25 citations · 94 across the 21 of their papers we have counts for
4 papers · 1 filter
Subsampling MCMC for Bayesian Variable Selection and Model Averaging in BGNLM
Jon Lachmann, Aliaksandr Hubin
Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible nonlinear alternative to GLM while still providing better interpretability than machine learning techniques such as n…
Fractional Polynomials Models as Special Cases of Bayesian Generalized Nonlinear Models
Aliaksandr Hubin, Georg Heinze, Riccardo De Bin
We propose a framework for fitting fractional polynomials models as special cases of Bayesian Generalized Nonlinear Models, applying an adapted version of the Genetically Modified…
Sparsifying Bayesian neural networks with latent binary variables and normalizing flows
Lars Skaaret-Lund, Geir Storvik, Aliaksandr Hubin
Artificial neural networks (ANNs) are powerful machine learning methods used in many modern applications such as facial recognition, machine translation, and cancer diagnostics. A…
Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
Aliaksandr Hubin, Geir Storvik
Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian infe…