ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2104.06255
  4. Cited By
Learning by example: fast reliability-aware seismic imaging with
  normalizing flows

Learning by example: fast reliability-aware seismic imaging with normalizing flows

13 April 2021
Ali Siahkoohi
Felix J. Herrmann
    OOD
ArXiv (abs)PDFHTML

Papers citing "Learning by example: fast reliability-aware seismic imaging with normalizing flows"

15 / 15 papers shown
Title
Preconditioned training of normalizing flows for variational inference
  in inverse problems
Preconditioned training of normalizing flows for variational inference in inverse problems
Ali Siahkoohi
G. Rizzuti
M. Louboutin
Philipp A. Witte
Felix J. Herrmann
102
32
0
11 Jan 2021
Faster Uncertainty Quantification for Inverse Problems with Conditional
  Normalizing Flows
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
Ali Siahkoohi
G. Rizzuti
Philipp A. Witte
Felix J. Herrmann
AI4CE
48
16
0
15 Jul 2020
Parameterizing uncertainty by deep invertible networks, an application
  to reservoir characterization
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
G. Rizzuti
Ali Siahkoohi
Philipp A. Witte
Felix J. Herrmann
UQCV
65
20
0
16 Apr 2020
Uncertainty quantification in imaging and automatic horizon tracking: a
  Bayesian deep-prior based approach
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach
Ali Siahkoohi
G. Rizzuti
Felix J. Herrmann
65
20
0
01 Apr 2020
A deep-learning based Bayesian approach to seismic imaging and
  uncertainty quantification
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification
Ali Siahkoohi
G. Rizzuti
Felix J. Herrmann
UQCVBDL
69
22
0
13 Jan 2020
Invert to Learn to Invert
Invert to Learn to Invert
P. Putzky
Max Welling
62
76
0
25 Nov 2019
Learned imaging with constraints and uncertainty quantification
Learned imaging with constraints and uncertainty quantification
Felix J. Herrmann
Ali Siahkoohi
G. Rizzuti
UQCV
75
23
0
13 Sep 2019
HINT: Hierarchical Invertible Neural Transport for Density Estimation
  and Bayesian Inference
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
Jakob Kruse
Gianluca Detommaso
Ullrich Kothe
Robert Scheichl
82
45
0
25 May 2019
Fully Hyperbolic Convolutional Neural Networks
Fully Hyperbolic Convolutional Neural Networks
Keegan Lensink
Bas Peters
E. Haber
MedIm
75
19
0
24 May 2019
Stochastic seismic waveform inversion using generative adversarial
  networks as a geological prior
Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
L. Mosser
O. Dubrule
M. Blunt
GANAI4CE
113
208
0
10 Jun 2018
Stein Variational Gradient Descent as Gradient Flow
Stein Variational Gradient Descent as Gradient Flow
Qiang Liu
OT
98
277
0
25 Apr 2017
Faster Eigenvector Computation via Shift-and-Invert Preconditioning
Faster Eigenvector Computation via Shift-and-Invert Preconditioning
Dan Garber
Laurent Dinh
Chi Jin
Jascha Narain Sohl-Dickstein
Samy Bengio
Praneeth Netrapalli
Aaron Sidford
277
3,722
0
26 May 2016
Variational Inference: A Review for Statisticians
Variational Inference: A Review for Statisticians
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
BDL
298
4,812
0
04 Jan 2016
Variational Inference with Normalizing Flows
Variational Inference with Normalizing Flows
Danilo Jimenez Rezende
S. Mohamed
DRLBDL
322
4,197
0
21 May 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.1K
150,364
0
22 Dec 2014
1