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Found 9 papers, 2 papers with code
Improving Cause-of-Death Classification from Verbal Autopsy Reports
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.
Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia
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.
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion
We compare against some state of the art ensembling techniques and arithmetic model averaging as a benchmark.
Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management
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.
Investigating Transfer Learning in Graph Neural Networks
Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces.
Surrogate Parameters Optimization for Data and Model Fusion of COVID-19 Time-series Data
We examine the development of a system named ADRIANA for the simulation using South Africa as a case study.
The efficient frontiers of mean-variance portfolio rules under distribution misspecification
Mean-variance portfolio decisions that combine prediction and optimisation have been shown to have poor empirical performance.
A Framework for Online Investment Algorithms
This article provides a workflow that can in-turn be embedded into a process level learning framework.
Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition
In this paper we analyse the effectiveness of using deep transfer learning for character recognition tasks.
