3 papers
stat.ML2019
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…
cs.CL2019
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…
cs.CL2018
Multimodal One-Shot Learning of Speech and Images
Ryan Eloff, Herman A. Engelbrecht, Herman Kamper
Imagine a robot is shown new concepts visually together with spoken tags, e.g. "milk", "eggs", "butter". After seeing one paired audio-visual example per class, it is shown a new s…