Biblio
] , “Time-lapse seismic survey design by maximizing the spectral gap”, ML4SEISMIC Partners Meeting. 2022.
, “Ultra-Low-Bitrate Speech Coding with Pretrained Transformers”, in Proceedings of INTERSPEECH, 2022.
, “Uncertainty-aware time-lapse CO$_2$ monitoring with learned end-to-end inversion”, ML4SEISMIC Partners Meeting. 2022.
, “Velocity continuation with Fourier neural operators for accelerated uncertainty quantification”, in International Meeting for Applied Geoscience and Energy Expanded Abstracts, 2022.
, “Wave-equation based inversion with amortized variational Bayesian inference”, in EAGE Annual Conference Proceedings, 2022, p. Session 2: Velocity model building and imaging (different domains).
, “Deep Bayesian Inference for Task-based Seismic Imaging”, in KAUST, 2021.
, “Enabling uncertainty quantification for seismic data pre-processing using normalizing flows (NF)—an interpolation example”, in SEG Technical Program Expanded Abstracts, 2021, pp. 1515-1519.
, “Fast and reliability-aware seismic imaging with conditional normalizing flows”, in Intelligent illumination of the Earth, 2021.
, “InvertibleNetworks.jl - Memory efficient deep learning in Julia”, in JuliaCon, 2021.
, “Learned wave-based imaging - variational inference at scale”, in Delft, 2021.
, “Learning by example: fast reliability-aware seismic imaging with normalizing flows”, in SEG Technical Program Expanded Abstracts, 2021, pp. 1580-1585.
, “Low-memory stochastic backpropagation with multi-channel randomized trace estimation”, TR-CSE-2021-1, 2021.
, “ML@scale using randomized linear algebra”, in Microsoft, 2021.
, “Multifidelity conditional normalizing flows for physics-guided Bayesian inference”, ML4SEISMIC Partners Meeting. 2021.
, “Photoacoustic Imaging with Conditional Priors from Normalizing Flows”, in Neural Information Processing Systems (NeurIPS), 2021.
, “Preconditioned training of normalizing flows for variational inference in inverse problems”, in 3rd Symposium on Advances in Approximate Bayesian Inference, 2021.
, “Seismic Velocity Inversion and Uncertainty Quantification Using Conditional Normalizing Flows”, in AGU Annual Meeting, 2021, p. U12A-03.
, “Uncertainty quantification in imaging and automatic horizon tracking—a Bayesian deep-prior based approach”, ML4SEISMIC Partners Meeting. 2021.
, “Variational inference for artifact removal of adjoint solutions in photoacoustic problems”, ML4SEISMIC Partners Meeting. 2021.
, “A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification”, in EAGE Annual Conference Proceedings, 2020.
, “A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification”, GT SEG Student Chapter. 2020.
, “Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows”, Georgia Institute of Technology, TR-CSE-2020-2, 2020.
, “Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization”, in SEG Technical Program Expanded Abstracts, 2020, pp. 1541-1545.
, “Seismic Imaging with Uncertainty Quantification: Sampling from the Posterior with Generative Networks”, in SIAM Conference on Imaging Science, 2020.
, “Transfer learning in large-scale ocean bottom seismic wavefield reconstruction”, in SEG Technical Program Expanded Abstracts, 2020, pp. 1666-1670.