This paper presents a multi-fidelity method that leverages dimension reduction to enhance the correlation between models, thereby reducing the amount of data needed to train a surrogate from an unordered ensemble of models.
This work presents the first exploration of multivariate conformal methods for chemical properties, including Bonferroni-corrected hyperrectangles, hyperellipsoidal sets based on the Mahalanobis distance, and custom loss functions within conformal risk control.
Katharine Fisher, M. Herbst, J. Kermode et al.· 0 citations
It is shown that a multi-fidelity approach can provide the best of both worlds: it generalizes to a warmer climate for which high fidelity data are not available, guided by abundant low-fidelity data from an even warmer climate, while retaining the accuracy of high-fidelity data, by learning a nonlinear mapping from lo...
B. Balogh, M. A. Bhouri, L. Peng et al.· Machine Learning: Earth· 0 citations
An unbiased direct gradient estimator is derived and the adaptive minimum-variance phase (AMVP) estimator is introduced for neural-network variational optimization by improving the signal-to-noise ratio of weak gradients to enable reliable scientific calculations where training previously failed, while substantially re...
Yi-He Xue, Rui Wang, Bai-Geng Wang et al.· 0 citations
Prior Fitted Networks (PFNs) such as TabPFN now rival established statistical procedures across prediction and estimation tasks. A natural explanation is that PFNs have the property of statistical adaptivity, that is, they perform nearly as well as a method tailored to the true data-generating model for a heterogeneous...
Martin Eppert, K. Balasubramanian, Subhro Ghosh et al.· 0 citations
An adjoint encoder-decoder structure with positive scalar contributions ensures energy consistency across scales and non-negative dissipation, leading to even lower training requirements, and demonstrates that data efficiency can be improved with careful architectural choices rooted in the underlying physics.
M. A. Maia, K. Meyer, A. Van Gils et al.· 1 citation
Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule used to combine them are typically chosen separately, and by ad hoc means. Recent advances in distributional optimisation (i.e. where the optimisation occurs over the set...
Cong-Ye Wang, Yan-Kai Lin, Zhe-Yang Shen et al.· 0 citations
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