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
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
CosmoGen employs evolutionary algorithms for symbolic regression to generate dark energy fluid models that alleviate S8 and H0 tensions, with Bayesian analysis of one model showing tension relief though weaker preference than LambdaCDM.
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.
-
CosmoGen: A genetic algorithm framework for the exploration of dark energy dynamics
CosmoGen employs evolutionary algorithms for symbolic regression to generate dark energy fluid models that alleviate S8 and H0 tensions, with Bayesian analysis of one model showing tension relief though weaker preference than LambdaCDM.