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Found 11 papers, 2 papers with code
Healing Gaussian Process Experts
Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems.
Open problems in causal structure learning: A case study of COVID-19 in the UK
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships.
MphayaNER: Named Entity Recognition for Tshivenda
Named Entity Recognition (NER) plays a vital role in various Natural Language Processing tasks such as information retrieval, text classification, and question answering.
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic
We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in low predictive skill over the ten Beninese stations.
Antithetic Riemannian Manifold And Quantum-Inspired Hamiltonian Monte Carlo
The Riemannian Manifold Hamiltonian Monte Carlo algorithm improves on Hamiltonian Monte Carlo by taking into account the local geometry of the target, which is beneficial for target densities that may exhibit strong correlations in the parameters.
Predicting Higher Education Throughput in South Africa Using a Tree-Based Ensemble Technique
We use gradient boosting machines and logistic regression to predict academic throughput at a South African university.
Forecasting The JSE Top 40 Using Long Short-Term Memory Networks
This paper uses a long-short term memory network to perform financial time series forecasting on the return data of the JSE Top 40 index.
Healing Products of Gaussian Processes
Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems.
An Automatic Relevance Determination Prior Bayesian Neural Network for Controlled Variable Selection
We present an Automatic Relevance Determination prior Bayesian Neural Network(BNN-ARD) weight l2-norm measure as a feature importance statistic for the model-x knockoff filter.
Automatic Relevance Determination Bayesian Neural Networks for Credit Card Default Modelling
Credit risk modelling is an integral part of the global financial system.
On the Performance of Network Parallel Training in Artificial Neural Networks
Artificial Neural Networks (ANNs) have received increasing attention in recent years with applications that span a wide range of disciplines including vital domains such as medicine, network security and autonomous transportation.
