Ali Siahkoohi

Orcid: 0000-0001-8779-2247

According to our database1, Ali Siahkoohi authored at least 30 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024
Boomerang: Local sampling on image manifolds using diffusion models.
Trans. Mach. Learn. Res., 2024

Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems.
CoRR, 2024

Removing Bias from Maximum Likelihood Estimation with Model Autophagy.
CoRR, 2024

ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems.
CoRR, 2024

Self-Consuming Generative Models Go MAD.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Titan: Bringing the Deep Image Prior to Implicit Representations.
Proceedings of the IEEE International Conference on Acoustics, 2024

2023
InvertibleNetworks.jl: A Julia package for scalable normalizing flows.
CoRR, 2023

Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders.
CoRR, 2023

Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics.
CoRR, 2023

Learned multiphysics inversion with differentiable programming and machine learning.
CoRR, 2023

Conditional score-based diffusion models for Bayesian inference in infinite dimensions.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification.
Proceedings of the Medical Imaging 2023: Image Processing, 2023

Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification.
Proceedings of the Medical Imaging with Deep Learning, 2023

Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data.
Proceedings of the International Conference on Machine Learning, 2023

2022
Reliable amortized variational inference with physics-based latent distribution correction.
CoRR, 2022

Accelerating innovation with software abstractions for scalable computational geophysics.
CoRR, 2022

Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators.
CoRR, 2022

Velocity continuation with Fourier neural operators for accelerated uncertainty quantification.
CoRR, 2022

Ultra-Low-Bitrate Speech Coding with Pretrained Transformers.
Proceedings of the 23rd Annual Conference of the International Speech Communication Association, 2022

2021
Low-memory stochastic backpropagation with multi-channel randomized trace estimation.
CoRR, 2021

Learning by example: fast reliability-aware seismic imaging with normalizing flows.
CoRR, 2021

Preconditioned training of normalizing flows for variational inference in inverse problems.
CoRR, 2021

2020
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows.
CoRR, 2020

Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization.
CoRR, 2020

Transfer learning in large-scale ocean bottom seismic wavefield reconstruction.
CoRR, 2020

Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach.
CoRR, 2020

A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification.
CoRR, 2020

2019
Neural network augmented wave-equation simulation.
CoRR, 2019

Learned imaging with constraints and uncertainty quantification.
CoRR, 2019

2011
A content-based digital image watermarking algorithm robust against JPEG compression.
Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology, 2011


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