20 citations · 72 across the 37 of their papers we have counts for
4 papers · 1 filter
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…
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
Ryan Eloff, André Nortje, Benjamin van Niekerk +7
For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis. Unsup…