References

Here you can find links to the papers describing the algorithms and metrics implemented in Bensemble.

Bayesian Inference & Posterior Approximation

Practical Variational Inference - Alex Graves "Practical Variational Inference for Neural Networks" (2011)
Probabilistic Backpropagation - José Miguel Hernández-Lobato, Ryan Adams"Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks" (2015)
Rényi Divergence Variational Inference - Yingzhen Li, Richard E. Turner "Rényi Divergence Variational Inference" (2016)
Kronecker-Factored Laplace Approximation - Hippolyt Ritter, Aleksandar Botev, David Barber"A Scalable Laplace Approximation for Neural Networks" (2018)

MC Dropout - Yarin Gal, Zoubin Ghahramani"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning" (2016)
Deep Ensembles - Balaji Lakshminarayanan et al. "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" (2017)
Neural Ensemble Search (NES) - Sheheryar Zaidi et al."Neural Ensemble Search for Uncertainty Estimation and Dataset Shift" (2021)
NES via Bayesian Sampling (NESBS) - Yao Shu et al. "Neural Ensemble Search via Bayesian Sampling" (2022)

Temperature / Vector Scaling & ECE - Chuan Guo et al."On Calibration of Modern Neural Networks" (2017)
Aleatoric and Epistemic Decomposition - Eyke Hüllermeier, Willem Waegeman "Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods" (2021)