1 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
On the expected behaviour of noise regularised deep neural networks as Gaussian processes
Arnu Pretorius, Herman Kamper, Steve Kroon
Recent work has established the equivalence between deep neural networks and Gaussian processes (GPs), resulting in so-called neural network Gaussian processes (NNGPs). The behavio…