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Found 4 papers, 1 papers with code
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Our results therefore suggest that, in the shallow-to-moderate depth setting, critical initialisation provides zero performance gains when compared to off-critical initialisations and that searching for off-critical initialisations that might improve training speed or generalisation, is likely to be a fruitless endeavour.
Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting
We discuss ongoing work into automating a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa.
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
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.
Multimodal One-Shot Learning of Speech and Images
Imagine a robot is shown new concepts visually together with spoken tags, e. g. "milk", "eggs", "butter".
