25 citations · 94 across the 21 of their papers we have counts for
6 papers · 1 filter
Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks
Eirik Høyheim, Lars Skaaret-Lund, Solve Sæbø +1
Modeling natural phenomena with artificial neural networks (ANNs) often provides highly accurate predictions. However, ANNs often suffer from over-parameterization, complicating in…
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…
Flexible Bayesian Nonlinear Model Configuration
Aliaksandr Hubin, Geir Storvik, Florian Frommlet
Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are ofte…
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models
Aliaksandr Hubin
Non-homogeneous hidden Markov models (NHHMM) are a subclass of dependent mixture models used for semi-supervised learning, where both transition probabilities between the latent st…
Combining Model and Parameter Uncertainty in Bayesian Neural Networks
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…