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2020 – today
- 2024
[j8]Siteng Ma, Prateek Mathur, Zheng Ju, Aonghus Lawlor
, Ruihai Dong:
Model-data-driven adversarial active learning for brain tumor segmentation. Comput. Biol. Medicine 176: 108585 (2024)- [j7]Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor, Kathleen M. Curran:
Towards a unified approach for unsupervised brain MRI Motion Artefact Detection with few shot Anomaly Detection. Comput. Medical Imaging Graph. 115: 102391 (2024) - [c51]Neil Hurley, Erika Duriakova, James Geraci, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Aonghus Lawlor:
ALS Algorithm for Robust and Communication-Efficient Federated Learning. EuroMLSys@EuroSys 2024: 56-64 - [c50]Diarmuid O'Reilly-Morgan, Elias Z. Tragos, James Geraci, Qinqin Wang, Neil Hurley, Barry Smyth, Aonghus Lawlor:
A Hybrid Decentralised Learning Topology for Recommendations with Improved Privacy. EuroMLSys@EuroSys 2024: 161-168 - [c49]Siteng Ma, Haochang Wu, Aonghus Lawlor, Ruihai Dong:
Breaking the Barrier: Selective Uncertainty-Based Active Learning for Medical Image Segmentation. ICASSP 2024: 1531-1535 - [c48]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
A Case-Based Reasoning Approach to Post-injury Training Recommendations for Marathon Runners. ICCBR 2024: 338-353 - [c47]Daniel Anojan Atputharuban, Christoph Theopold, Aonghus Lawlor:
Enhancing Surgical Visualization: Feasibility Study on GAN-Based Image Generation for Post Operative Cleft Palate Images. ICPRAM 2024: 939-945 - [c46]Siteng Ma, Honghui Du, Kathleen M. Curran, Aonghus Lawlor, Ruihai Dong:
Adaptive Curriculum Query Strategy for Active Learning in Medical Image Classification. MICCAI (11) 2024: 48-57 - [c45]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Recommending Personalised Targeted Training Adjustments for Marathon Runners. RecSys 2024: 1051-1056 - [c44]Zheng Ju, Honghui Du, Elias Z. Tragos, Neil Hurley, Aonghus Lawlor:
Exploring Coresets for Efficient Training and Consistent Evaluation of Recommender Systems. RecSys 2024: 1152-1157 - [i12]Siteng Ma, Haochang Wu, Aonghus Lawlor, Ruihai Dong:
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation. CoRR abs/2401.16298 (2024) - [i11]Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor, Kathleen M. Curran:
Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach. CoRR abs/2407.09515 (2024) - [i10]Niamh Belton, Aonghus Lawlor, Kathleen M. Curran:
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data. CoRR abs/2407.11500 (2024) - 2023
- [c43]Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor, Kathleen M. Curran:
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks. CVPR Workshops 2023: 2978-2987 - [c42]Edoardo D'Amico, Khalil Muhammad, Elias Z. Tragos, Barry Smyth, Neil Hurley, Aonghus Lawlor:
Item Graph Convolution Collaborative Filtering for Inductive Recommendations. ECIR (1) 2023: 249-263 - [c41]Aayush Singha Roy, Edoardo D'Amico, Aonghus Lawlor, Neil Hurley:
Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems. FLAIRS 2023 - [c40]Haiwen Du, Zheng Ju, Yu An, Honghui Du, Dongjie Zhu, Zhaoshuo Tian, Aonghus Lawlor, Ruihai Dong:
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases. ICONIP (15) 2023: 125-139 - [c39]Siteng Ma, Yu An, Jing Wang, Aonghus Lawlor, Ruihai Dong:
Adaptive Adversarial Samples Based Active Learning for Medical Image Classification. ICPRAM 2023: 751-758 - [c38]Elias Z. Tragos, Diarmuid O'Reilly-Morgan, James Geraci, Bichen Shi, Barry Smyth, Cailbhe Doherty, Aonghus Lawlor, Neil Hurley:
Keeping People Active and Healthy at Home Using a Reinforcement Learning-based Fitness Recommendation Framework. IJCAI 2023: 6237-6245 - [c37]Edoardo D'Amico, Aonghus Lawlor, Neil Hurley:
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation. PAKDD (3) 2023: 310-321 - [c36]Aayush Singha Roy, Edoardo D'Amico, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley:
Scalable Deep Q-Learning for Session-Based Slate Recommendation. RecSys 2023: 877-882 - [c35]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations. UMAP 2023: 183-193 - [i9]Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor, Kathleen M. Curran:
FewSOME: Few Shot Anomaly Detection. CoRR abs/2301.06957 (2023) - [i8]Edoardo D'Amico, Khalil Muhammad, Elias Z. Tragos, Barry Smyth, Neil Hurley, Aonghus Lawlor:
