Biblio
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Author Title Type [ Year
] Filters: Author is Gabrio Rizzuti and Keyword is deep learning [Clear All Filters]
, “InvertibleNetworks.jl: A Julia package for scalable normalizing flows”, Journal of Open Source Software, vol. 9, 2024.
, “Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification”, in SPIE Medical Imaging Conference, 2023.
, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, vol. 42, pp. 452-516, 2023.
, “Adjoint operators as summary functions in amortized Bayesian inference frameworks”, ML4SEISMIC Partners Meeting. 2022.
, “Reliable amortized variational inference with conditional normalizing flows via physics-based latent distribution correction”, in IMAGE Workshop on Subsurface Uncertainty Description and Estimation - Moving Away from Single Prediction with Distribution Learning, 2022.
, “InvertibleNetworks.jl - Memory efficient deep learning in Julia”, in JuliaCon, 2021.
, “Multifidelity conditional normalizing flows for physics-guided Bayesian inference”, 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.
, “Unsupervised data-guided uncertainty analysis in imaging and horizon tracking”, in SIAM Texas-Louisiana, 2020.
, “Learned imaging with constraints and uncertainty quantification”, HotCSE Seminar. 2019.
, “Learned imaging with constraints and uncertainty quantification”, in Neural Information Processing Systems (NeurIPS), 2019.