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Tristan van Leeuwen, Mark Schmidt, Michael P. Friedlander, and Felix J. Herrmann, “A hybrid stocahstic-deterministic method for waveform inversion”, in AMP, 2011.
Mark Schmidt, “Hybrid stochastic-deterministic methods”, SINBAD Fall consortium talks. SINBAD, 2010.
Tristan van Leeuwen, Felix J. Herrmann, Mark Schmidt, and Michael P. Friedlander, “A hybrid stochastic-deterministic optimization method for waveform inversion”, in EAGE Annual Conference Proceedings, 2011.
I
Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “The importance of transfer learning in seismic modeling and imaging”, Geophysics, 2019.
Rafael Orozco, Philipp A. Witte, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Bas Peters, and Felix J. Herrmann, “InvertibleNetworks.jl: A Julia package for scalable normalizing flows”. 2023.
Philipp A. Witte, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Bas Peters, and Felix J. Herrmann, “InvertibleNetworks.jl - Memory efficient deep learning in Julia”, in JuliaCon, 2021.
J
Brendan R. Smithyman, Bas Peters, Bryan DeVault, and Felix J. Herrmann, “Joint full-waveform inversion of on-land surface and VSP data from the Permian Basin”, UBC, TR-EOAS-2014-4, 2014.
Ziyi Yin, Mathias Louboutin, Philipp A. Witte, Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, Henryk Modzelewski, and Felix J. Herrmann, “Julia for Geoscience”, Transform. 2022.
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Shashin Sharan, “Large scale high-frequency seismic wavefield reconstruction, acquisition via rank minimization and sparsity-promoting source estimation”, Georgia Institute of Technology, Atlanta, 2020.
Shashin Sharan, Yijun Zhang, Oscar Lopez, and Felix J. Herrmann, “Large scale high-frequency wavefield reconstruction with recursively weighted matrix factorizations”, TR-CSE-2020-4, 2020.
Ziyi Yin, Ali Siahkoohi, Mathias Louboutin, and Felix J. Herrmann, “Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators”, in International Meeting for Applied Geoscience and Energy Expanded Abstracts, 2022.
Felix J. Herrmann, Ali Siahkoohi, and Gabrio Rizzuti, “Learned imaging with constraints and uncertainty quantification”, in Neural Information Processing Systems (NeurIPS), 2019.
Felix J. Herrmann, Ali Siahkoohi, and Gabrio Rizzuti, “Learned imaging with constraints and uncertainty quantification”, HotCSE Seminar. 2019.
Gabrio Rizzuti, Ali Siahkoohi, and Felix J. Herrmann, “Learned iterative solvers for the Helmholtz equation”, in EAGE Annual Conference Proceedings, 2019.
Mathias Louboutin, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, Olav Møyner, Gerard J. Gorman, and Felix J. Herrmann, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, vol. 42, pp. 452-516, 2023.
Mathias Louboutin, Rafael Orozco, Ali Siahkoohi, and Felix J. Herrmann, “Learned non-linear simultenous source and corresponding supershot for seismic imaging.”, in International Meeting for Applied Geoscience and Energy, 2023.
Felix J. Herrmann, Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti, Philipp A. Witte, and Mathias Louboutin, “Learned wave-based imaging - variational inference at scale”, in Delft, 2021.
Ali Siahkoohi and Felix J. Herrmann, “Learning by example: fast reliability-aware seismic imaging with normalizing flows”, in SEG Technical Program Expanded Abstracts, 2021, pp. 1580-1585.
Xiang Li, Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Brendan R. Smithyman, Haneet Wason, and Felix J. Herrmann, “Lessons learned from Chevron Gulf of Mexico data set”, SINBAD Fall consortium talks. SINBAD, 2013.
Rajiv Kumar, Felix Oghenekohwo, Shashin Sharan, Haneet Wason, and Felix J. Herrmann, “Low-cost, randomized 3D towed-marine time-lapse seismic acquisition”, SINBAD Fall consortium talks. SINBAD, 2016.
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, and Felix J. Herrmann, “Low-cost uncertainty quantification for large-scale inverse problems”, ML4SEISMIC Partners Meeting. 2022.
Mathias Louboutin, Ali Siahkoohi, Rongrong Wang, and Felix J. Herrmann, “Low-memory stochastic backpropagation with multi-channel randomized trace estimation”, TR-CSE-2021-1, 2021.
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Peyman P. Moghaddam, Felix J. Herrmann, and Christiaan C. Stolk, “Migration amplitude recovery using curvelets”, in CSEG Annual Conference Proceedings, 2007.
Felix J. Herrmann, Mathias Louboutin, and Ali Siahkoohi, “ML@scale using randomized linear algebra”, in Microsoft, 2021.
Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti, Philipp A. Witte, Mathias Louboutin, and Felix J. Herrmann, “Multifidelity conditional normalizing flows for physics-guided Bayesian inference”, ML4SEISMIC Partners Meeting. 2021.

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