Item Graph Convolution Collaborative Filtering for Inductive Recommendations. CoRR abs/2303.15946 (2023) - [i7]Edoardo D'Amico, Aonghus Lawlor, Neil Hurley:
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation. CoRR abs/2305.18374 (2023) - [i6]Haiwen Du, Zheng Ju, Yu An, Honghui Du, Dongjie Zhu, Zhaoshuo Tian, Aonghus Lawlor, Ruihai Dong:
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases. CoRR abs/2308.01138 (2023) - [i5]Dairui Liu, Boming Yang, Honghui Du, Derek Greene, Aonghus Lawlor, Ruihai Dong, Irene Li:
RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models. CoRR abs/2312.10463 (2023) - 2022
- [j6]Qinqin Wang, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Barry Smyth, Aonghus Lawlor, Ruihai Dong:
Learning Domain-Independent Representations via Shared Weight Auto-Encoder for Transfer Learning in Recommender Systems. IEEE Access 10: 71961-71972 (2022) - [j5]Barry Smyth, Aonghus Lawlor, Jakim Berndsen, Ciara Feely:
Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners. User Model. User Adapt. Interact. 32(5): 787-838 (2022) - [c34]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
An Extended Case-Based Approach to Race-Time Prediction for Recreational Marathon Runners. ICCBR 2022: 335-349 - [c33]Qinqin Wang, Khalil Muhammad, Diarmuid O'Reilly-Morgan, Barry Smyth, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley, Ruihai Dong:
MARF: User-Item Mutual Aware Representation with Feedback. ICWE 2022: 3-15 - [c32]Qinqin Wang, Elias Z. Tragos, Neil Hurley, Barry Smyth, Aonghus Lawlor, Ruihai Dong:
Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation. UMAP 2022: 79-88 - 2021
- [j4]Sixun Ouyang, Aonghus Lawlor:
Improving Explainable Recommendations by Deep Review-Based Explanations. IEEE Access 9: 67444-67455 (2021) - [j3]Bichen Shi, Elias Z. Tragos, Makbule Gulcin Ozsoy, Ruihai Dong, Neil Hurley, Barry Smyth, Aonghus Lawlor:
DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning. IEEE Access 9: 83340-83354 (2021) - [c31]Erika Duriakova, Elias Z. Tragos, Aonghus Lawlor, Barry Smyth, Neil Hurley:
Boosting the Training Time of Weakly Coordinated Distributed Machine Learning. IEEE BigData 2021: 1023-1029 - [c30]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries. ICCBR 2021: 79-93 - [c29]Niamh Belton, Ivan Welaratne, Adil Dahlan, Ronan T. Hearne, Misgina Tsighe Hagos, Aonghus Lawlor, Kathleen M. Curran:
Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability. MIUA 2021: 71-86 - [i4]Niamh Belton, Aonghus Lawlor, Kathleen M. Curran:
Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasets. CoRR abs/2108.07130 (2021) - [i3]Niamh Belton, Ivan Welaratne, Adil Dahlan, Ronan T. Hearne, Misgina Tsighe Hagos, Aonghus Lawlor, Kathleen M. Curran:
Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability. CoRR abs/2108.08136 (2021) - 2020
- [j2]Makbule Gulcin Ozsoy, Diarmuid O'Reilly-Morgan, Panagiotis Symeonidis, Elias Z. Tragos, Neil Hurley, Barry Smyth, Aonghus Lawlor:
MP4Rec: Explainable and Accurate Top-N Recommendations in Heterogeneous Information Networks. IEEE Access 8: 181835-181847 (2020) - [c28]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans. ICCBR 2020: 67-81 - [c27]Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
A Collaborative Filtering Approach to Successfully Completing The Marathon. ICMLA 2020: 653-658 - [c26]Khalil Muhammad, Qinqin Wang, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Neil Hurley, James Geraci, Aonghus Lawlor:
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems. KDD 2020: 1234-1242 - [c25]Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
Mining Marathon Training Data to Generate Useful User Profiles. MLSA@PKDD/ECML 2020: 113-125 - [c24]Erika Duriakova, Weipéng Huáng, Elias Z. Tragos, Aonghus Lawlor, Barry Smyth, James Geraci, Neil Hurley:
An Algorithmic Framework for Decentralised Matrix Factorisation. ECML/PKDD (2) 2020: 307-323 - [c23]Francisco J. Peña, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Erika Duriakova, Barry Smyth, Aonghus Lawlor:
Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation. RecSys 2020: 438-443 - [c22]Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
