1 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Arnu Pretorius, Elan van Biljon, Benjamin van Niekerk +6
Recent work in signal propagation theory has shown that dropout limits the depth to which information can propagate through a neural network. In this paper, we investigate the effe…
A Coordinated Search Strategy for Multiple Solitary Robots: An Extension
Jordan F. Masakuna, Simukai W. Utete, Steve Kroon
The problem of coordination without a priori information about the environment is important in robotics. Applications vary from formation control to search and rescue. This paper c…