Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry
원문 초록
The advent of next-generation radio interferometers like the Square Kilometer Array promises to revolutionise our radio astronomy observational capabilities. The unprecedented volume of data these devices generate requires fast and accurate image reconstruction algorithms to solve the ill-posed radio interferometric imaging problem. Most state-of-the-art reconstruction methods lack trustworthy and scalable uncertainty quantification, which is critical for the rigorous scientific interpretation of radio observations. We propose an unsupervised technique based on a conformalized version of a radio-augmented equivariant bootstrapping method, which allows us to quantify uncertainties for fast reconstruction methods. Noticeably, we rely on reconstructions from ultra-fast unrolled algorithms. The proposed method brings more reliable uncertainty estimations to our problem than existing alternatives.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Statistical Machine Learning for Astronomy -- A Textbook
Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Estimating prevalence with precision and accuracy
토론
이 논문과 연결된 포럼 글이 아직 없습니다.