Paul Albert

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 29

Top Topics

Unlabelled Data

Super Resolution

Supervised Learning

Top Venues

Publications

Frederic Z. Zhang, Paul Albert, Cristian Rodriguez Opazo, Anton van den Hengel, Ehsan Abbasnejad
Knowledge Composition using Task Vectors with Learned Anisotropic Scaling. CoRR (2024)
Muhammad Zawish, Paul Albert, Flavio Esposito, Steven Davy, Lizy Abraham
Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge. CoRR (2024)
Paul Albert, Jack Valmadre, Eric Arazo, Tarun Krishna, Noel E. O'Connor, Kevin McGuinness
An accurate detection is not all you need to combat label noise in web-noisy datasets. CoRR (2024)
Paul Albert, Eric Arazo, Tarun Krishna, Noel E. O'Connor, Kevin McGuinness
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection. WACV (2023)
Tarun Krishna, Ayush K. Rai, Alexandru Drimbarean, Eric Arazo, Paul Albert, Alan F. Smeaton, Kevin McGuinness, Noel E. O'Connor
Unifying Synergies between Self-supervised Learning and Dynamic Computation. BMVC (2023)
Paul Albert, Diego Ortego, Eric Arazo, Noel E. O'Connor, Kevin McGuinness
Addressing out-of-distribution label noise in webly-labelled data. WACV (2022)
Paul Albert, Mohamed Saadeldin, Badri Narayanan, Brian Mac Namee, Deirdre Hennessy, Aisling H. O'Connor, Noel E. O'Connor, Kevin McGuinness
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation. CoRR (2022)
Paul Albert, Mohamed Saadeldin, Badri Narayanan, Brian Mac Namee, Deirdre Hennessy, Noel E. O'Connor, Kevin McGuinness
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation. CVPR Workshops (2022)
Paul Albert, Eric Arazo, Noel E. O'Connor, Kevin McGuinness
Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets. CoRR (2022)
Paul Albert, Eric Arazo, Noel E. O'Connor, Kevin McGuinness
Embedding Contrastive Unsupervised Features to Cluster In- And Out-of-Distribution Noise in Corrupted Image Datasets. ECCV (31) (2022)
Paul Albert, Mohamed Saadeldin, Badri Narayanan, Jaime B. Fernandez, Brian Mac Namee, Deirdre Hennessy, Noel E. O'Connor, Kevin McGuinness
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation. CoRR (2022)
Paul Albert, Eric Arazo, Tarun Krishna, Noel E. O'Connor, Kevin McGuinness
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection. CoRR (2022)
Paul Albert, Mohamed Saadeldin, Badri Narayanan, Brian Mac Namee, Deirdre Hennessy, Aisling O'Connor, Noel E. O'Connor, Kevin McGuinness
Semi-supervised dry herbage mass estimation using automatic data and synthetic images. CoRR (2021)
Badri Narayanan, Mohamed Saadeldin, Paul Albert, Kevin McGuinness, Brian Mac Namee
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset. CoRR (2021)
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
How Important is Importance Sampling for Deep Budgeted Training? CoRR (2021)
Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Multi-Objective Interpolation Training for Robustness To Label Noise. CVPR (2021)
Badri Narayanan, Mohamed Saadeldin, Paul Albert, Kevin McGuinness, Noel E. O'Connor, Brian Mac Namee
Adaptation of Compositional Data Analysis in Deep Learning to Predict Pasture Biomass Proportions. AICS (2021)
Paul Albert, Diego Ortego, Eric Arazo, Noel E. O'Connor, Kevin McGuinness
ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning. IJCNN (2021)
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
How Important is Importance Sampling for Deep Budgeted Training? BMVC (2021)
Paul Albert, Mohamed Saadeldin, Badri Narayanan, Brian Mac Namee, Deirdre Hennessy, Aisling O'Connor, Noel E. O'Connor, Kevin McGuinness
Semi-supervised dry herbage mass estimation using automatic data and synthetic images. ICCVW (2021)
Paul Albert, Diego Ortego, Eric Arazo, Noel E. O'Connor, Kevin McGuinness
Addressing out-of-distribution label noise in webly-labelled data. CoRR (2021)
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. IJCNN (2020)
Paul Albert, Diego Ortego, Eric Arazo, Noel E. O'Connor, Kevin McGuinness
ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning. CoRR (2020)
Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Towards Robust Learning with Different Label Noise Distributions. ICPR (2020)
Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Multi-Objective Interpolation Training for Robustness to Label Noise. CoRR (2020)
Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Towards Robust Learning with Different Label Noise Distributions. CoRR (2019)
Eric Arazo Sanchez, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Unsupervised label noise modeling and loss correction. CoRR (2019)
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Unsupervised Label Noise Modeling and Loss Correction. ICML (2019)
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. CoRR (2019)
Paul Albert, Kristi L. Holmes, Katy Börner, Mike Conlon
Research discovery through linked open data. JCDL (2012)
Geoffry S. Howard, Thomas Bodnovich, Thomas Janicki, Jens Liegle, Steven Klein, Paul Albert, David Cannon
The efficacy of matching information systems development methodologies with application characteristics - an empirical study. J. Syst. Softw. 45 (3) (1999)