| A transformer-based framework for multivariate time series representation learning G Zerveas, S Jayaraman, D Patel, A Bhamidipaty, C Eickhoff Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 1123 | 2021 |
| Machine learning for real-time prediction of complications in critical care: a retrospective study A Meyer, D Zverinski, B Pfahringer, J Kempfert, T Kuehne, ... The Lancet Respiratory Medicine 6 (12), 905-914, 2018 | 349 | 2018 |
| Quality through flow and immersion: gamifying crowdsourced relevance assessments C Eickhoff, CG Harris, AP de Vries, P Srinivasan Proceedings of the 35th international ACM SIGIR conference on Research and …, 2012 | 305 | 2012 |
| Increasing cheat robustness of crowdsourcing tasks C Eickhoff, AP de Vries Information retrieval 16, 121-137, 2013 | 275 | 2013 |
| Cognitive biases in crowdsourcing C Eickhoff Proceedings of the eleventh ACM international conference on web search and …, 2018 | 205 | 2018 |
| How much spam can you take? an analysis of crowdsourcing results to increase accuracy J Vuurens, AP de Vries, C Eickhoff Proc. ACM SIGIR Workshop on Crowdsourcing for Information Retrieval (CIR’11 …, 2011 | 200 | 2011 |
| Lessons from the journey: a query log analysis of within-session learning C Eickhoff, J Teevan, R White, S Dumais Proceedings of the 7th ACM international conference on Web search and data …, 2014 | 191 | 2014 |
| Probabilistic bag-of-hyperlinks model for entity linking OE Ganea, M Ganea, A Lucchi, C Eickhoff, T Hofmann Proceedings of the 25th international conference on world wide web, 927-938, 2016 | 168 | 2016 |
| How crowdsourcable is your task C Eickhoff, A de Vries Proceedings of the workshop on crowdsourcing for search and data mining …, 2011 | 144 | 2011 |
| The where in the tweet W Li, P Serdyukov, AP de Vries, C Eickhoff, M Larson Proceedings of the 20th ACM international conference on Information and …, 2011 | 130 | 2011 |
| Linearly mapping from image to text space J Merullo, L Castricato, C Eickhoff, E Pavlick arXiv preprint arXiv:2209.15162, 2022 | 109 | 2022 |
| Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance N Rank, B Pfahringer, J Kempfert, C Stamm, T Kühne, F Schoenrath, ... NPJ digital medicine 3 (1), 139, 2020 | 108 | 2020 |
| Advancing health equity with artificial intelligence NM Thomasian, C Eickhoff, EY Adashi Journal of public health policy 42 (4), 602, 2021 | 101 | 2021 |
| Overview of ImageCLEF 2018: Challenges, datasets and evaluation B Ionescu, H Müller, M Villegas, A García Seco de Herrera, C Eickhoff, ... Experimental IR Meets Multilinguality, Multimodality, and Interaction: 9th …, 2018 | 86 | 2018 |
| Overview of ImageCLEFcaption 2017: image caption prediction and concept detection for biomedical images C Eickhoff, I Schwall, A García Seco de Herrera, H Müller Proceedings of the CLEF 2017 working notes, 2017 | 80 | 2017 |
| Multimodal attention-based deep learning for Alzheimer’s disease diagnosis M Golovanevsky, C Eickhoff, R Singh Journal of the American Medical Informatics Association 29 (12), 2014-2022, 2022 | 72 | 2022 |
| Overview of ImageCLEF 2017: Information extraction from images B Ionescu, H Müller, M Villegas, H Arenas, G Boato, DT Dang-Nguyen, ... Experimental IR Meets Multilinguality, Multimodality, and Interaction: 8th …, 2017 | 72 | 2017 |
| Detecting large vessel occlusion at multiphase CT angiography by using a deep convolutional neural network MT Stib, J Vasquez, MP Dong, YH Kim, SS Subzwari, HJ Triedman, ... Radiology 297 (3), 640-649, 2020 | 69 | 2020 |
| Neural document embeddings for intensive care patient mortality prediction P Grnarova, F Schmidt, SL Hyland, C Eickhoff arXiv preprint arXiv:1612.00467, 2016 | 69 | 2016 |
| Overview of the ImageCLEF 2018 caption prediction tasks A García Seco de Herrera, C Eickhof, V Andrearczyk, H Müller Working Notes of CLEF 2018-Conference and Labs of the Evaluation Forum (CLEF …, 2018 | 68 | 2018 |
Arjen P. de VriesRadboud UniversiteitVerified email at cs.ru.nl
