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2020 – today
- 2024
[j29]Qin Ruan
, Brian Mac Namee, Ruihai Dong:
The Effects of Media Bias on News Recommendations. IEEE Access 12: 83391-83404 (2024)- [c94]James Murphy, Maria Buckley, Léonie Buckley, Adam Taylor, Jake O'Brien, Brian Mac Namee:
Deploying Machine Learning Anomaly Detection Models to Flight Ready AI Boards. CVPR Workshops 2024: 6828-6836 - [c93]Brian Mac Namee:
Explainable Interactive Machine Learning Using Prototypical Part Networks for Medical Image Analysis. HHAI 2024: 402-408 - [c92]Joyce Mahon, Brian Mac Namee, Brett A. Becker:
Guidelines for the Evolving Role of Generative AI in Introductory Programming Based on Emerging Practice. ITiCSE (1) 2024 - [c91]Joyce Mahon, Brett A. Becker, Brian Mac Namee, Juho Leinonen:
Post Primary Teachers' Perspectives on Machine Learning and Artificial Intelligence in the Leaving Certificate Computer Science Curriculum. Koli Calling 2024: 29:1-29:2 - [c90]Qin Ruan, Jin Xu, Susan Leavy, Brian Mac Namee, Ruihai Dong:
Rewriting Bias: Mitigating Media Bias in News Recommender Systems through Automated Rewriting. UMAP 2024: 67-77 - [i37]Jinghui Lu, Ziwei Yang, Yanjie Wang, Xuejing Liu, Brian Mac Namee, Can Huang:
PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition. CoRR abs/2402.04838 (2024) - 2023
- [j28]Luis Miralles-Pechuán, Muhammad Atif Qureshi, Brian Mac Namee:
Real-time bidding campaigns optimization using user profile settings. Electron. Commer. Res. 23(2): 1297-1322 (2023) - [j27]Arjun Pakrashi, Duncan Wallace, Brian Mac Namee, Derek Greene, Christophe Guéret:
CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring. Frontiers Artif. Intell. 6 (2023) - [j26]Arjun Pakrashi, Brian Mac Namee:
ML-KFHE: Multi-label Ensemble Classification Algorithm Exploiting Sensor Fusion Properties of the Kalman Filter. SN Comput. Sci. 4(6): 821 (2023) - [c89]Jinghui Lu, Rui Zhao, Brian Mac Namee, Fei Tan:
PUnifiedNER: A Prompting-Based Unified NER System for Diverse Datasets. AAAI 2023: 13327-13335 - [c88]Changhong Jin, John Upton, Brian Mac Namee:
Do Cows Have Fingerprints? Using Time Series Techniques and Milk Flow Profiles to Characterise Cow Milking Performance and Detect Health Issues. AALTD@ECML/PKDD 2023: 230-242 - [c87]Jinghui Lu, Dongsheng Zhu, Weidong Han, Rui Zhao, Brian Mac Namee, Fei Tan:
What Makes Pre-trained Language Models Better Zero-shot Learners? ACL (1) 2023: 2288-2303 - [c86]Qin Ruan, Brian Mac Namee, Ruihai Dong:
Reducing Media Bias in News Headlines. AICS 2023: 1-4 - [c85]Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee:
Unlearning Spurious Correlations in Chest X-Ray Classification. DS 2023: 387-397 - [c84]Min Jing, Kathryn Owen, Brian Mac Namee, Iab B. A. Menown, James McLaughlin:
Investigating Temporal Features of Carotid Intima-Media Thickness from Ultrasound Imaging with Recurrent Neural Networks. EMBC 2023: 1-4 - [c83]Payel Sadhukhan, Arjun Pakrashi, Sarbani Palit, Brian Mac Namee:
Integrating Unsupervised Clustering and Label-Specific Oversampling to Tackle Imbalanced Multi-Label Data. ICAART (2) 2023: 489-498 - [c82]Misgina Tsighe Hagos, Niamh Belton, Kathleen M. Curran, Brian Mac Namee:
Distance-Aware eXplanation Based Learning. ICTAI 2023: 279-286 - [c81]Alec Parise, Brian Mac Namee:
Exploring Optimal Configurations in Active Learning for Medical Imaging. SGAI Conf. 2023: 75-88 - [c80]Qin Ruan, Brian Mac Namee, Ruihai Dong:
Unveiling the Relationship Between News Recommendation Algorithms and Media Bias: A Simulation-Based Analysis of the Evolution of Bias Prevalence. SGAI Conf. 2023: 210-215 - [c79]Misgina Tsighe Hagos, Niamh Belton, Ronan P. Killeen, Kathleen M. Curran, Brian Mac Namee:
Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images. SGAI Conf. 2023: 391-403 - [c78]Joyce Mahon, Brian Mac Namee, Brett A. Becker:
No More Pencils No More Books: Capabilities of Generative AI on Irish and UK Computer Science School Leaving Examinations. UKICER 2023: 2:1-2:7 - [c77]Qin Ruan, Brian Mac Namee, Ruihai Dong:
The Influence of Media Bias on News Recommender Systems. UMAP 2023: 301-305 - [i36]Misgina Tsighe Hagos, Niamh Belton, Ronan P. Killeen, Kathleen M. Curran, Brian Mac Namee:
Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images. CoRR abs/2304.07097 (2023) - [i35]Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee:
Learning from Exemplary Explanations. CoRR abs/2307.06026 (2023) - [i34]Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee:
Unlearning Spurious Correlations in Chest X-ray Classification. CoRR abs/2308.01119 (2023) - [i33]Misgina Tsighe Hagos, Niamh Belton, Kathleen M. Curran, Brian Mac Namee:
Distance-Aware eXplanation Based Learning. CoRR abs/2309.05548 (2023) - [i32]Svetoslav Nizhnichenkov, Rahul Nair, Elizabeth Daly, Brian Mac Namee:
Explaining Knock-on Effects of Bias Mitigation. CoRR abs/2312.00765 (2023) - 2022
- [j25]Mehran Hossein Zadeh Bazargani, Arjun Pakrashi, Brian Mac Namee:
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection. IEEE Access 10: 70645-70661 (2022) - [c76]Jinghui Lu, Linyi Yang, Brian MacNamee, Yue Zhang:
A Rationale-Centric Framework for Human-in-the-loop Machine Learning. ACL (1) 2022: 6986-6996 - [c75]Joyce Mahon, Brett A. Becker, Brian Mac Namee:
AI and ML in School Level Computing Education: Who, What and Where? AICS 2022: 201-213 - [c74]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: 1635-1645 - [c73]Misgina Tsighe Hagos, Ronan P. Killeen, Kathleen M. Curran, Brian Mac Namee:
Interpretable Identification of Mild Cognitive Impairment Progression Using Stereotactic Surface Projections. PAIS@ECAI 2022: 153-156 - [c72]Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee:
Impact of Feedback Type on Explanatory Interactive Learning. ISMIS 2022: 127-137 - [c71]Ellen Rushe, Brian Mac Namee:
Deep Contextual Novelty Detection with Context Prediction. LIDTA 2022: 127-138 - [c70]Joyce Mahon, Keith Quille, Brian Mac Namee, Brett A. Becker:
A Novel Machine Learning and Artificial Intelligence Course for Secondary School Students. SIGCSE (2) 2022: 1155 - [c69]Payel Sadhukhan, Arjun Pakrashi, Brian Mac Namee:
Random Walk-steered Majority Undersampling. SMC 2022: 530-537 - [e4]Arjun Pakrashi, Ellen Rushe, Mehran Hossein Zadeh Bazargani, Brian Mac Namee:
The 29th Irish Conference on Artificial Intelligence and Cognitive Science 2021, Dublin, Republic of Ireland, December 9-10, 2021. CEUR Workshop Proceedings 3105, CEUR-WS.org 2022 [contents] - [i31]Jinghui Lu, Linyi Yang, Brian Mac Namee, Yue Zhang:
A Rationale-Centric Framework for Human-in-the-loop Machine Learning. CoRR abs/2203.12918 (2022) - [i30]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 abs/2204.08271 (2022) - [i29]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 abs/2204.09343 (2022) - [i28]Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee:
