Exoformer uses a transformer network to generate informative priors that accelerate Bayesian atmospheric retrievals of hot Jupiters by 3-8 times without altering the final parameters or Bayesian evidence.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3roles
background 1polarities
background 1representative citing papers
A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.
A machine learning model trained on IllustrisTNG predicts galaxy baryonic properties from dark matter subhalo features, producing a mock catalog for the A-SPEC survey that matches observed galaxy clustering.
citing papers explorer
-
$\texttt{Exoformer}$: Accelerating Bayesian atmospheric retrievals with transformer neural networks
Exoformer uses a transformer network to generate informative priors that accelerate Bayesian atmospheric retrievals of hot Jupiters by 3-8 times without altering the final parameters or Bayesian evidence.
-
Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data
A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.
-
Constructing a Mock Galaxy Catalog for the All-sky SPECtroscopic Survey of Nearby Galaxies (A-SPEC) Using the Machine-assisted Semi-Simulation Model
A machine learning model trained on IllustrisTNG predicts galaxy baryonic properties from dark matter subhalo features, producing a mock catalog for the A-SPEC survey that matches observed galaxy clustering.