TL;DR
This chapter introduces DEGU (Distilling Ensembles for Genomic Uncertainty-aware models), a model-training framework that integrates ensemble learning with knowledge distillation to enhance the robustness and explainability of genomic deep learning predictions.
Uncertainty estimate is redefined as essential to machine learning performance rather than as a byproduct, and a systematic research program centered on unified theoretical frameworks, uniform evaluation procedures, domain-sensitive calibration, and scalable uncertainty-aware structures is suggested.
An uncertainty-aware machine learning framework that integrates probabilistic modeling techniques into conventional predictive architectures to jointly estimate epistemic and aleatoric uncertainty is proposed, indicating that incorporating uncertainty estimation enhances trustworthiness and robustness without sacrifici...
Robust modeling of complex reaction systems requires not only accurate predictions and physical consistency, but also reliable estimation of predictive uncertainty. Here, we investigate how mechanistic constraint type influences the deep ensemble estimates of epistemic uncertainty in neural ordinary differential equa...
AdaptNTK is introduced, a single-model framework that measures uncertainty as a regularized Mahalanobis distance in empirical neural tangent kernel (NTK) feature space, providing efficient single-model uncertainty estimation with sequential updates for data-efficient active learning.
Nonlinear mixed-effects (NLME) models are essential for analyzing hierarchical and longitudinal data, particularly in pharmacometrics. This paper presents deep nonlinear mixed effects (DeepNLME) models and applications thereof in the DeepPumas software framework. DeepNLME integrates neural networks with scientific an...
This paper tackles visible challenges in deep ensemble learning, where deep neural networks serve as ensemble members: training and storage burdens, and robustness of cautious (set-valued) predictions targeting multiple utilities, which may involve reward-sensitivity. To mitigate the training and storage burdens, we pr...
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