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09/11/2023
Distance-Aware eXplanation Based Learning
eXplanation Based Learning (XBL) is an interactive learning approach tha...
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08/02/2023
Unlearning Spurious Correlations in Chest X-ray Classification
Medical image classification models are frequently trained using trainin...
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07/12/2023
Learning from Exemplary Explanations
eXplanation Based Learning (XBL) is a form of Interactive Machine Learni...
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04/14/2023
Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images
Alzheimer's Disease (AD), which is the most common cause of dementia, is...
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11/27/2022
PUnifiedNER: a Prompting-based Unified NER System for Diverse Datasets
Much of named entity recognition (NER) research focuses on developing da...
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11/15/2022
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning
Identifying spurious correlations learned by a trained model is at the c...
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09/30/2022
What Makes Pre-trained Language Models Better Zero/Few-shot Learners?
In this paper, we propose a theoretical framework to explain the efficac...
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09/26/2022
Impact of Feedback Type on Explanatory Interactive Learning
Explanatory Interactive Learning (XIL) collects user feedback on visual ...
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04/20/2022
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation
Sward species composition estimation is a tedious one. Herbage must be c...
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04/18/2022
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation
Herbage mass yield and composition estimation is an important tool for d...
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03/24/2022
A Rationale-Centric Framework for Human-in-the-loop Machine Learning
We present a novel rationale-centric framework with human-in-the-loop – ...
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10/26/2021
Semi-supervised dry herbage mass estimation using automatic data and synthetic images
Monitoring species-specific dry herbage biomass is an important aspect o...
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09/25/2021
Random Walk-steered Majority Undersampling
In this work, we propose Random Walk-steered Majority Undersampling (RWM...
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09/25/2021
Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data
There is often a mixture of very frequent labels and very infrequent lab...
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07/16/2021
Pseudo-labelling Enhanced Media Bias Detection
Leveraging unlabelled data through weak or distant supervision is a comp...
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07/15/2021
On the Importance of Regularisation Auxiliary Information in OOD Detection
Neural networks are often utilised in critical domain applications (e.g....
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06/12/2021
A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data
Training deep learning models with limited labelled data is an attractiv...
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01/29/2021
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection
Anomaly detection is a challenging problem in machine learning, and is e...
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01/08/2021
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset
The dairy industry uses clover and grass as fodder for cows. Accurate es...
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01/06/2021
Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cows
Mastitis is a billion dollar health problem for the modern dairy industr...
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11/05/2020
Can We Detect Mastitis earlier than Farmers?
The aim of this study was to build a modelling framework that would allo...
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09/03/2020
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings
Deep neural networks have been successful in diverse discriminative clas...
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06/01/2020
Deep Context-Aware Novelty Detection
A common assumption of novelty detection is that the distribution of bot...
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12/03/2019
On the Validity of Bayesian Neural Networks for Uncertainty Estimation
Deep neural networks (DNN) are versatile parametric models utilised succ...
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10/29/2019
Real-time Bidding campaigns optimization using attribute selection
Real-Time Bidding is nowadays one of the most promising systems in the o...
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10/04/2019
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets
Manually labelling large collections of text data is a time-consuming, e...
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10/04/2019
Investigating the Effectiveness of Word-Embedding Based Active Learning for Labelling Text Datasets
Manually labelling large collections of text data is a time-consuming, e...
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06/06/2019
ZeLiC and ZeChipC: Time Series Interpolation Methods for Lebesgue or Event-based Sampling
Lebesgue sampling is based on collecting information depending on the va...
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04/23/2019
KFHE-HOMER: Kalman Filter-based Heuristic Ensemble of HOMER for Multi-Label Classification
Multi-label classification allows a datapoint to be labelled with more t...
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04/23/2019
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification
Multi-label classification is an approach which allows a datapoint to be...
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04/04/2019
A Categorisation of Post-hoc Explanations for Predictive Models
The ubiquity of machine learning based predictive models in modern socie...
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10/06/2018
MeetupNet Dublin: Discovering Communities in Dublin's Meetup Network
Meetup.com is a global online platform which facilitates the organisatio...
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07/30/2018
Kalman Filter-based Heuristic Ensemble: A New Perspective on Ensemble Classification Using Kalman Filters
A classifier ensemble is a combination of multiple diverse classifier mo...
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07/22/2018
Deep learning at the shallow end: Malware classification for non-domain experts
Current malware detection and classification approaches generally rely o...
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02/23/2017