| A framework for selecting deep learning hyper-parameters JO Donoghue, M Roantree British international conference on databases, 120-132, 2015 | 14 | 2015 |
| Mapping longitudinal studies to risk factors in an ontology for dementia M Roantree, J O’Donoghue, N O’Kelly, M Pierce, K Irving, M Van Boxtel, ... Health Informatics Journal 22 (2), 414-426, 2016 | 13 | 2016 |
| Detecting feature interactions in agricultural trade data using a deep neural network J O’Donoghue, M Roantree, A McCarren International Conference on Big Data Analytics and Knowledge Discovery, 449-458, 2017 | 12 | 2017 |
| Automating the integration of clinical studies into medical ontologies M Roantree, J ODonoghue, N OKelly, M van Boxtel, S Köhler 2014 47th Hawaii International Conference on System Sciences, 2938-2947, 2014 | 11 | 2014 |
| Anomaly and event detection for unsupervised athlete performance data J O'Donoghue, M Roantree, B Cullen, N Moyna, C O'Sullivan, A McCarren CEUR-WS. org, 2015 | 10 | 2015 |
| A configurable deep network for high-dimensional clinical trial data J O'Donoghue, M Roantree, M Van Boxtel 2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015 | 8 | 2015 |
| Variable interactions in risk factors for dementia J O'Donoghue, M Roantree, A McCarren 2016 IEEE Tenth International Conference on Research Challenges in …, 2016 | 3 | 2016 |
| A toolkit for analysis of deep learning experiments J O’Donoghue, M Roantree Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA …, 2016 | 2 | 2016 |
| Exploring variable interactions with restricted Boltzmann machines J O'Donoghue, M Roantree | | 2015 |
| Deep learning for high dimensional and sparse clinical study data J O'Donoghue, M Roantree | | 2014 |
