A Bayesian CNN maps 2D slitless spectral images to redshift estimates with NMAD precision 0.0104 for SNR_GI >=1 and better for brighter sources, while remaining robust to wavelength calibration errors via spatial augmentations.
Bayesian Neural Networks: An Introduction and Survey
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing. Despite their success, they are often implemented in a frequentist scheme, meaning they are unable to reason about uncertainty in their predictions. This article introduces Bayesian Neural Networks (BNNs) and the seminal research regarding their implementation. Different approximate inference methods are compared, and used to highlight where future research can improve on current methods.
fields
astro-ph.IM 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey
A Bayesian CNN maps 2D slitless spectral images to redshift estimates with NMAD precision 0.0104 for SNR_GI >=1 and better for brighter sources, while remaining robust to wavelength calibration errors via spatial augmentations.