The efficacy of matching information systems development methodologies with application characteristics - an empirical study. - Geoffry S. Howard; Thomas Bodnovich; Thomas Janicki; Jens Liegle; Steven Klein; Paul Albert; David Cannon (J. Syst. Softw., 1999)

Research discovery through linked open data. - Paul Albert; Kristi L. Holmes; Katy Börner; Mike Conlon (JCDL, 2012)

Unsupervised Label Noise Modeling and Loss Correction. - Eric Arazo; Diego Ortego; Paul Albert; Noel E. O'Connor; Kevin McGuinness (ICML, 2019)

Towards Robust Learning with Different Label Noise Distributions. - Diego Ortego; Eric Arazo; Paul Albert; Noel E. O'Connor; Kevin McGuinness (ICPR, 2020)

Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. - Eric Arazo; Diego Ortego; Paul Albert; Noel E. O'Connor; Kevin McGuinness (IJCNN, 2020)

Adaptation of Compositional Data Analysis in Deep Learning to Predict Pasture Biomass Proportions. - Badri Narayanan 002; Mohamed Saadeldin; Paul Albert; Kevin McGuinness; Noel E. O'Connor; Brian Mac Namee (AICS, 2021)

How Important is Importance Sampling for Deep Budgeted Training? - Eric Arazo; Diego Ortego; Paul Albert; Noel E. O'Connor; Kevin McGuinness (BMVC, 2021)

Multi-Objective Interpolation Training for Robustness To Label Noise. - Diego Ortego; Eric Arazo; Paul Albert; Noel E. O'Connor; Kevin McGuinness (CVPR, 2021)

Semi-supervised dry herbage mass estimation using automatic data and synthetic images. - Paul Albert; Mohamed Saadeldin; Badri Narayanan 002; Brian Mac Namee; Deirdre Hennessy; Aisling O'Connor; Noel E. O'Connor; Kevin McGuinness (ICCVW, 2021)

ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning. - Paul Albert; Diego Ortego; Eric Arazo; Noel E. O'Connor; Kevin McGuinness (IJCNN, 2021)

Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset. - Badri Narayanan 002; Mohamed Saadeldin; Paul Albert; Kevin McGuinness; Brian Mac Namee (CoRR, 2021)

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation. - Paul Albert; Mohamed Saadeldin; Badri Narayanan 002; Brian Mac Namee; Deirdre Hennessy; Noel E. O'Connor; Kevin McGuinness (CVPR Workshops, 2022)

Embedding Contrastive Unsupervised Features to Cluster In- And Out-of-Distribution Noise in Corrupted Image Datasets. - Paul Albert; Eric Arazo; Noel E. O'Connor; Kevin McGuinness (ECCV (31), 2022)

Addressing out-of-distribution label noise in webly-labelled data. - Paul Albert; Diego Ortego; Eric Arazo; Noel E. O'Connor; Kevin McGuinness (WACV, 2022)

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation. - Paul Albert; Mohamed Saadeldin; Badri Narayanan 002; Jaime B. Fernandez; Brian Mac Namee; Deirdre Hennessy; Noel E. O'Connor; Kevin McGuinness (CoRR, 2022)

Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation. - Paul Albert; Mohamed Saadeldin; Badri Narayanan 002; Brian Mac Namee; Deirdre Hennessy; Aisling H. O'Connor; Noel E. O'Connor; Kevin McGuinness (CoRR, 2022)

Unifying Synergies between Self-supervised Learning and Dynamic Computation. - Tarun Krishna; Ayush K. Rai; Alexandru Drimbarean; Eric Arazo; Paul Albert; Alan F. Smeaton; Kevin McGuinness; Noel E. O'Connor (BMVC, 2023)

Is your noise correction noisy? PLS: Robustness to label noise with two stage detection. - Paul Albert; Eric Arazo; Tarun Krishna; Noel E. O'Connor; Kevin McGuinness (WACV, 2023)

Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge. - Muhammad Zawish; Paul Albert; Flavio Esposito; Steven Davy; Lizy Abraham (CVPR Workshops, 2024)

An Accurate Detection Is Not All You Need to Combat Label Noise in Web-Noisy Datasets. - Paul Albert; Jack Valmadre; Eric Arazo; Tarun Krishna; Noel E. O'Connor; Kevin McGuinness (ECCV (49), 2024)

Knowledge Composition using Task Vectors with Learned Anisotropic Scaling. - Frederic Z. Zhang; Paul Albert; Cristian Rodriguez Opazo; Anton van den Hengel; Ehsan Abbasnejad (NeurIPS, 2024)

RandLoRA: Full rank parameter-efficient fine-tuning of large models. - Paul Albert; Frederic Z. Zhang; Hemanth Saratchandran; Cristian Rodriguez Opazo; Anton van den Hengel; Ehsan Abbasnejad (ICLR, 2025)

Compressing Sine-Activated Low-Rank Adapters through Post-Training Quantization. - Cameron Gordon; Yiping Ji; Hemanth Saratchandran; Paul Albert; Simon Lucey (CoRR, 2025)

Towards Higher Effective Rank in Parameter-efficient Fine-tuning using Khatri-Rao Product. - Paul Albert; Frederic Z. Zhang; Hemanth Saratchandran; Anton van den Hengel; Ehsan Abbasnejad (CoRR, 2025)