Brian Mac Namee
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View article: This Looks Like that and that and that: Multi-objective Optimization for Diverse Prototype Learning
This Looks Like that and that and that: Multi-objective Optimization for Diverse Prototype Learning Open
View article: ActiViz: Understanding Sample Selection in Active Learning through Boundary Visualization
ActiViz: Understanding Sample Selection in Active Learning through Boundary Visualization Open
View article: Exploring explanation deficits in subclinical mastitis detection with explainable boosting machines
Exploring explanation deficits in subclinical mastitis detection with explainable boosting machines Open
Subclinical mastitis in cows is a major challenge to the global development of the dairy sector, as it can reduce milk yield and lead to significant financial losses for dairy farmers. Given the difficulty in detecting subclinical mastitis…
View article: Reducing inference cost of Alzheimer’s disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners
Reducing inference cost of Alzheimer’s disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners Open
View article: Using milk flow profiles for subclinical mastitis detection
Using milk flow profiles for subclinical mastitis detection Open
Mastitis is a significant disease on dairy farms and can have serious negative animal performance and economic consequences if not controlled. While clinical mastitis is often easily identified due to visibly abnormal milk, subclinical mas…
View article: Guidelines for the Evolving Role of Generative AI in Introductory Programming Based on Emerging Practice
Guidelines for the Evolving Role of Generative AI in Introductory Programming Based on Emerging Practice Open
View article: Rewriting Bias: Mitigating Media Bias in News Recommender Systems through Automated Rewriting
Rewriting Bias: Mitigating Media Bias in News Recommender Systems through Automated Rewriting Open
Personalised news recommender systems are effective in disseminating news content based on users' reading histories but can also amplify and proliferate biased media. This work examines the potential of automated sentence rewriting methods…
View article: Explainable Interactive Machine Learning Using Prototypical Part Networks for Medical Image Analysis
Explainable Interactive Machine Learning Using Prototypical Part Networks for Medical Image Analysis Open
Medical imaging is a critical component of clinical decision-making, patient diagnosis, treatment planning, intervention, and therapy. However, due to the shortage of qualified radiologists, there is an increasing burden on healthcare prac…
View article: The Effects of Media Bias on News Recommendations
The Effects of Media Bias on News Recommendations Open
The negative effects of media bias, such as influencing readers’ perceptions and affecting their social decisions, have been widely identified by social scientists. However, the combined impact of media bias and personalised news recommend…
View article: ML-KFHE: Multi-label Ensemble Classification Algorithm Exploiting Sensor Fusion Properties of the Kalman Filter
ML-KFHE: Multi-label Ensemble Classification Algorithm Exploiting Sensor Fusion Properties of the Kalman Filter Open
Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman filter…
View article: CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring
CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring Open
Dairy is an economically significant industry that caters to the huge demand for food products in people's lives. To remain profitable, farmers need to manage their farms and the health of the dairy cows in their herds. There are, however,…
View article: PB1520 Deep Learning Extracts Embedded Signatures Associated with Pregnancy and Preeclampsia from Circulating Vesicle Profiles
PB1520 Deep Learning Extracts Embedded Signatures Associated with Pregnancy and Preeclampsia from Circulating Vesicle Profiles Open
View article: Distance-Aware eXplanation Based Learning
Distance-Aware eXplanation Based Learning Open
eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations. XBL augments loss functions to penalize a model based on devia…
View article: No More Pencils No More Books: Capabilities of Generative AI on Irish and UK Computer Science School Leaving Examinations
No More Pencils No More Books: Capabilities of Generative AI on Irish and UK Computer Science School Leaving Examinations Open
Supplemental data for: Joyce Mahon, Brian Mac Namee, and Brett A. Becker. 2023. No More Pencils No More Books: Capabilities of Generative AI on Irish and UK Computer Science School Leaving Examinations. In The United Kingdom and Ir…
View article: Unlearning Spurious Correlations in Chest X-ray Classification
Unlearning Spurious Correlations in Chest X-ray Classification Open
Medical image classification models are frequently trained using training datasets derived from multiple data sources. While leveraging multiple data sources is crucial for achieving model generalization, it is important to acknowledge tha…
View article: Learning from Exemplary Explanations
Learning from Exemplary Explanations Open
eXplanation Based Learning (XBL) is a form of Interactive Machine Learning (IML) that provides a model refining approach via user feedback collected on model explanations. Although the interactivity of XBL promotes model transparency, XBL …
View article: Investigating Temporal Features of Carotid Intima-Media Thickness from Ultrasound Imaging with Recurrent Neural Networks
Investigating Temporal Features of Carotid Intima-Media Thickness from Ultrasound Imaging with Recurrent Neural Networks Open
Measuring carotid intima-media thickness (cIMT) of the Common Carotid Artery (CCA) via B-mode ultrasound imaging is a non-invasive yet effective way to monitor and assess cardiovascular risk. Recent studies using Convolutional Neural Netwo…
View article: Learning from Exemplary Explanations
Learning from Exemplary Explanations Open
eXplanation Based Learning (XBL) is a form of Interactive Machine Learning (IML) that provides a model refining approach via user feedback collected on model explanations. Although the interactivity of XBL promotes model transparency, XBL …
View article: PUnifiedNER: A Prompting-Based Unified NER System for Diverse Datasets
PUnifiedNER: A Prompting-Based Unified NER System for Diverse Datasets Open
Much of named entity recognition (NER) research focuses on developing dataset-specific models based on data from the domain of interest, and a limited set of related entity types. This is frustrating as each new dataset requires a new mode…
View article: Early detection of subclinical mastitis in lactating dairy cows using cow-level features
Early detection of subclinical mastitis in lactating dairy cows using cow-level features Open
Subclinical mastitis in cows affects their health, well-being, longevity, and performance, leading to reduced productivity and profit. Early prediction of subclinical mastitis can enable dairy farmers to perform interventions to mitigate i…
View article: Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images
Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images Open
Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD is crucial for making treatment decisions. However, most of the literature on computer-assisted detection of AD focuses on…
View article: Unlearning Spurious Correlations in Chest X-Ray Classification
Unlearning Spurious Correlations in Chest X-Ray Classification Open
Medical image classification models are frequently trained using training datasets derived from multiple data sources. While leveraging multiple data sources is crucial for achieving model generalization, it is important to acknowledge tha…
View article: AI and ML in School Level Computing Education: Who, What and Where?
AI and ML in School Level Computing Education: Who, What and Where? Open
This paper presents the results of a systematic review of the literature relating to artificial intelligence (AI) and machine learning (ML) education at school level. We conducted a search of the ACM Full-text Collection and 33 papers from…
View article: Exploring Optimal Configurations in Active Learning for Medical Imaging
Exploring Optimal Configurations in Active Learning for Medical Imaging Open
Medical imaging is a critical component of clinical decision-making, patient diagnosis, treatment planning, intervention, and therapy. However, due to the shortage of qualified radiologists, there is an increasing burden on healthcare prac…
View article: What Makes Pre-trained Language Models Better Zero-shot Learners?
What Makes Pre-trained Language Models Better Zero-shot Learners? Open
Current methods for prompt learning in zero-shot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori. This is not ideal because in a real-world zero-sho…
View article: Unveiling the Relationship Between News Recommendation Algorithms and Media Bias: A Simulation-Based Analysis of the Evolution of Bias Prevalence
Unveiling the Relationship Between News Recommendation Algorithms and Media Bias: A Simulation-Based Analysis of the Evolution of Bias Prevalence Open
Media bias has significant negative effects, such as influencing elections and shaping people's perceptions. However, the relationship between media bias and personalised news recommendation algorithms (widely adopted by many news platform…
View article: Integrating Unsupervised Clustering and Label-Specific Oversampling to Tackle Imbalanced Multi-Label Data
Integrating Unsupervised Clustering and Label-Specific Oversampling to Tackle Imbalanced Multi-Label Data Open
There is often a mixture of very frequent labels and very infrequent labels in multi-label datatsets. This variation in label frequency, a type class imbalance, creates a significant challenge for building efficient multi-label classificat…
View article: Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer’s Disease from MRI Images
Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer’s Disease from MRI Images Open
Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD is crucial for making treatment decisions. However, most of the literature on computer-assisted detection of AD focuses on…
View article: PUnifiedNER: A Prompting-based Unified NER System for Diverse Datasets
PUnifiedNER: A Prompting-based Unified NER System for Diverse Datasets Open
Much of named entity recognition (NER) research focuses on developing dataset-specific models based on data from the domain of interest, and a limited set of related entity types. This is frustrating as each new dataset requires a new mode…
View article: Identifying Spurious Correlations and Correcting them with an Explanation-based Learning
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning Open
Identifying spurious correlations learned by a trained model is at the core of refining a trained model and building a trustworthy model. We present a simple method to identify spurious correlations that have been learned by a model traine…