research
∙
05/28/2023
Conditional score-based diffusion models for Bayesian inference in infinite dimensions
Since their first introduction, score-based diffusion models (SDMs) have...
share
research
∙
05/25/2023
Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders
Unsupervised source separation involves unraveling an unknown set of sou...
research
∙
05/15/2023
Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics
We present an iterative framework to improve the amortized approximation...
research
∙
04/12/2023
Learned multiphysics inversion with differentiable programming and machine learning
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (S...
research
∙
03/06/2023
Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification
We present a novel approach to transcranial ultrasound computed tomograp...
research
∙
01/27/2023
Unearthing InSights into Mars: unsupervised source separation with limited data
Source separation entails the ill-posed problem of retrieving a set of s...
research
∙
10/21/2022
Boomerang: Local sampling on image manifolds using diffusion models
Diffusion models can be viewed as mapping points in a high-dimensional l...
research
∙
07/24/2022
Reliable amortized variational inference with physics-based latent distribution correction
Bayesian inference for high-dimensional inverse problems is challenged b...
research
∙
07/05/2022
Ultra-Low-Bitrate Speech Coding with Pretrained Transformers
Speech coding facilitates the transmission of speech over low-bandwidth ...
research
∙
03/28/2022
Accelerating innovation with software abstractions for scalable computational geophysics
We present the SLIM (https://github.com/slimgroup) open-source software ...
research
∙
03/27/2022
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
Seismic monitoring of carbon storage sequestration is a challenging prob...
research
∙
03/27/2022
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
Seismic imaging is an ill-posed inverse problem that is challenged by no...
research
∙
10/10/2021
Deep Bayesian inference for seismic imaging with tasks
We propose to use techniques from Bayesian inference and deep neural net...
research
∙
06/13/2021
Low-memory stochastic backpropagation with multi-channel randomized trace estimation
Thanks to the combination of state-of-the-art accelerators and highly op...
research
∙
04/13/2021
Learning by example: fast reliability-aware seismic imaging with normalizing flows
Uncertainty quantification provides quantitative measures on the reliabi...
research
∙
01/11/2021
Preconditioned training of normalizing flows for variational inference in inverse problems
Obtaining samples from the posterior distribution of inverse problems wi...
research
∙
07/15/2020
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
In inverse problems, we often have access to data consisting of paired s...
research
∙
04/16/2020
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
Uncertainty quantification for full-waveform inversion provides a probab...
research
∙
04/15/2020
Transfer learning in large-scale ocean bottom seismic wavefield reconstruction
Achieving desirable receiver sampling in ocean bottom acquisition is oft...
research
∙
04/01/2020
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach
In inverse problems, uncertainty quantification (UQ) deals with a probab...
research
∙
01/13/2020
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification
Uncertainty quantification is essential when dealing with ill-conditione...
research
∙
09/27/2019
Neural network augmented wave-equation simulation
Accurate forward modeling is important for solving inverse problems. An ...
research
∙
09/13/2019