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plan-net uses machine learning with an ensemble of Bayesian neural networks for atmospheric retrieval; this approach yields greater accuracy and more robust uncertainties than a single model. A new loss function for BNNs learns correlations between the model outputs. Performance is improved by incorporating domain-specific knowledge into the machine learning models and provides additional insight by inferring the covariance of the retrieved atmospheric parameters.