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Found 26 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.
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.
Nano Version Control and Robots of Robots: Data Driven, Regenerative Production Code
By using agent-based simulation and NanoVC repos for agent arbitration, we are able to create a simulated environment where patterns developed by people are used to transform working prototypes into templates that data can be fed through to create the robots that create the production code.
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.
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.
Relative Net Utility and the Saint Petersburg Paradox
Over the years, many economic theories were developed to resolve the paradox and explain gaps in the economic value theory in the evaluation of economic decisions, the subjective utility of the expected outcomes, and risk aversion as observed in the game of the St. Petersburg Paradox.
Automatic Relevance Determination Bayesian Neural Networks for Credit Card Default Modelling
Credit risk modelling is an integral part of the global financial system.
Relative rationality: Is machine rationality subjective?
Rationality has two elements and these are the use of relevant information and the efficient processing of such information.
Can rationality be measured?
Rationality is defined as the use of complete information, which is processed with a perfect biological or physical brain, in an optimized fashion.
The limit of artificial intelligence: Can machines be rational?
This paper studies the question on whether machines can be rational.
Creativity and Artificial Intelligence: A Digital Art Perspective
There are generally three types of artificial intelligence and these are machine learning, evolutionary programming and soft computing.
Blockchain and Artificial Intelligence
It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate.
Early prediction of the duration of protests using probabilistic Latent Dirichlet Allocation and Decision Trees
Protests and agitations are an integral part of every democratic civil society.
Rational Choice and Artificial Intelligence
The theory of rational choice assumes that when people make decisions they do so in order to maximize their utility.
Artificial Intelligence and Economic Theories
This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied.
Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach
This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset.
Proposition of a Theoretical Model for Missing Data Imputation using Deep Learning and Evolutionary Algorithms
In this article, considering arbitrary and monotone missing data patterns, we hypothesize that the use of deep neural networks built using autoencoders and denoising autoencoders in conjunction with genetic algorithms, swarm intelligence and maximum likelihood estimator methods as novel data imputation techniques will lead to better imputed values than existing techniques.
Artificial Intelligence and Asymmetric Information Theory
When human agents come together to make decisions, it is often the case that one human agent has more information than the other.
Causal Model Analysis using Collider v-structure with Negative Percentage Mapping
A major problem of causal inference is the arrangement of dependent nodes in a directed acyclic graph (DAG) with path coefficients and observed confounders.
Rational Counterfactuals
This paper introduces the concept of rational countefactuals which is an idea of identifying a counterfactual from the factual (whether perceived or real) that maximizes the attainment of the desired consequent.
Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification
In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence, specifically, Artificial Immune Systems to identify takeover targets.
Flexibly-bounded Rationality and Marginalization of Irrationality Theories for Decision Making
Rational decision making involves using information which is almost always imperfect and incomplete together with some intelligent machine which if it is a human being is inconsistent to make decisions.
Semi-bounded Rationality: A model for decision making
Rational decision making involves using information which is almost always imperfect and incomplete as well as some intelligent machine which if it is a human being is inconsistent to make decisions.
