Graph structure learning with interpretable Bayesian neural networks
M Wasserman, G Mateos - arXiv preprint arXiv:2406.14786, 2024 - arxiv.org
Graphs serve as generic tools to encode the underlying relational structure of data. Often
this graph is not given, and so the task of inferring it from nodal observations becomes …
this graph is not given, and so the task of inferring it from nodal observations becomes …
The Foundations of Computational Imaging: A signal processing perspective
Twenty-five years ago, the field of computational imaging arguably did not exist, at least not
as a standalone arena of research activity and technical development. Of course, the idea of …
as a standalone arena of research activity and technical development. Of course, the idea of …
Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction
In imaging inverse problems, one seeks to recover an image from missing/corrupted
measurements. Because such problems are ill-posed, there is great motivation to quantify …
measurements. Because such problems are ill-posed, there is great motivation to quantify …
Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model
Estimating and disentangling epistemic uncertainty (uncertainty that can be reduced with
more training data) and aleatoric uncertainty (uncertainty that is inherent to the task at hand) …
more training data) and aleatoric uncertainty (uncertainty that is inherent to the task at hand) …
Quantifying generative model uncertainty in posterior sampling methods for computational imaging
The idea of using generative models to perform posterior sampling for imaging inverse
problems has elicited attention from the computational imaging community. The main …
problems has elicited attention from the computational imaging community. The main …
Quantifying uncertainty in soil moisture retrieval using a Bayesian neural network framework
Y Li, S Yan, J Gong - Computers and Electronics in Agriculture, 2023 - Elsevier
Soil moisture (SM) is an important parameter for precision agriculture and water cycle.
Recent studies of using Global Navigation Satellite System-Reflectometry (GNSS-R) to …
Recent studies of using Global Navigation Satellite System-Reflectometry (GNSS-R) to …
Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional
regression or learning problems to increase the confidence of a given predictor. We develop …
regression or learning problems to increase the confidence of a given predictor. We develop …
Estimating Epistemic and Aleatoric Uncertainty with a Single Model
Estimating and disentangling epistemic uncertainty, uncertainty that is reducible with more
training data, and aleatoric uncertainty, uncertainty that is inherent to the task at hand, is …
training data, and aleatoric uncertainty, uncertainty that is inherent to the task at hand, is …
Self-supervision Meets Bootstrap Estimation: New Paradigm for Unsupervised Reconstruction with Uncertainty Quantification
Z Wu, L Cao, X Li - openreview.net
Deep learning-based self-supervised reconstruction (SSR) plays a vital role in diverse
domains, including unsupervisedly reconstructing magnetic resonance imaging (MRI) …
domains, including unsupervisedly reconstructing magnetic resonance imaging (MRI) …