reconstruction and image quality evaluation for a 64-slice CT scanner with z-flying focal spot
T.G. Flohr, K. Stierstorfer, S. Ulzheimer, H Bruder, A.N. Primak, and C. H. McCollough · 2005
Earlier work this paper cites.
Statistical and Computational Inverse Problems
J. P. Kaipio and E. Somersalo · 2005
Earlier work this paper cites.
Reconstruction algorithms for computed tomography
C. Bontus and T. Köhler · 2009
Earlier work this paper cites.
Discretization-invariant Bayesian inversion and Besov space priors
M. Lassas, E. Saksman, and S. Siltanen · 2009
Earlier work this paper cites.
Optimal Transport: Old and New
C. Villani · 2009
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
Earlier work this paper cites.
Maximum a posteriori estimates in linear inverse problems with log-concave priors are proper Bayes estimators
M. Burger and F. Lucka · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Earlier work this paper cites.
Understanding Machine Learning: From Theory to Algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
Earlier work this paper cites.
Unbalanced optimal transport: Geometry and Kantorovich formulation
L. Chizat, G. Peyré, B. Schmitzer, and F.-X. Vialard · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Higher moments of Banach space valued random variables
S. Janson and S. Kaijser · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2016
Earlier work this paper cites.
The Bayesian approach to inverse problems
M. Dashti and A.M. Stuart · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
Earlier work this paper cites.
WaveNet: a deep convolutional neural network using directional wavelets for low-dose x-ray CT reconstruction
E. Kang, Min; J., and J. C. Ye · 2016
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2016
Cited alongside, same era.
TFG-207A-04: Overview of the low dose CT grand challenge
C. McCollough · 2016
Cited alongside, same era.
Plug & play generative networks: Conditional iterative generation of images in latent space
A. Nguyen, J. Clune, Y. Bengio, Y. Dosovitskiy, and J. Yosinski · 2016
Cited alongside, same era.
Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Cited alongside, same era.
On Bayesian inference for some statistical inverse problems with partial differential equations
R. Nickl · 2017
Later among the works it cites.
Maximum-a-posteriori estimation with Bayesian confidence regions
M. Pereyra · 2017
Later among the works it cites.
Loss-sensitive generative adversarial networks on Lipschitz densities
G.-L. Qi · 2017
Later among the works it cites.
Generative adversarial networks for noise reduction in low-dose CT
J. M. Wolterink, T. Leiner, M. A. Viergever, and I. Išgum · 2017
Later among the works it cites.
Sharpness-aware low dose CT denoising using conditional generative adversarial network
X. Yi and P. Babyn · 2017
Later among the works it cites.
Deep sets
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. Salakhutdinov, and A. Smola · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang · 2016
Cited alongside, same era.
Fast and flexible x-ray tomography using the astra toolbox
W. van Aarle, W. J. Palenstijn, J. Cant, E. Janssens, F. Bleichrodt, A. Dabravolski, J. De Beenhouwer, J. K. Batenburg, and J. Sijbers · 2016
Cited alongside, same era.
Deep ADMM-Net for compressive sensing MRI
Y. Yang, J. Sun, H. Li, and Z. Xu · 2016
Cited alongside, same era.
Operator discretization library (ODL), January 2017
J. Adler, H. Kohr, and O. Öktem · 2017
Cited alongside, same era.
Solving ill-posed inverse problems using iterative deep neural networks
J. Adler and O. Öktem · 2017
Cited alongside, same era.
Learning to solve inverse problems using Wasserstein loss
J. Adler, A. Ringh, O. Öktem, and J. Karlsson · 2017
Cited alongside, same era.
Wasserstein GAN
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Cited alongside, same era.
J. Adler and S. Lunz · 2018
Closest in time.
Task adapted reconstruction for inverse problems
J. Adler, S. Lunz, O. Verdier, C.-B. Schönlieb, and O. Öktem · 2018
Closest in time.
Learned primal-dual reconstruction
J. Adler and O. Öktem · 2018
Closest in time.
Do GANs learn the distribution? some theory and empirics
S. Arora, A. Risteski, and Y. Zhang · 2018
Closest in time.
Geometry and dynamics for Markov Chain Monte Carlo
A. Barp, F.-X. Briol, A. D. Kennedy, and M. Girolami · 2018
Closest in time.
Conditional neural processes
M. Garnelo, D. Rosenbaum, C. J. Maddison, T. Ramalho, D. Saxton, M. Shanahan, Y. W. Teh, D. J. Rezende, and A. S. M. Eslami · 2018
Closest in time.
Neural processes
M. Garnelo, J. Schwarz, D. Rosenbaum, F. Viola, D. J. Rezende, A. S. M. Eslami, and Y. W. Teh · 2018
Closest in time.
Deep D-bar: Real time electrical impedance tomography imaging with deep neural networks
S. J. Hamilton and A. Hauptmann · 2018
Closest in time.
Model-based learning for accelerated, limited-view 3-D photoacoustic tomography
A. Hauptmann, F. Lucka, M. Betcke, N. Huynh, J. Adler, B. Cox, P. Beard, S. Ourselin, and S. Arridge · 2018
Closest in time.
Progressive growing of GANs for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2018
Closest in time.
A probabilistic U-Net for segmentation of ambiguous images
S. A. A. Kohl, B. Romera-Paredes, C. Meyer, J. De Fauw, J. R. Ledsam, K. H. Maier-Hein, S. M. A. Eslami, D. J. Rezende, and O. Ronneberger · 2018
Closest in time.
Image transformer
N. Parmar, A. Vaswani, J. Uszkoreit, L. Kaiser, N. Shazeer, and A. Ku · 2018
Closest in time.
Image transformer
N. Parmar, A. Vaswani, J. Uszkoreit, L. Kaiser, N. Shazeer, A. Ku, and D. Tran · 2018
Closest in time.
Probabilistic approach to limited-data computed tomography reconstruction
Z. Purisha, C. Jidling, N. Wahlström, S. Särkkä, and T. B. Schön · 2018
Closest in time.
Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization
A. Repetti, M. Pereyra, and Y. Wiaux · 2018
Closest in time.
DAGAN: Deep De-Aliasing Generative Adversarial Networks for fast compressed sensing MRI reconstruction
G. Yang, S. Yu, H. Dong, G. Slabaugh, P. L. Dragotti, X. Ye, F. Liu, S. Arridge, J. Keegan, Y. Guo, and D. Firmin · 2018
Closest in time.