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Outputs (57)

A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data (2021)
Presentation / Conference Contribution
Sun, Z., Harit, A., Yu, J., Cristea, A., & Al Moubayed, N. (2021, July). A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data. Presented at IEEE International Joint Conference on Neural Network (IJCNN2021), Virtual

This research focuses on semi-supervised classification tasks, specifically for graph-structured data under datascarce situations. It is known that the performance of conventional supervised graph convolutional models is mediocre at classification ta... Read More about A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data.

Curvature-based feature selection with application in classifying electronic health records (2021)
Journal Article
Zuo, Z., Li, J., Xu, H., & Al Moubayed, N. (2021). Curvature-based feature selection with application in classifying electronic health records. Technological Forecasting and Social Change, 173, Article 121127. https://doi.org/10.1016/j.techfore.2021.121127

Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Things through to Machine Learning (ML) techniques. As a powerful tool, ML... Read More about Curvature-based feature selection with application in classifying electronic health records.

ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference (2021)
Book Chapter
Gajbhiye, A., Al Moubayed, N., & Bradley, S. (2021). ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2021 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V (460-472). Springer Verlag. https://doi.org/10.1007/978-3-030-86383-8_37

Neural language representation models such as BERT, pretrained on large-scale unstructured corpora lack explicit grounding to real-world commonsense knowledge and are often unable to remember facts required for reasoning and inference. Natural Langua... Read More about ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference.

Towards Equal Gender Representation in the Annotations of Toxic Language Detection (2021)
Presentation / Conference Contribution
Excell, E., & Al Moubayed, N. (2021, August). Towards Equal Gender Representation in the Annotations of Toxic Language Detection. Presented at 3rd Workshop on Gender Bias in Natural Language Processing (GeBNLP2021), International Joint Conference on Natural Language Processing (INCNLP2021), Bangkok, Thailand

Classifiers tend to propagate biases present in the data on which they are trained. Hence, it is important to understand how the demographic identities of the annotators of comments affect the fairness of the resulting model. In this paper, we focus... Read More about Towards Equal Gender Representation in the Annotations of Toxic Language Detection.

Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning (2021)
Presentation / Conference Contribution
Watson, M., & Al Moubayed, N. (2021, January). Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning. Presented at The 25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy

Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks h... Read More about Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning.

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records (2021)
Journal Article
Alhassan, Z., Watson, M., Budgen, D., Alshammari, R., Alessa, A., & Al Moubayed, N. (2021). Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records. JMIR Medical Informatics, 9(5), Article e25237. https://doi.org/10.2196/25237

Background: Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems such as diabetes. Early preventive interventions based upon advanced predictive mode... Read More about Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records.

Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models (2020)
Book Chapter
Gajbhiye, A., Winterbottom, T., Al Moubayed, N., & Bradley, S. (2020). Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020 (633-646). Springer Verlag. https://doi.org/10.1007/978-3-030-61609-0_50

We consider the task of incorporating real-world commonsense knowledge into deep Natural Language Inference (NLI) models. Existing external knowledge incorporation methods are limited to lexical-level knowledge and lack generalization across NLI mode... Read More about Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models.

On Modality Bias in the TVQA Dataset (2020)
Presentation / Conference Contribution
Winterbottom, T., Xiao, S., McLean, A., & Al Moubayed, N. (2020, September). On Modality Bias in the TVQA Dataset. Presented at The British Machine Vision Conference (BMVC), Manchester, England

TVQA is a large scale video question answering (video-QA) dataset based on popular TV shows. The questions were specifically designed to require “both vision and language understanding to answer”. In this work, we demonstrate an inherent bias in the... Read More about On Modality Bias in the TVQA Dataset.

Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling (2020)
Journal Article
Al Moubayed, N., McGough, S., & Awwad Shiekh Hasan, B. (2020). Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling. PeerJ Computer Science, 6, Article e252. https://doi.org/10.7717/peerj-cs.252

The article presents a discriminative approach to complement the unsupervised probabilistic nature of topic modelling. The framework transforms the probabilities of the topics per document into class-dependent deep learning models that extract highly... Read More about Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling.