Outputs (83)

INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations (2022)
Presentation / Conference Contribution
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decisionmaking, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a s... Read More about INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations.

Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification (2022)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022, July). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

Graph neural networks (GNNs) have attracted extensive interest in text classification tasks due to their expected superior performance in representation learning. However, most existing studies adopted the same semi-supervised learning setting as the... Read More about Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification.

Efficient Uncertainty Quantification for Multilabel Text Classification (2022)
Presentation / Conference Contribution
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). Efficient Uncertainty Quantification for Multilabel Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty qua... Read More about Efficient Uncertainty Quantification for Multilabel Text Classification.

In-Materio Extreme Learning Machines (2022)
Book Chapter
Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). In-Materio Extreme Learning Machines. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII (505-519). Springer Verlag. https://doi.org/10.1007/978-3-031-14714-2_35

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as... Read More about In-Materio Extreme Learning Machines.

Towards Intelligently Designed Evolvable Processors (2022)
Journal Article
Jones, B. A., Chouard, J. L., Branco, B. C., Vissol-Gaudin, E. G., Pearson, C., Petty, M. C., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). Towards Intelligently Designed Evolvable Processors. Evolutionary Computation, 30(4), 479-501. https://doi.org/10.1162/evco_a_00309

Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material’s properties to achieve a specific computational function. This paper addresses the question of how successful and well performing Evolution-in-Materio pro... Read More about Towards Intelligently Designed Evolvable Processors.

A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification (2022)
Journal Article
Winterbottom, T., Leone, A., & Al Moubayed, N. (2022). A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification. Scientific Reports, 12(1), Article 13468. https://doi.org/10.1038/s41598-022-15965-2

We approach the task of detecting the illicit movement of cultural heritage from a machine learning perspective by presenting a framework for detecting a known artefact in a new and unseen image. To this end, we explore the machine learning problem o... Read More about A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification.

Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task (2022)
Presentation / Conference Contribution
Ampomah, I., Burton, J., Enshaei, A., & Al Moubayed, N. (2022, June). Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task. Presented at 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille, France

Numerical tables are widely employed to communicate or report the classification performance of machine learning (ML) models with respect to a set of evaluation metrics. For non-experts, domain knowledge is required to fully understand and interpret... Read More about Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task.

MuLD: The Multitask Long Document Benchmark (2022)
Presentation / Conference Contribution
Hudson, G. T., & Al Moubayed, N. (2022, June). MuLD: The Multitask Long Document Benchmark. Presented at 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille, France

The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks focus on tasks for one or two input sentences, there has been exciting work in designing efficien... Read More about MuLD: The Multitask Long Document Benchmark.

Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels (2022)
Journal Article
Winterbottom, T., Xiao, S., McLean, A., & Al Moubayed, N. (2022). Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels. PeerJ Computer Science, 8(e974), Article e974. https://doi.org/10.7717/peerj-cs.974

Bilinear pooling (BLP) refers to a family of operations recently developed for fusing features from different modalities predominantly for visual question answering (VQA) models. Successive BLP techniques have yielded higher performance with lower co... Read More about Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels.

ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR (2022)
Journal Article
Shakeel, A., Walters, R. J., Ebmeier, S. K., & Moubayed, N. A. (2022). ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR. IEEE Transactions on Geoscience and Remote Sensing, 60, https://doi.org/10.1109/tgrs.2022.3169455

In this study, we address the challenging problem of automatic detection of transient deformation of the Earth’s crust in time series of differential satellite radar [interferometric synthetic aperture radar (InSAR)] images. The detection of these ev... Read More about ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR.