| Learning from multiple annotators with Gaussian processes P Groot, A Birlutiu, T Heskes International Conference on Artificial Neural Networks, 159-164, 2011 | 52 | 2011 |
| Standard-free calibration transfer-An evaluation of different techniques B Malli, A Birlutiu, T Natschläger Chemometrics and Intelligent Laboratory Systems 161, 49-60, 2017 | 49 | 2017 |
| Multi-task preference learning with an application to hearing aid personalization A Birlutiu, P Groot, T Heskes Neurocomputing 73 (7-9), 1177-1185, 2010 | 45 | 2010 |
| Transcriptional plasticity of a soil arthropod across different ecological conditions TE De Boer, A Birlutiu, Z Bochdanovits, MJTN Timmermans, TMH Dijkstra, ... Molecular Ecology 20 (6), 1144-1154, 2011 | 30 | 2011 |
| Efficiently learning the preferences of people A Birlutiu, P Groot, T Heskes Machine Learning 90 (1), 1-28, 2013 | 29 | 2013 |
| Expectation propagation for rating players in sports competitions A Birlutiu, T Heskes European Conference on Principles of Data Mining and Knowledge Discovery …, 2007 | 22 | 2007 |
| Decision tree models for developing molecular classifiers for cancer diagnosis A Floares, A Birlutiu The 2012 international joint conference on neural networks (IJCNN), 1-7, 2012 | 20 | 2012 |
| Multi-domain transfer component analysis for domain generalization T Grubinger, A Birlutiu, H Schöner, T Natschläger, T Heskes Neural processing letters 46 (3), 845-855, 2017 | 19 | 2017 |
| Domain generalization based on transfer component analysis T Grubinger, A Birlutiu, H Schöner, T Natschläger, T Heskes International Work-Conference on Artificial Neural Networks, 325-334, 2015 | 17 | 2015 |
| A bayesian framework for combining protein and network topology information for predicting protein-protein interactions A Birlutiu, F d’Alché-Buc, T Heskes IEEE/ACM transactions on computational biology and bioinformatics 12 (3 …, 2014 | 16 | 2014 |
| Bayesian Monte Carlo for the Global Optimization of Expensive Functions. P Groot, A Birlutiu, T Heskes ECAI, 249-254, 2010 | 16 | 2010 |
| Defect detection in porcelain industry based on deep learning techniques A Birlutiu, A Burlacu, M Kadar, D Onita 2017 19th International Symposium on Symbolic and Numeric Algorithms for …, 2017 | 14 | 2017 |
| A kernel-based framework for intra-fractional respiratory motion estimation in radiation therapy T Geimer, M Unberath, A Birlutiu, O Taubmann, J Wölfelschneider, C Bert, ... 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 12 | 2017 |
| Multi-task preference learning with gaussian processes A Birlutiu, P Groot, T Heskes [Sl: sn], 2009 | 10 | 2009 |
| A Kernel Ridge regression model for respiratory motion estimation in radiotherapy T Geimer, A Birlutiu, M Unberath, O Taubmann, C Bert, A Maier Bildverarbeitung für die Medizin 2017, 155-160, 2017 | 9 | 2017 |
| Using topology information for protein-protein interaction prediction A Birlutiu, T Heskes IAPR international conference on pattern recognition in bioinformatics, 10-22, 2014 | 9 | 2014 |
| Automatic character recognition in porcelain ware DÁ Sanchez, SG Bulon, L Moreno, A Birlutiu, M Kadar Acta Technica Napocensis 59 (3), 8-12, 2018 | 7 | 2018 |
| From image to text in sentiment analysis via regression and deep learning D Onita, LP Dinu, A Birlutiu Proceedings of the International Conference on Recent Advances in Natural …, 2019 | 6 | 2019 |
| Quality control in porcelain industry based on computer vision techniques D Onita, N Vartan, M Kadar, A Birlutiu 2018 International Young Engineers Forum (YEF-ECE), 79-84, 2018 | 5 | 2018 |
| Machine learning for pairwise data: applications for preference learning and supervised network inference A Birlutiu [Sl: sn], 2011 | 4 | 2011 |
Tom HeskesProfessor of Computer Science and Artificial Intelligence, Radboud University NijmegenVerified email at science.ru.nl
