Search Results for author:
Found 39 papers, 15 papers with code
PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition
In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs).
Explaining Knock-on Effects of Bias Mitigation
In this paper, we aim to characterise impacted cohorts when mitigation interventions are applied.
Distance-Aware eXplanation Based Learning
eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations.
Unlearning Spurious Correlations in Chest X-ray Classification
We train a deep learning model using a Covid-19 chest X-ray dataset and we showcase how this dataset can lead to spurious correlations due to unintended confounding regions.
Learning from Exemplary Explanations
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.
Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images
To this end, we select progressive MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and construct an ordinal dataset with a prediction target that indicates the time to progression to AD.
PUnifiedNER: A Prompting-based Unified NER System for Diverse Datasets
In this work, we present a ``versatile'' model -- the Prompting-based Unified NER system (PUnifiedNER) -- that works with data from different domains and can recognise up to 37 entity types simultaneously, and theoretically it could be as many as possible.
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning
Identifying spurious correlations learned by a trained model is at the core of refining a trained model and building a trustworthy model.
What Makes Pre-trained Language Models Better Zero-shot Learners?
Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori.
Impact of Feedback Type on Explanatory Interactive Learning
Explanatory Interactive Learning (XIL) collects user feedback on visual model explanations to implement a Human-in-the-Loop (HITL) based interactive learning scenario.
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation
Sward species composition estimation is a tedious one.
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation
In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage.
A Rationale-Centric Framework for Human-in-the-loop Machine Learning
We present a novel rationale-centric framework with human-in-the-loop -- Rationales-centric Double-robustness Learning (RDL) -- to boost model out-of-distribution performance in few-shot learning scenarios.
Semi-supervised dry herbage mass estimation using automatic data and synthetic images
Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device.
Random Walk-steered Majority Undersampling
In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes.
Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data
The training dataset is augmented with the set of label-specific synthetic minority points, and classifiers are trained to predict the relevance of each label independently.
Pseudo-labelling Enhanced Media Bias Detection
Leveraging unlabelled data through weak or distant supervision is a compelling approach to developing more effective text classification models.
On the Importance of Regularisation & Auxiliary Information in OOD Detection
Neural networks are often utilised in critical domain applications (e. g. self-driving cars, financial markets, and aerospace engineering), even though they exhibit overconfident predictions for ambiguous inputs.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data
While with the recent emergence of BERT, deep learning language models can achieve reasonably good performance in document classification with few labelled instances, there is a lack of evidence in the utility of applying BERT-like models on long document classification.
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection
The Radial Basis Function Data Descriptor (RBFDD) network is an effective solution for anomaly detection, however, it is a shallow model that does not deal effectively with raw data representations.
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset
In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset.
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 industry, with implications for antibiotic resistance.
Can We Detect Mastitis earlier than Farmers?
The aim of this study was to build a modelling framework that would allow us to be able to detect mastitis infections before they would normally be found by farmers through the introduction of machine learning techniques.
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 classification tasks, although, they are poorly calibrated often assigning high probability to misclassified predictions.
Deep Context-Aware Novelty Detection
A common assumption of novelty detection is that the distribution of both "normal" and "novel" data are static.
Diverging Divergences: Examining Variants of Jensen Shannon Divergence for Corpus Comparison Tasks
Jensen-Shannon divergence (JSD) is a distribution similarity measurement widely used in natural language processing.
On the Validity of Bayesian Neural Networks for Uncertainty Estimation
Deep neural networks (DNN) are versatile parametric models utilised successfully in a diverse number of tasks and domains.
Real-time Bidding campaigns optimization using attribute selection
In contrast, the objective of our research consists of optimising RTB campaigns by finding out configurations that maximise both the number of impressions and their average profitability.
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets
Active learning has been shown to be an effective way to alleviate some of the effort required in utilising large collections of unlabelled data for machine learning tasks without needing to fully label them.
ZeLiC and ZeChipC: Time Series Interpolation Methods for Lebesgue or Event-based Sampling
Although the interpolation methods for periodic sampling have been a topic of research for a long time, there is a lack of study in methods capable of taking advantage of the Lebesgue sampling characteristics to reconstruct time series more accurately.
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 labelled with more than one class at the same time.
ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter
The Kalman Filter-based Heuristic Ensemble (KFHE) is an ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective.
A Categorisation of Post-hoc Explanations for Predictive Models
The ubiquity of machine learning based predictive models in modern society naturally leads people to ask how trustworthy those models are?
MeetupNet Dublin: Discovering Communities in Dublin's Meetup Network
Meetup. com is a global online platform which facilitates the organisation of meetups in different parts of the world.
Social and Information Networks Computers and Society
Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem.
Deep learning at the shallow end: Malware classification for non-domain experts
Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification.
Ranked #1 on
Malware Classification
on Microsoft Malware Classification Challenge
(F1 score (5-fold) metric)
Stability of Topic Modeling via Matrix Factorization
Topic models can provide us with an insight into the underlying latent structure of a large corpus of documents.
Drift detection using uncertainty distribution divergence
Data generated from naturally occurring processes tends to be non-stationary.