Impact of Feedback Type on Explanatory Interactive Learning. CoRR abs/2209.12476 (2022) - [i27]Jinghui Lu, Rui Zhao, Brian Mac Namee, Dongsheng Zhu, Weidong Han, Fei Tan:
What Makes Pre-trained Language Models Better Zero/Few-shot Learners? CoRR abs/2209.15206 (2022) - [i26]Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee:
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning. CoRR abs/2211.08285 (2022) - [i25]Jinghui Lu, Rui Zhao, Brian Mac Namee, Fei Tan:
PUnifiedNER: a Prompting-based Unified NER System for Diverse Datasets. CoRR abs/2211.14838 (2022) - 2021
- [j24]Liang Zhao, Kun Chen, Jie Song, Xiaoliang Zhu, Jianwen Sun, Brian Caulfield, Brian Mac Namee:
Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data. IEEE Access 9: 5453-5465 (2021) - [j23]Arjun Pakrashi, Brian Mac Namee:
A multi-label cascaded neural network classification algorithm for automatic training and evolution of deep cascaded architecture. Expert Syst. J. Knowl. Eng. 38(7) (2021) - [j22]Min Jing, Kok Yew Ng, Brian Mac Namee, Pardis Biglarbeigi, Rob Brisk, Raymond R. Bond, Dewar D. Finlay, James McLaughlin:
COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps. J. Biomed. Informatics 122: 103905 (2021) - [c68]James Murphy, John E. Ward, Brian Mac Namee:
Machine Learning in Space: A Review of Machine Learning Algorithms and Hardware for Space Applications. AICS 2021: 72-83 - [c67]Qin Ruan, Brian Mac Namee, Ruihai Dong:
Bias Bubbles: Using Semi-Supervised Learning to Measure How Many Biased News Articles Are Around Us. AICS 2021: 153-164 - [c66]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: 176-187 - [c65]Jinghui Lu, Maeve Henchion, Ivan Bacher, Brian Mac Namee:
A Sentence-Level Hierarchical BERT Model for Document Classification with Limited Labelled Data. DS 2021: 231-241 - [c64]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: 1284-1293 - [c63]John Mitros, Brian Mac Namee:
On the Importance of Regularisation and Auxiliary Information in OOD Detection. ICONIP (6) 2021: 361-368 - [c62]Mohamed Saadeldin, Brian MacNamee:
An Orthogonal Classification Layer with Kasami Sequences for Discriminative Feature Learning in Neural Networks. ICTAI 2021: 370-375 - [c61]Arjun Pakrashi, Payel Sadhukhan, Brian Mac Namee:
ML-NCA: Multi-label Neighbourhood Component Analysis. LIDTA@ECML/PKDD 2021: 35-48 - [i24]Cathal Ryan, Christophe Guéret, Donagh Berry, Medb Corcoran, Mark T. Keane, Brian Mac Namee:
Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cows. CoRR abs/2101.02188 (2021) - [i23]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 abs/2101.03198 (2021) - [i22]Mehran Hossein Zadeh Bazargani, Arjun Pakrashi, Brian Mac Namee:
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection. CoRR abs/2101.12632 (2021) - [i21]Jinghui Lu, Maeve Henchion, Ivan Bacher, Brian Mac Namee:
A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data. CoRR abs/2106.06738 (2021) - [i20]John Mitros, Brian Mac Namee:
On the Importance of Regularisation & Auxiliary Information in OOD Detection. CoRR abs/2107.07564 (2021) - [i19]Qin Ruan, Brian Mac Namee, Ruihai Dong:
Pseudo-labelling Enhanced Media Bias Detection. CoRR abs/2107.07705 (2021) - [i18]Payel Sadhukhan, Arjun Pakrashi, Sarbani Palit, Brian Mac Namee:
Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data. CoRR abs/2109.12421 (2021) - [i17]Payel Sadhukhan, Arjun Pakrashi, Brian Mac Namee:
Random Walk-steered Majority Undersampling. CoRR abs/2109.12423 (2021) - [i16]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 abs/2110.13719 (2021) - 2020
- [j21]Arjun Pakrashi, Brian Mac Namee:
KalmanTune: A Kalman Filter Based Tuning Method to Make Boosted Ensembles Robust to Class-Label Noise. IEEE Access 8: 145887-145897 (2020) - [j20]Elham Alghamdi, Ellen Rushe, Brian Mac Namee, Derek Greene:
Overlapping community finding with noisy pairwise constraints. Appl. Netw. Sci. 5(1): 98 (2020) - [j19]Elizabeth Hunter, Brian Mac Namee, John D. Kelleher:
A Hybrid Agent-Based and Equation Based Model for the Spread of Infectious Diseases. J. Artif. Soc. Soc. Simul. 23(4) (2020) - [c60]Min Jing, Donal McLaughlin, David Steele, Sara McNamee, Brian MacNamee, Patrick Cullen, Dewar D. Finlay, James McLaughlin:
Detection and Categorisation of Multilevel High-sensitivity Cardiovascular Biomarkers from Lateral Flow Immunoassay Images via Recurrent Neural Networks. BIOIMAGING 2020: 177-183 - [c59]John Mitros, Arjun Pakrashi, Brian Mac Namee:
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings. ECCV Workshops (1) 2020: 71-87 - [c58]Min Jing, Brian Mac Namee, Donal McLaughlin, David Steele, Sara McNamee, Patrick Cullen, Dewar D. Finlay, James McLaughlin:
Enhance Categorisation Of Multilevel High-Sensitivity Cardiovascular Biomarkers From Lateral Flow Immunoassay Images Via Neural Networks And Dynamic Time Warping. ICIP 2020: 365-369 - [c57]Jinghui Lu, Maeve Henchion, Brian Mac Namee:
Diverging Divergences: Examining Variants of Jensen Shannon Divergence for Corpus Comparison Tasks. LREC 2020: 6740-6744 - [c56]Luis Miralles-Pechuán, Matthieu Bellucci, Muhammad Atif Qureshi, Brian Mac Namee:
ZeChipC: Time Series Interpolation Method Based on Lebesgue Sampling. MICAI (1) 2020: 182-196 - [c55]Muhammad Atif Qureshi, Luis Miralles-Pechuán, Jason Payne, Ronan O'Malley, Brian Mac Namee:
Valve Health Identification Using Sensors and Machine Learning Methods. IoT Streams/ITEM@PKDD/ECML 2020: 45-60 - [i15]Jinghui Lu, Brian MacNamee:
Investigating the Effectiveness of Representations Based on Pretrained Transformer-based Language Models in Active Learning for Labelling Text Datasets. CoRR abs/2004.13138 (2020) - [i14]Ellen Rushe, Brian Mac Namee:
Deep Context-Aware Novelty Detection. CoRR abs/2006.01168 (2020) - [i13]John Mitros, Arjun Pakrashi, Brian Mac Namee:
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings. CoRR abs/2009.01798 (2020) - [i12]Cathal Ryan, Christophe Guéret, Donagh Berry, Brian Mac Namee:
Can We Detect Mastitis earlier than Farmers? CoRR abs/2011.03344 (2020)
2010 – 2019
- 2019
- [j18]Arjun Pakrashi, Brian Mac Namee:
Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters. Inf. Sci. 485: 456-485 (2019) - [c54]Jinghui Lu, Maeve Henchion, Brian Mac Namee:
A Topic-Based Approach to Multiple Corpus Comparison. AICS 2019: 64-75 - [c53]John Mitros, Brian Mac Namee:
On the Validity of Bayesian Neural Networks for Uncertainty Estimation. AICS 2019: 140-151 - [c52]Ellen Rushe, Brian Mac Namee:
Anomaly Detection in Raw Audio Using Deep Autoregressive Networks. ICASSP 2019: 3597-3601 - [c51]Mehran Hossein Zadeh Bazargani, Brian Mac Namee:
The Elliptical Basis Function Data Descriptor (EBFDD) Network: A One-Class Classification Approach to Anomaly Detection. ECML/PKDD (1) 2019: 107-123 - [c50]Arjun Pakrashi, Brian Mac Namee:
