Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Siahkoohi, Ali"'
This work introduces a sampling method capable of solving Bayesian inverse problems in function space. It does not assume the log-concavity of the likelihood, meaning that it is compatible with nonlinear inverse problems. The method leverages the rec
Externí odkaz:
http://arxiv.org/abs/2405.15676
We propose autophagy penalized likelihood estimation (PLE), an unbiased alternative to maximum likelihood estimation (MLE) which is more fair and less susceptible to model autophagy disorder (madness). Model autophagy refers to models trained on thei
Externí odkaz:
http://arxiv.org/abs/2405.13977
Due to their uncertainty quantification, Bayesian solutions to inverse problems are the framework of choice in applications that are risk averse. These benefits come at the cost of computations that are in general, intractable. New advances in machin
Externí odkaz:
http://arxiv.org/abs/2405.05398
Autor:
Orozco, Rafael, Witte, Philipp, Louboutin, Mathias, Siahkoohi, Ali, Rizzuti, Gabrio, Peters, Bas, Herrmann, Felix J.
InvertibleNetworks.jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions. This package excels in memory efficiency by leveraging the inheren
Externí odkaz:
http://arxiv.org/abs/2312.13480
Autor:
Zhang, Yijun, Yin, Ziyi, López, Oscar, Siahkoohi, Ali, Louboutin, Mathias, Herrmann, Felix J.
The massive cost of 3D acquisition calls for methods to reduce the number of receivers by designing optimal receiver sampling masks. Recent studies on 2D seismic showed that maximizing the spectral gap of the subsampling mask leads to better wavefiel
Externí odkaz:
http://arxiv.org/abs/2311.02298
Autor:
Alemohammad, Sina, Casco-Rodriguez, Josue, Luzi, Lorenzo, Humayun, Ahmed Imtiaz, Babaei, Hossein, LeJeune, Daniel, Siahkoohi, Ali, Baraniuk, Richard G.
Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this process creates an autophagous (self-consuming) loop whose properties
Externí odkaz:
http://arxiv.org/abs/2307.01850
Since their initial introduction, score-based diffusion models (SDMs) have been successfully applied to solve a variety of linear inverse problems in finite-dimensional vector spaces due to their ability to efficiently approximate the posterior distr
Externí odkaz:
http://arxiv.org/abs/2305.19147
Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders
Autor:
Siahkoohi, Ali, Morel, Rudy, Balestriero, Randall, Allys, Erwan, Sainton, Grégory, Kawamura, Taichi, de Hoop, Maarten V.
Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is inherently ill-po
Externí odkaz:
http://arxiv.org/abs/2305.16189
We present an iterative framework to improve the amortized approximations of posterior distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled gradient descent methods and is theoretically grounded in maximally i
Externí odkaz:
http://arxiv.org/abs/2305.08733
Autor:
Louboutin, Mathias, Yin, Ziyi, Orozco, Rafael, Grady II, Thomas J., Siahkoohi, Ali, Rizzuti, Gabrio, Witte, Philipp A., Møyner, Olav, Gorman, Gerard J., Herrmann, Felix J.
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regular
Externí odkaz:
http://arxiv.org/abs/2304.05592