919 citations · 959 across the 6 of their papers we have counts for
6 papers
Why do women pursue a PhD in Computer Science?
Erika Ábrahám, Miguel Goulão, Milena Vujošević Janičić +13
Computer science attracts few women, and their proportion decreases through advancing career stages. Few women progress to PhD studies in CS after completing master's studies. Empo…
Addressing the "Leaky Pipeline": A Review and Categorisation of Actions to Recruit and Retain Women in Computing Education
Alina Berry, Susan McKeever, Brenda Murphy +1
Gender imbalance in computing education is a well-known issue around the world. The term "leaky pipeline" is often used to describe the lack of retention of women before they progr…
Feature Selection Tutorial with Python Examples
Padraig Cunningham, Bahavathy Kathirgamanathan, Sarah Jane Delany
In Machine Learning, feature selection entails selecting a subset of the available features in a dataset to use for model development. There are many motivations for feature select…
Wider Vision: Enriching Convolutional Neural Networks via Alignment to External Knowledge Bases
Xuehao Liu, Sarah Jane Delany, Susan McKeever
Deep learning models suffer from opaqueness. For Convolutional Neural Networks (CNNs), current research strategies for explaining models focus on the target classes within the asso…
Underestimation Bias and Underfitting in Machine Learning
Padraig Cunningham, Sarah Jane Delany
Often, what is termed algorithmic bias in machine learning will be due to historic bias in the training data. But sometimes the bias may be introduced (or at least exacerbated) by…
k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)
Padraig Cunningham, Sarah Jane Delany
Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier -- classification is achieved by identifying the neare…