Ali Siahkoohi | DeepAI

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∙ 05/28/2023

Conditional score-based diffusion models for Bayesian inference in infinite dimensions

Since their first introduction, score-based diffusion models (SDMs) have...

0 Lorenzo Baldassari, et al. ∙

∙ 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...

0 Ali Siahkoohi, et al. ∙

∙ 05/15/2023

Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics

We present an iterative framework to improve the amortized approximation...

0 Rafael Orozco, et al. ∙

∙ 04/12/2023

Learned multiphysics inversion with differentiable programming and machine learning

We present the Seismic Laboratory for Imaging and Modeling/Monitoring (S...

0 Mathias Louboutin, et al. ∙

∙ 03/06/2023

Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification

We present a novel approach to transcranial ultrasound computed tomograp...

0 Rafael Orozco, et al. ∙

∙ 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...

0 Ali Siahkoohi, et al. ∙

∙ 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...

0 Lorenzo Luzi, et al. ∙

∙ 07/24/2022

Reliable amortized variational inference with physics-based latent distribution correction

Bayesian inference for high-dimensional inverse problems is challenged b...

3 Ali Siahkoohi, et al. ∙

∙ 07/05/2022

Ultra-Low-Bitrate Speech Coding with Pretrained Transformers

Speech coding facilitates the transmission of speech over low-bandwidth ...

0 Ali Siahkoohi, et al. ∙

∙ 03/28/2022

Accelerating innovation with software abstractions for scalable computational geophysics

We present the SLIM (https://github.com/slimgroup) open-source software ...

0 Mathias Louboutin, et al. ∙

∙ 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...

0 Ziyi Yin, et al. ∙

∙ 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...

0 Ali Siahkoohi, et al. ∙

∙ 10/10/2021

Deep Bayesian inference for seismic imaging with tasks

We propose to use techniques from Bayesian inference and deep neural net...

10 Ali Siahkoohi, et al. ∙

∙ 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...

0 Mathias Louboutin, et al. ∙

∙ 04/13/2021

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

Uncertainty quantification provides quantitative measures on the reliabi...

0 Ali Siahkoohi, et al. ∙

∙ 01/11/2021

Preconditioned training of normalizing flows for variational inference in inverse problems

Obtaining samples from the posterior distribution of inverse problems wi...

8 Ali Siahkoohi, et al. ∙

∙ 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...

0 Ali Siahkoohi, et al. ∙

∙ 04/16/2020

Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization

Uncertainty quantification for full-waveform inversion provides a probab...

0 Gabrio Rizzuti, et al. ∙

∙ 04/15/2020

Transfer learning in large-scale ocean bottom seismic wavefield reconstruction

Achieving desirable receiver sampling in ocean bottom acquisition is oft...

0 Mi Zhang, et al. ∙

∙ 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...

0 Ali Siahkoohi, et al. ∙

∙ 01/13/2020

A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification

Uncertainty quantification is essential when dealing with ill-conditione...

0 Ali Siahkoohi, et al. ∙

∙ 09/27/2019

Neural network augmented wave-equation simulation

Accurate forward modeling is important for solving inverse problems. An ...

14 Ali Siahkoohi, et al. ∙

∙ 09/13/2019

Learned imaging with constraints and uncertainty quantification

We outline new approaches to incorporate ideas from convolutional networ...

0 Felix J. Herrmann, et al. ∙