| API design for machine learning software: experiences from the scikit-learn project L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... arXiv preprint arXiv:1309.0238, 2013 | 1181 | 2013 |
| L1-based compression of random forest models A Joly, F Schnitzler, P Geurts, L Wehenkel 20th European symposium on artificial neural networks, 2012 | 27 | 2012 |
| Random forests with random projections of the output space for high dimensional multi-label classification A Joly, P Geurts, L Wehenkel Joint European conference on machine learning and knowledge discovery in …, 2014 | 26 | 2014 |
| Sepsis prediction in critically ill patients by platelet activation markers on ICU admission: a prospective pilot study N Layios, C Delierneux, A Hego, J Huart, C Gosset, C Lecut, N Maes, ... Intensive care medicine experimental 5 (1), 1-12, 2017 | 18 | 2017 |
| API design for machine learning software: experiences from the scikit-learn project. 2013 L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... URL: https://dtai. cs. kuleuven. be/events/lml2013/papers …, 2017 | 16 | 2017 |
| ECML PKDD Workshop: Languages for Data Mining and Machine Learning L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... API Design for Machine Learning Software: Experiences from the Scikit-Learn …, 2013 | 11 | 2013 |
| Simple connectome inference from partial correlation statistics in calcium imaging A Sutera, A Joly, V François-Lavet, A Qiu, G Louppe, D Ernst, P Geurts Neural Connectomics Workshop, 23-35, 2015 | 10 | 2015 |
| CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech S Karlapati, A Moinet, A Joly, V Klimkov, D Sáez-Trigueros, T Drugman arXiv preprint arXiv:2004.14617, 2020 | 7 | 2020 |
| Exploiting random projections and sparsity with random forests and gradient boosting methods--Application to multi-label and multi-output learning, random forest model … A Joly arXiv preprint arXiv:1704.08067, 2017 | 7 | 2017 |
| Globally induced forest: A prepruning compression scheme JM Begon, A Joly, P Geurts International Conference on Machine Learning, 420-428, 2017 | 6 | 2017 |
| Exploiting random projections and sparsity with random forests and gradient boosting methods A Joly arXiv preprint arXiv:1704.08067, 2016 | 6 | 2016 |
| ArnaudJoly, Brian Holt, and Gaël Varoquaux. 2013. API design for machine learning software: experiences from the scikit-learn project L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 0 | 3 | |
| Gradient tree boosting with random output projections for multi-label classification and multi-output regression A Joly, L Wehenkel, P Geurts arXiv preprint arXiv:1905.07558, 2019 | 2 | 2019 |
| Joint learning and pruning of decision forests JM Begon, A Joly, P Geurts | 1 | 2016 |
| Scikit-Learn: Machine Learning in the Python ecosystem A Joly, G Louppe | 1 | 2014 |
| Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech S Karlapati, A Abbas, Z Hodari, A Moinet, A Joly, P Karanasou, ... arXiv preprint arXiv:2011.02252, 2020 | | 2020 |
| CAMP: a Two-Stage Approach to Modelling Prosody in Context Z Hodari, A Moinet, S Karlapati, J Lorenzo-Trueba, T Merritt, A Joly, ... arXiv preprint arXiv:2011.01175, 2020 | | 2020 |
| Prospective immune profiling in critically ill adults: before, during and after severe sepsis and septic shock N Layios Critical Care 19 (1), 1-201, 2015 | | 2015 |
| Elevated basal levels of circulating activated platelets predict ICU-acquired sepsis and mortality: a prospective study N Layios Critical Care 19 (1), 1-201, 2015 | | 2015 |
| Elevated basal levels of platelet-bound fibrinogen predict the occurrence of sepsis in ICU: a prospective study. C Delierneux, N LAYIOS, A Hego, J HUART, A Joly, P Geurts, P DAMAS, ... | | 2015 |
Robert LaytonFederation University AustraliaVerified email at datapipeline.com.au
