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Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort +5
Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over th…
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy +3
Bayesian neural networks (BNN) and deep ensembles are principled approaches to estimate the predictive uncertainty of a deep learning model. However their practicality in real-time…
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process
Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1
Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assigns to base models a set of deterministic, constant model weights that (1) do…