Fit to Run: Personalised Recommendations for Marathon Training. RecSys 2020: 480-485 - [c21]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners. RecSys 2020: 539-544
2010 – 2019
- 2019
- [c20]Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
Pace my race: recommendations for marathon running. RecSys 2019: 246-250 - [c19]Erika Duriakova, Elias Z. Tragos, Barry Smyth, Neil Hurley, Francisco J. Peña, Panagiotis Symeonidis, James Geraci, Aonghus Lawlor:
PDMFRec: a decentralised matrix factorisation with tunable user-centric privacy. RecSys 2019: 457-461 - [c18]Bichen Shi, Makbule Gulcin Ozsoy, Neil Hurley, Barry Smyth, Elias Z. Tragos, James Geraci, Aonghus Lawlor:
PyRecGym: a reinforcement learning gym for recommender systems. RecSys 2019: 491-495 - [c17]Cataldo Musto, Amon Rapp, Federica Cena, Frank Hopfgartner, Judy Kay, Aonghus Lawlor, Pasquale Lops, Giovanni Semeraro, Nava Tintarev:
UMAP 2019 Workshop on Explainable and Holistic User Modeling (ExHUM) Chairs' Welcome & Organization. UMAP (Adjunct Publication) 2019: 225-227 - [c16]Sixun Ouyang, Aonghus Lawlor:
NEAR: A Partner to Explain Any Factorised Recommender System. UMAP (Adjunct Publication) 2019: 247-249 - 2018
- [c15]Khalil Muhammad, Aonghus Lawlor, Barry Smyth:
A Multi-Domain Analysis of Explanation-Based Recommendation using User-Generated Reviews. FLAIRS 2018: 474-477 - [c14]Felipe Costa, Sixun Ouyang, Peter Dolog, Aonghus Lawlor:
Automatic Generation of Natural Language Explanations. IUI Companion 2018: 57:1-57:2 - [i2]Sixun Ouyang, Aonghus Lawlor, Felipe Costa, Peter Dolog:
Improving Explainable Recommendations with Synthetic Reviews. CoRR abs/1807.06978 (2018) - 2017
- [c13]Khalil Muhammad, Aonghus Lawlor, Barry Smyth:
On the Pros and Cons of Explanation-Based Ranking. ICCBR 2017: 227-241 - [c12]Jakim Berndsen, Aonghus Lawlor, Barry Smyth:
Running with Recommendation. HealthRecSys@RecSys 2017: 18-21 - [i1]Felipe Costa, Sixun Ouyang, Peter Dolog, Aonghus Lawlor:
Automatic Generation of Natural Language Explanations. CoRR abs/1707.01561 (2017) - 2016
- [c11]Khalil Muhammad, Aonghus Lawlor, Barry Smyth:
Explanation-based Ranking in Opinionated Recommender Systems. AICS 2016: 128-139 - [c10]Khalil Muhammad, Aonghus Lawlor, Barry Smyth:
On the Use of Opinionated Explanations to Rank and Justify Recommendations. FLAIRS 2016: 554-559 - [c9]Sergio Oramas, Luis Espinosa Anke, Aonghus Lawlor, Xavier Serra, Horacio Saggion:
Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies. ISMIR 2016: 150-156 - [c8]Khalil Ibrahim Muhammad, Aonghus Lawlor, Barry Smyth:
A Live-User Study of Opinionated Explanations for Recommender Systems. IUI 2016: 256-260 - 2015
- [c7]Khalil Muhammad, Aonghus Lawlor, Rachael Rafter, Barry Smyth:
Great Explanations: Opinionated Explanations for Recommendations. ICCBR 2015: 244-258 - [c6]Aonghus Lawlor, Khalil Muhammad, Rachael Rafter, Barry Smyth:
Opinionated Explanations for Recommendation Systems. SGAI Conf. 2015: 331-344 - [c5]Doychin Doychev, Rachael Rafter, Aonghus Lawlor, Barry Smyth:
News Recommenders: Real-Time, Real-Life Experiences. UMAP 2015: 337-342 - [c4]Khalil Muhammad, Aonghus Lawlor, Rachael Rafter, Barry Smyth:
Generating Personalised and Opinionated Review Summaries. UMAP Workshops 2015 - 2014
- [j1]Gavin McArdle, Eoghan Furey, Aonghus Lawlor, Alexei Pozdnoukhov:
Using Digital Footprints for a City-Scale Traffic Simulation. ACM Trans. Intell. Syst. Technol. 5(3): 41:1-41:16 (2014) - [c3]Doychin Doychev, Aonghus Lawlor, Rachael Rafter:
An Analysis of Recommender Algorithms for Online News. CLEF (Working Notes) 2014: 825-836 - 2012
- [c2]Gavin McArdle, Aonghus Lawlor, Eoghan Furey, Alexei Pozdnoukhov:
City-scale traffic simulation from digital footprints. UrbComp@KDD 2012: 47-54
2000 – 2009
- 2009
- [r1]Paolo De Gregorio, Aonghus Lawlor, Kenneth A. Dawson:
Bootstrap Percolation. Encyclopedia of Complexity and Systems Science 2009: 608-626 - 2004
- [c1]Paolo De Gregorio, Aonghus Lawlor, Phil Bradley, Kenneth A. Dawson:
Cellular Automata with Rare Events; Resolution of an Outstanding Problem in the Bootstrap Percolation Model. ACRI 2004: 365-374
Coauthor Index
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last updated on 2024-10-23 21:25 CEST by the dblp team
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