no code implementations • 31 Oct 2022 • Thokozile Manaka, Terence van Zyl, Deepak Kar
We present a system that relies on two transfer learning paradigms of monolingual learning and multi-source domain adaptation to improve VA narratives for the target task of the COD classification.
1 code implementation • 26 Apr 2022 • Thokozile Manaka, Terence van Zyl, Alisha N Wade, Deepak Kar
Lower-and-middle income countries are faced with challenges arising from a lack of data on cause of death (COD), which can limit decisions on population health and disease management.
no code implementations • 7 Mar 2022 • Pieter Cawood, Terence van Zyl
We compare against some state of the art ensembling techniques and arithmetic model averaging as a benchmark.
no code implementations • 13 Feb 2022 • Ruan Pretorius, Terence van Zyl
By incorporating specific investor preferences into our RL models' reward functions, a more comprehensive comparison could be made to traditional methods in risk-return space.
no code implementations • 1 Feb 2022 • Nishai Kooverjee, Steven James, Terence van Zyl
Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces.
no code implementations • 9 Sep 2021 • Timilehin Ogundare, Terence van Zyl
We examine the development of a system named ADRIANA for the simulation using South Africa as a case study.
no code implementations • 19 Jun 2021 • Andrew Paskaramoorthy, Tim Gebbie, Terence van Zyl
Mean-variance portfolio decisions that combine prediction and optimisation have been shown to have poor empirical performance.
1 code implementation • 30 Mar 2020 • Andrew Paskaramoorthy, Terence van Zyl, Tim Gebbie
This article provides a workflow that can in-turn be embedded into a process level learning framework.
no code implementations • 2 Jan 2020 • Nishai Kooverjee, Steven James, Terence van Zyl
In this paper we analyse the effectiveness of using deep transfer learning for character recognition tasks.