Search Results for author:
Found 14 papers, 8 papers with code
InvertibleNetworks.jl: A Julia package for scalable normalizing flows
InvertibleNetworks. jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions.
Learned multiphysics inversion with differentiable programming and machine learning
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e. g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations.
Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification
Our method combines physics-informed methods and data-driven methods to accelerate the reconstruction of the final image.
Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast
Motion artifacts often spoil the radiological interpretation of MR images, and in the most severe cases the scan needs be repeated, with additional costs for the provider.
Reliable amortized variational inference with physics-based latent distribution correction
While generic and applicable to other inverse problems, by means of a linearized seismic imaging example, we show that our correction step improves the robustness of amortized variational inference with respect to changes in the number of seismic sources, noise variance, and shifts in the prior distribution.
Photoacoustic imaging with conditional priors from normalizing flows
For many ill-posed inverse problems, such as photoacoustic imaging, the uncertainty of the solution is highly affected by measurement noise and data incompleteness (due, for example, to limited aperture).
Deep Bayesian inference for seismic imaging with tasks
We propose to use techniques from Bayesian inference and deep neural networks to translate uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as horizon tracking.
Preconditioned training of normalizing flows for variational inference in inverse problems
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneous Earth.
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
In inverse problems, we often have access to data consisting of paired samples $(x, y)\sim p_{X, Y}(x, y)$ where $y$ are partial observations of a physical system, and $x$ represents the unknowns of the problem.
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution with respect to the starting model and data noise.
Weak deep priors for seismic imaging
The chief advantage of this approach is that the updates for the CNN weights do not involve the modeling operator, and become relatively cheap.
Geophysics Image and Video Processing
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach
In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result.
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification
Uncertainty quantification is essential when dealing with ill-conditioned inverse problems due to the inherent nonuniqueness of the solution.
Learned imaging with constraints and uncertainty quantification
We outline new approaches to incorporate ideas from deep learning into wave-based least-squares imaging.