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification (Best Technical Paper). SGAI Conf. 2019: 3-17 - [c49]Niladri Sett, Brian Mac Namee, Francesc Calvo, Brian Caulfield, John Costello, Seamas Donnelly, Jonas F. Dorn, Louis Jeay, Alison Keogh, Killian McManus, Ronan H. Mullan, Emer O'Hare, Caroline G. M. Perraudin:
Are You in Pain? Predicting Pain and Stiffness from Wearable Sensor Activity Data. SGAI Conf. 2019: 183-197 - [e3]Ulf Brefeld, Edward Curry, Elizabeth Daly, Brian MacNamee, Alice Marascu, Fabio Pinelli, Michele Berlingerio, Neil Hurley:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III. Lecture Notes in Computer Science 11053, Springer 2019, ISBN 978-3-030-10996-7 [contents] - [i11]John Mitros, Brian Mac Namee:
A Categorisation of Post-hoc Explanations for Predictive Models. CoRR abs/1904.02495 (2019) - [i10]Arjun Pakrashi, Brian Mac Namee:
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification. CoRR abs/1904.10551 (2019) - [i9]Arjun Pakrashi, Brian Mac Namee:
KFHE-HOMER: Kalman Filter-based Heuristic Ensemble of HOMER for Multi-Label Classification. CoRR abs/1904.10552 (2019) - [i8]Matthieu Bellucci, Luis Miralles, Muhammad Atif Qureshi, Brian Mac Namee:
ZeLiC and ZeChipC: Time Series Interpolation Methods for Lebesgue or Event-based Sampling. CoRR abs/1906.03110 (2019) - [i7]Jinghui Lu, Maeve Henchion, Brian Mac Namee:
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets. CoRR abs/1910.03505 (2019) - [i6]Luis Miralles, Muhammad Atif Qureshi, Brian Mac Namee:
Real-time Bidding campaigns optimization using attribute selection. CoRR abs/1910.13292 (2019) - [i5]John Mitros, Brian Mac Namee:
On the Validity of Bayesian Neural Networks for Uncertainty Estimation. CoRR abs/1912.01530 (2019) - 2018
- [j17]Quan Le, Oisín Boydell, Brian Mac Namee, Mark Scanlon:
Deep learning at the shallow end: Malware classification for non-domain experts. Digit. Investig. 26 Supplement: S118-S126 (2018) - [j16]Mark Belford, Brian Mac Namee, Derek Greene:
Stability of topic modeling via matrix factorization. Expert Syst. Appl. 91: 159-169 (2018) - [j15]Elizabeth Hunter, Brian Mac Namee, John D. Kelleher:
Using a Socioeconomic Segregation Burn-in Model to Initialise an Agent-Based Model for Infectious Diseases. J. Artif. Soc. Soc. Simul. 21(4) (2018) - [c48]Elizabeth Hunter, Brian Mac Namee, John D. Kelleher:
A Comparison of Agent-Based Models and Equation Based Models for Infectious Disease Epidemiology. AICS 2018: 33-44 - [c47]Arjun Pakrashi, Elham Alghamdi, Brian Mac Namee, Derek Greene:
MeetupNet Dublin: Discovering Communities in Dublin's Meetup Network. AICS 2018: 114-125 - [c46]Niamh Donnelly, Conor Nugent, Brian Mac Namee:
Identifying Urban Canopy Coverage from Satellite Imagery Using Convolutional Neural Networks. AICS 2018: 315-326 - [c45]Shen Wang, Aditya Grover, Brian Mac Namee, Philip Plantholt, Javier Lopez-Leones, Pablo Sanchez-Escalonilla:
ROGER: An On-Line Flight Efficiency Monitoring System Using ADS-B Data. MDM 2018: 233-238 - [c44]Jack O'Neill, Sarah Jane Delany, Brian Mac Namee:
Rank Scoring via Active Learning (RaScAL). HumL@ISWC 2018: 38-50 - [c43]Jack O'Neill, Sarah Jane Delany, Brian Mac Namee:
From Rankings to Ratings: Rank Scoring via Active Learning. ISWC (Best Workshop Papers) 2018: 69-81 - [c42]Ivan Bacher, Brian Mac Namee, John D. Kelleher:
Scoped: Evaluating A Composite Visualisation of the Scope Chain Hierarchy Within Source Code. VISSOFT 2018: 117-121 - [c41]Ivan Bacher, Brian Mac Namee, John D. Kelleher:
The Code Mini-Map Visualisation: Encoding Conceptual Structures Within Source Code. VISSOFT 2018: 127-131 - [i4]Quan Le, Oisín Boydell, Brian Mac Namee, Mark Scanlon:
Deep learning at the shallow end: Malware classification for non-domain experts. CoRR abs/1807.08265 (2018) - [i3]Arjun Pakrashi, Brian Mac Namee:
Kalman Filter-based Heuristic Ensemble: A New Perspective on Ensemble Classification Using Kalman Filters. CoRR abs/1807.11429 (2018) - [i2]Arjun Pakrashi, Elham Alghamdi, Brian Mac Namee, Derek Greene:
MeetupNet Dublin: Discovering Communities in Dublin's Meetup Network. CoRR abs/1810.03046 (2018) - 2017
- [j14]Niels Schütte, Brian Mac Namee, John D. Kelleher:
Robot perception errors and human resolution strategies in situated human-robot dialogue. Adv. Robotics 31(5): 243-257 (2017) - [j13]Elizabeth Hunter, Brian Mac Namee, John D. Kelleher:
A Taxonomy for Agent-Based Models in Human Infectious Disease Epidemiology. J. Artif. Soc. Soc. Simul. 20(3) (2017) - [c40]Brian Mac Namee, John D. Kelleher, Noel Fitzpatrick:
Assessing the Usefulness of Different Feature Sets for Predicting the Comprehension Difficulty of Text. AICS 2017: 12-25 - [c39]Mark Belford, Brian Mac Namee, Derek Greene:
Synthetic Dataset Generation for Online Topic Modeling. AICS 2017: 63-75 - [c38]Jinghui Lu, Maeve Henchion, Brian Mac Namee:
Extending Jensen Shannon Divergence to Compare Multiple Corpora. AICS 2017: 76-88 - [c37]Ivan Bacher, Brian Mac Namee, John D. Kelleher:
The Code-Map Metaphor - A Review of Its Use Within Software Visualisations. VISIGRAPP (3: IVAPP) 2017: 17-28 - [c36]Arjun Pakrashi, Brian Mac Namee:
Stacked-MLkNN: A stacking based improvement to Multi-Label k-Nearest Neighbours. LIDTA@PKDD/ECML 2017: 51-63 - [c35]Jack O'Neill, Sarah Jane Delany, Brian Mac Namee:
Rating by Ranking: An Improved Scale for Judgement-Based Labels. IntRS@RecSys 2017: 24-29 - [c34]Ivan Bacher, Brian Mac Namee, John D. Kelleher:
Scoped: Visualising the Scope Chain Within Source Code. EuroVis (Short Papers) 2017: 115-119 - [i1]Mark Belford, Brian Mac Namee, Derek Greene:
Stability of Topic Modeling via Matrix Factorization. CoRR abs/1702.07186 (2017) - 2016
- [j12]Rong Hu, Brian Mac Namee, Sarah Jane Delany:
Active learning for text classification with reusability. Expert Syst. Appl. 45: 438-449 (2016) - [j11]Arkaitz Zubiaga, Brian Mac Namee:
Graphical Perception of Value Distributions: An Evaluation of Non-Expert Viewers' Data Literacy. J. Community Informatics 12(3) (2016) - [c33]Mark Belford, Brian Mac Namee, Derek Greene:
Ensemble Topic Modeling via Matrix Factorization. AICS 2016: 21-32 - [c32]Elizabeth Hunter, Brian Mac Namee, John D. Kelleher:
An Open Data Driven Epidemiological Agent-Based Model for Irish Towns. AICS 2016: 92-103 - [c31]Jack O'Neill, Sarah Jane Delany, Brian Mac Namee:
Activist: A New Framework for Dataset Labelling. AICS 2016: 140-148 - [c30]Arjun Pakrashi, Derek Greene, Brian Mac Namee:
Benchmarking Multi-label Classification Algorithms. AICS 2016: 149-160 - [c29]Jack O'Neill, Sarah Jane Delany, Brian Mac Namee:
Model-Free and Model-Based Active Learning for Regression. UKCI 2016: 375-386 - [c28]Ivan Bacher, Brian Mac Namee, John D. Kelleher:
On Using Tree Visualisation Techniques to Support Source Code Comprehension. VISSOFT 2016: 91-95 - [c27]Ivan Bacher, Brian Mac Namee, John D. Kelleher:
Using Icicle Trees to Encode the Hierarchical Structure of Source Code. EuroVis (Short Papers) 2016: 97-101 - [e2]Derek Greene, Brian Mac Namee, Robert J. Ross:
Proceedings of the 24th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2016, Dublin, Ireland, September 20-21, 2016. CEUR Workshop Proceedings 1751, CEUR-WS.org 2016 [contents] - 2014
- [j10]Alexey Tarasov, Sarah Jane Delany, Brian Mac Namee:
Dynamic estimation of worker reliability in crowdsourcing for regression tasks: Making it work. Expert Syst. Appl. 41(14): 6190-6210 (2014) - [c26]Niels Schütte, John D. Kelleher, Brian Mac Namee:
The Effect of Sensor Errors in Situated Human-Computer Dialogue. VL@COLING 2014: 1-8 - [c25]Eoghan O'Shea, Sarah Jane Delany, Rob Lane, Brian Mac Namee:
NudgeAlong: A Case Based Approach to Changing User Behaviour. ICCBR 2014: 345-359 - [c24]Niels Schütte, John D. Kelleher, Brian Mac Namee:
Clarification Dialogues for Perception-based Errors in Situated Human-Computer Dialogues. MMRWHRI@ICMI 2014: 25-26 - 2013
- [j9]Kenneth Kennedy, Brian Mac Namee, Sarah Jane Delany, M. O'Sullivan, N. Watson:
A window of opportunity: Assessing behavioural scoring. Expert Syst. Appl. 40(4): 1372-1380 (2013) - [j8]Patrick Lindstrom, Brian Mac Namee, Sarah Jane Delany:
Drift detection using uncertainty distribution divergence. Evol. Syst. 4(1): 13-25 (2013) - [j7]Kenneth Kennedy, Brian Mac Namee, Sarah Jane Delany:
Using semi-supervised classifiers for credit scoring. J. Oper. Res. Soc. 64(4): 513-529 (2013) - [c23]Yan Li, Brian Mac Namee, John D. Kelleher:
Expecting the Unexpected: Measure the Uncertainties for Mobile Robot Path Planning in Dynamic Environment. TAROS 2013: 363-374 - 2012
- [j6]Sarah Jane Delany, Nicola Segata, Brian Mac Namee:
Profiling instances in noise reduction. Knowl. Based Syst. 31: 28-40 (2012) - [c22]Dmitry Strunkin, Brian Mac Namee, John D. Kelleher:
An Investigation Into Feature Selection for Oncological Survival Prediction. ITNG 2012: 764-768 - [c21]Mark Dunne, Brian Mac Namee, John D. Kelleher:
The Turning, Stretching and Boxing Technique: A Step in the Right Direction. IVA 2012: 363-369 - [c20]Alexey Tarasov, Sarah Jane Delany, Brian Mac Namee:
Dynamic Estimation of Rater Reliability in Subjective Tasks Using Multi-armed Bandits. SocialCom/PASSAT 2012: 979-980 - 2011
- [j5]John D. Kelleher, Robert J. Ross, Colm Sloan, Brian Mac Namee:
The effect of occlusion on the semantics of projective spatial terms: a case study in grounding language in perception. Cogn. Process. 12(1): 95-108 (2011) - [j4]Niels Schütte, John D. Kelleher, Brian Mac Namee:
Automatic Annotation of Referring Expressions in Situated Dialogues. Int. J. Comput. Linguistics Appl. 2(1-2): 175-190 (2011) - [c19]Colm Sloan, John D. Kelleher, Brian Mac Namee:
Feasibility study of utility-directed behaviour for computer game agents. Advances in Computer Entertainment Technology 2011: 5 - [c18]Colm Sloan, John D. Kelleher, Brian Mac Namee:
Feeling the ambiance: using smart ambiance to increase contextual awareness in game agents. FDG 2011: 298-300 - [c17]Patrick Lindstrom, Brian Mac Namee, Sarah Jane Delany:
Drift Detection Using Uncertainty Distribution Divergence. ICDM Workshops 2011: 604-608 - 2010
- [j3]Brian Mac Namee, David Beaney, Qingqing Dong:
