169 citations · 253 across the 12 of their papers we have counts for
14 papers
SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks
Jacobie Mouton, Steve Kroon
Initial work on variational autoencoders assumed independent latent variables with simple distributions. Subsequent work has explored incorporating more complex distributions and d…
Graphical Residual Flows
Jacobie Mouton, Steve Kroon
Graphical flows add further structure to normalizing flows by encoding non-trivial variable dependencies. Previous graphical flow models have focused primarily on a single flow dir…
Distributed Identification of Central Nodes with Less Communication
Jordan F. Masakuna, Steve Kroon
This paper is concerned with distributed detection of central nodes in complex networks using closeness centrality. Closeness centrality plays an essential role in network analysis…
Performance-Agnostic Fusion of Probabilistic Classifier Outputs
Jordan F. Masakuna, Simukai W. Utete, Steve Kroon
We propose a method for combining probabilistic outputs of classifiers to make a single consensus class prediction when no further information about the individual classifiers is a…
Stochastic Gradient Annealed Importance Sampling for Efficient Online Marginal Likelihood Estimation
Scott A. Cameron, Hans C. Eggers, Steve Kroon
We consider estimating the marginal likelihood in settings with independent and identically distributed (i.i.d.) data. We propose estimating the predictive distributions in a seque…
Stabilising priors for robust Bayesian deep learning
Felix McGregor, Arnu Pretorius, Johan du Preez +1
Bayesian neural networks (BNNs) have developed into useful tools for probabilistic modelling due to recent advances in variational inference enabling large scale BNNs. However, BNN…