A content-based digital image watermarking algorithm robust against JPEG compression. - Amir Najafi 002; Ali Siahkoohi; Mohammad B. Shamsollahi (ISSPIT, 2011)

Learned imaging with constraints and uncertainty quantification. - Felix J. Herrmann; Ali Siahkoohi; Gabrio Rizzuti (CoRR, 2019)

Neural network augmented wave-equation simulation. - Ali Siahkoohi; Mathias Louboutin; Felix J. Herrmann (CoRR, 2019)

A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification. - Ali Siahkoohi; Gabrio Rizzuti; Felix J. Herrmann (CoRR, 2020)

Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach. - Ali Siahkoohi; Gabrio Rizzuti; Felix J. Herrmann (CoRR, 2020)

Transfer learning in large-scale ocean bottom seismic wavefield reconstruction. - Mi Zhang; Ali Siahkoohi; Felix J. Herrmann (CoRR, 2020)

Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization. - Gabrio Rizzuti; Ali Siahkoohi; Philipp A. Witte; Felix J. Herrmann (CoRR, 2020)

Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows. - Ali Siahkoohi; Gabrio Rizzuti; Philipp A. Witte; Felix J. Herrmann (CoRR, 2020)

Preconditioned training of normalizing flows for variational inference in inverse problems. - Ali Siahkoohi; Gabrio Rizzuti; Mathias Louboutin; Philipp A. Witte; Felix J. Herrmann (CoRR, 2021)

Learning by example: fast reliability-aware seismic imaging with normalizing flows. - Ali Siahkoohi; Felix J. Herrmann (CoRR, 2021)

Low-memory stochastic backpropagation with multi-channel randomized trace estimation. - Mathias Louboutin; Ali Siahkoohi; Rongrong Wang; Felix J. Herrmann (CoRR, 2021)

Ultra-Low-Bitrate Speech Coding with Pretrained Transformers. - Ali Siahkoohi; Michael Chinen; Tom Denton; W. Bastiaan Kleijn; Jan Skoglund (INTERSPEECH, 2022)

Velocity continuation with Fourier neural operators for accelerated uncertainty quantification. - Ali Siahkoohi; Mathias Louboutin; Felix J. Herrmann (CoRR, 2022)

Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators. - Ziyi Yin 002; Ali Siahkoohi; Mathias Louboutin; Felix J. Herrmann (CoRR, 2022)

Accelerating innovation with software abstractions for scalable computational geophysics. - Mathias Louboutin; Philipp A. Witte; Ali Siahkoohi; Gabrio Rizzuti; Ziyi Yin 002; Rafael Orozco; Felix J. Herrmann (CoRR, 2022)

Reliable amortized variational inference with physics-based latent distribution correction. - Ali Siahkoohi; Gabrio Rizzuti; Rafael Orozco; Felix J. Herrmann (CoRR, 2022)

Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data. - Ali Siahkoohi; Rudy Morel; Maarten V. de Hoop; Erwan Allys; Grégory Sainton; Taichi Kawamura (ICML, 2023)

Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification. - Rafael Orozco; Mathias Louboutin; Ali Siahkoohi; Gabrio Rizzuti; Tristan van Leeuwen; Felix Johan Herrmann (MIDL, 2023)

Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification. - Rafael Orozco; Ali Siahkoohi; Gabrio Rizzuti; Tristan van Leeuwen; Felix J. Herrmann (Image Processing, 2023)

Conditional score-based diffusion models for Bayesian inference in infinite dimensions. - Lorenzo Baldassari; Ali Siahkoohi; Josselin Garnier; Knut Solna; Maarten V. de Hoop (NeurIPS, 2023)

Learned multiphysics inversion with differentiable programming and machine learning. - Mathias Louboutin; Ziyi Yin 002; Rafael Orozco; Thomas J. Grady II; Ali Siahkoohi; Gabrio Rizzuti; Philipp A. Witte; Olav Møyner; Gerard J. Gorman; Felix J. Herrmann (CoRR, 2023)

Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics. - Rafael Orozco; Ali Siahkoohi; Mathias Louboutin; Felix J. Herrmann (CoRR, 2023)

Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders. - Ali Siahkoohi; Rudy Morel; Randall Balestriero; Erwan Allys; Grégory Sainton; Taichi Kawamura; Maarten V. de Hoop (CoRR, 2023)

Titan: Bringing the Deep Image Prior to Implicit Representations. - Lorenzo Luzi; Daniel LeJeune; Ali Siahkoohi; Sina Alemohammad; Vishwanath Saragadam; Hossein Babaei; Naiming Liu; Zichao Wang 001; Richard G. Baraniuk (ICASSP, 2024)

Self-Consuming Generative Models Go MAD. - Sina Alemohammad; Josue Casco-Rodriguez; Lorenzo Luzi; Ahmed Imtiaz Humayun; Hossein Babaei; Daniel LeJeune; Ali Siahkoohi; Richard G. Baraniuk (ICLR, 2024)

ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems. - Rafael Orozco; Ali Siahkoohi; Mathias Louboutin; Felix J. Herrmann (CoRR, 2024)

Removing Bias from Maximum Likelihood Estimation with Model Autophagy. - Paul M. Mayer; Lorenzo Luzi; Ali Siahkoohi; Don H. Johnson; Richard G. Baraniuk (CoRR, 2024)

Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems. - Lorenzo Baldassari; Ali Siahkoohi; Josselin Garnier; Knut Solna; Maarten V. de Hoop (CoRR, 2024)

InvertibleNetworks.jl: A Julia package for scalable normalizing flows. - Rafael Orozco; Philipp A. Witte; Mathias Louboutin; Ali Siahkoohi; Gabrio Rizzuti; Bas Peters; Felix J. Herrmann (J. Open Source Softw., 2024)

Boomerang: Local sampling on image manifolds using diffusion models. - Lorenzo Luzi; Paul M. Mayer; Josue Casco-Rodriguez; Ali Siahkoohi; Richard G. Baraniuk (Trans. Mach. Learn. Res., 2024)