Motion in Augmented Reality Games: An Engine for Creating Plausible Physical Interactions in Augmented Reality Games. Int. J. Comput. Games Technol. 2010: 979235:1-979235:8 (2010) - [c16]John D. Kelleher, Robert J. Ross, Brian Mac Namee, Colm Sloan:
Situating Spatial Templates for Human-Robot Interaction. AAAI Fall Symposium: Dialog with Robots 2010 - [c15]Niels Schuette, John D. Kelleher, Brian Mac Namee:
Visual Salience and Reference Resolution in Situated Dialogues: A Corpus-based Evaluation. AAAI Fall Symposium: Dialog with Robots 2010 - [c14]Rong Hu, Brian Mac Namee, Sarah Jane Delany:
Off to a Good Start: Using Clustering to Select the Initial Training Set in Active Learning. FLAIRS 2010 - [c13]Patrick Lindstrom, Sarah Jane Delany, Brian Mac Namee:
Handling Concept Drift in a Text Data Stream Constrained by High Labelling Cost. FLAIRS 2010 - [c12]Rong Hu, Sarah Jane Delany, Brian Mac Namee:
EGAL: Exploration Guided Active Learning for TCBR. ICCBR 2010: 156-170 - [c11]Brian Mac Namee, Sarah Jane Delany:
CBTV: Visualising Case Bases for Similarity Measure Design and Selection. ICCBR 2010: 213-227 - [e1]John D. Kelleher, Brian Mac Namee, Ielka van der Sluis, Anja Belz, Albert Gatt, Alexander Koller:
INLG 2010 - Proceedings of the Sixth International Natural Language Generation Conference, July 7-9, 2010, Trim, Co. Meath, Ireland. The Association for Computer Linguistics 2010 [contents]
2000 – 2009
- 2009
- [c10]Nicholas Hanlon, Brian Mac Namee, John D. Kelleher:
Just Say It: An Evaluation of Speech Interfaces for Augmented Reality Design Applications. AICS 2009: 134-143 - [c9]Kenneth Kennedy, Brian Mac Namee, Sarah Jane Delany:
Learning without Default: A Study of One-Class Classification and the Low-Default Portfolio Problem. AICS 2009: 174-187 - [c8]David Beaney, Brian Mac Namee:
Forked! A demonstration of physics realism in augmented reality. ISMAR 2009: 171-172 - [c7]Brian Mac Namee, Mark Dunne:
Widening the Evaluation Net. IVA 2009: 525-526 - [c6]Pauline Rooney, K. C. O'Rourke, Greg Burke, Brian MacNamee, Claudia Igbrude:
Cross-Disciplinary Approaches for Developing Serious Games in Higher Education. VS-GAMES 2009: 161-165 - [r1]Brian Mac Namee:
Computer Graphics and Games, Agent Based Modeling in. Encyclopedia of Complexity and Systems Science 2009: 1335-1352 - 2008
- [c5]Qian Zhang, Rong Hu, Brian Mac Namee, Sarah Jane Delany:
Back to the Future: Knowledge Light Case Base Cookery. ECCBR Workshops 2008: 239-248 - [c4]John D. Kelleher, Brian Mac Namee:
Referring Expression Generation Challenge 2008 DIT System Descriptions (DIT-FBI, DIT-TVAS, DIT-CBSR, DIT-RBR, DIT-FBI-CBSR, DIT-TVAS-RBR). INLG 2008 - 2003
- [j2]Brian MacNamee, Padraig Cunningham:
Creating socially interactive no-player characters: The µ-SIV system. Int. J. Intell. Games Simul. 2(1): 28-35 (2003) - [c3]Brian MacNamee, Simon Dobbyn, Padraig Cunningham, Carol O'Sullivan:
Simulating Virtual Humans Across Diverse Situations. IVA 2003: 159-163 - [c2]Christopher Peters, Simon Dobbyn, Brian MacNamee, Carol O'Sullivan:
Smart Objects for Attentive Agents. WSCG 2003 - 2002
- [j1]Brian MacNamee, Padraig Cunningham, Stephen Byrne, O. I. Corrigan:
The problem of bias in training data in regression problems in medical decision support. Artif. Intell. Medicine 24(1): 51-70 (2002) - [c1]Brian MacNamee, Padraig Cunningham:
The u-SIG System; A Connectionist Driven Simulation of Socially Interactive Agents. GAME-ON 2002
Coauthor Index
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