| Self-consuming generative models go MAD S Alemohammad, J Casco-Rodriguez, L Luzi, AI Humayun, H Babaei, ... International Conference on Learning Representations (ICLR), 2024 | 161 | 2024 |
| The importance of transfer learning in seismic modeling and imaging A Siahkoohi, M Louboutin, FJ Herrmann Geophysics 84 (6), A47-A52, 2019 | 94 | 2019 |
| Seismic data reconstruction with generative adversarial networks A Siahkoohi, R Kumar, F Herrmann 80th EAGE conference and exhibition 2018 2018 (1), 1-5, 2018 | 81 | 2018 |
| Surface-related multiple elimination with deep learning A Siahkoohi, DJ Verschuur, FJ Herrmann SEG International Exposition and Annual Meeting, 2019 | 60* | 2019 |
| Preconditioned training of normalizing flows for variational inference in inverse problems A Siahkoohi, G Rizzuti, M Louboutin, PA Witte, FJ Herrmann 3rd Symposium on Advances in Approximate Bayesian Inference, 2021 | 47* | 2021 |
| Reliable amortized variational inference with physics-based latent distribution correction A Siahkoohi, G Rizzuti, R Orozco, FJ Herrmann Geophysics 88 (3), R297-R322, 2023 | 36 | 2023 |
| Learned imaging with constraints and uncertainty quantification FJ Herrmann, A Siahkoohi, G Rizzuti NeurIPS 2019 Deep Inverse Workshop, 2019 | 32* | 2019 |
| Deep Bayesian inference for seismic imaging with tasks A Siahkoohi, G Rizzuti, FJ Herrmann Geophysics 87 (5), S281-S302, 2022 | 30* | 2022 |
| Parameterizing uncertainty by deep invertible networks: An application to reservoir characterization G Rizzuti, A Siahkoohi, PA Witte, FJ Herrmann SEG Technical Program Expanded Abstracts 2020, 1541-1545, 2020 | 27* | 2020 |
| A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification A Siahkoohi, G Rizzuti, F Herrmann EAGE 2020 Annual Conference & Exhibition Online 2020 (1), 1-5, 2020 | 26* | 2020 |
| Faster uncertainty quantification for inverse problems with conditional normalizing flows A Siahkoohi, G Rizzuti, PA Witte, FJ Herrmann arXiv preprint arXiv:2007.07985, 2020 | 26* | 2020 |
| Boomerang: Local sampling on image manifolds using diffusion models L Luzi, PM Mayer, J Casco-Rodriguez, A Siahkoohi, RG Baraniuk Transactions on Machine Learning Research, 2024 | 24 | 2024 |
| Uncertainty quantification in imaging and automatic horizon tracking—a Bayesian deep-prior based approach A Siahkoohi, G Rizzuti, FJ Herrmann SEG Technical Program Expanded Abstracts 2020, 1636-1640, 2020 | 23* | 2020 |
| Learned iterative solvers for the Helmholtz equation G Rizzuti, A Siahkoohi, FJ Herrmann 81st EAGE Conference and Exhibition 2019 2019 (1), 1-5, 2019 | 22 | 2019 |
| Conditional score-based diffusion models for Bayesian inference in infinite dimensions L Baldassari, A Siahkoohi, J Garnier, K Solna, MV de Hoop Advances in Neural Information Processing Systems 36, 2024 | 21 | 2024 |
| Deep-convolutional neural networks in prestack seismic: Two exploratory examples A Siahkoohi, M Louboutin, R Kumar, FJ Herrmann SEG Technical Program Expanded Abstracts 2018, 2196-2200, 2018 | 20 | 2018 |
| InvertibleNetworks.jl: A Julia package for scalable normalizing flows R Orozco, P Witte, M Louboutin, A Siahkoohi, G Rizzuti, B Peters, ... arXiv preprint arXiv:2312.13480, 2023 | 19* | 2023 |
| Neural network augmented wave-equation simulation A Siahkoohi, M Louboutin, FJ Herrmann arXiv preprint arXiv:1910.00925, 2019 | 18 | 2019 |
| Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators Z Yin, A Siahkoohi, M Louboutin, FJ Herrmann Second International Meeting for Applied Geoscience & Energy, 467-472, 2022 | 17* | 2022 |
| Learning by example: fast reliability-aware seismic imaging with normalizing flows A Siahkoohi, FJ Herrmann First International Meeting for Applied Geoscience & Energy, 1580-1585, 2021 | 17* | 2021 |
Felix J. HerrmannProfessor Schools of Earth and Atmospheric Sciences, Computational Science and EngineeringVerified email at gatech.edu