Bayesian inverse problems with unknown operators
PulseAugur coverage of Bayesian inverse problems with unknown operators — every cluster mentioning Bayesian inverse problems with unknown operators across labs, papers, and developer communities, ranked by signal.
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Score-based diffusion models enhance diffuse optical tomography reconstructions
Researchers have developed a novel approach using score-based diffusion models to improve reconstructions in diffuse optical tomography (DOT), a complex inverse problem. The new method constructs a mixed score by combin…
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New infinite-dimensional normalizing flow model for Bayesian inverse problems
Researchers have developed a novel infinite-dimensional continuous normalizing flow model to address Bayesian inference for inverse problems involving partial differential equations. This model utilizes a neural ordinar…
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New criterion for conditional expectation operators in machine learning
A new paper introduces a verifiable criterion for understanding conditional expectation operators (CEOs) and conditional mean embeddings (CMEs). These concepts are crucial in areas like nonparametric regression, Bayesia…
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New research paper details "prior laundering" in Bayesian inverse problems
A new research paper introduces the concept of "prior laundering," a technique where learned generative priors are used for ill-posed Bayesian inverse problems. This method involves using an archive of legacy reconstruc…
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New framework enhances neural likelihood approximation for complex Bayesian problems
Researchers have developed a new framework for neural likelihood approximation in Bayesian inverse problems, addressing challenges posed by complex scientific and engineering models. This approach trains likelihood surr…
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New Laplace--Fisher Gate Identity Enhances Score Estimation in Bayesian Inverse Problems
Researchers have developed a new method called the Laplace--Fisher Gate Identity (LFGI) for estimating scores in sampling from unnormalized targets. This method uses matrix-valued blending coefficients, or gates, to opt…
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Research Unifies Data-Driven Priors for Bayesian Inverse Problems
A new research paper proposes a unified framework for integrating various data-driven priors into Bayesian inverse problems. The study demonstrates how diverse priors, including regularization-by-denoising, normalizing …