| 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 | 2518 | 2013 |
| 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 | 53 | 2020 |
| L1-based compression of random forest models A Joly, F Schnitzler, P Geurts, L Wehenkel European Symposium on Artificial Neural Networks, Computational Intelligence …, 2012 | 34 | 2012 |
| 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, 1-12, 2017 | 30 | 2017 |
| Random forests with random projections of the output space for high dimensional multi-label classification A Joly, P Geurts, L Wehenkel Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 30 | 2014 |
| 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 | 30 | 2013 |
| Camp: a two-stage approach to modelling prosody in context Z Hodari, A Moinet, S Karlapati, J Lorenzo-Trueba, T Merritt, A Joly, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 23 | 2021 |
| Scikit-learn: Machine learning in Python L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... Journal of Machine Learning Research 12 (85), 2825-2830, 2011 | 18 | 2011 |
| Globally induced forest: A prepruning compression scheme JM Begon, A Joly, P Geurts International conference on machine learning, 420-428, 2017 | 17 | 2017 |
| 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 | 17 | 2015 |
| Prosodic representation learning and contextual sampling for neural text-to-speech S Karlapati, A Abbas, Z Hodari, A Moinet, A Joly, P Karanasou, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 16 | 2021 |
| 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 | 9 | 2017 |
| A learned conditional prior for the VAE acoustic space of a TTS system P Karanasou, S Karlapati, A Moinet, A Joly, A Abbas, S Slangen, ... arXiv preprint arXiv:2106.10229, 2021 | 8 | 2021 |
| scikit-learn/scikit-learn: Scikit-learn 0.22. 1 O Grisel, A Mueller, A Gramfort, G Louppe, P Prettenhofer, M Blondel, ... Zenodo, 2020 | 8 | 2020 |
| Exploiting random projections and sparsity with random forests and gradient boosting methods A Joly arXiv preprint arXiv:1704.08067, 2016 | 6 | 2016 |
| 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 | 5 | 2019 |
| Distribution augmentation for low-resource expressive text-to-speech M Lajszczak, A Prasad, A Van Korlaar, B Bollepalli, A Bonafonte, A Joly, ... ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 3 | 2022 |
| Simple and effective multi-sentence TTS with expressive and coherent prosody P Makarov, A Abbas, M Łajszczak, A Joly, S Karlapati, A Moinet, ... arXiv preprint arXiv:2206.14643, 2022 | 2 | 2022 |
| Joint learning and pruning of decision forests JM Begon, A Joly, P Geurts The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), 2016 | 1 | 2016 |
| Scikit-Learn: Machine Learning in the Python ecosystem A Joly, G Louppe GIGA DAY 2014, 2014 | 1 | 2014 |
Robert LaytonFederation University AustraliaEmail verificata su datapipeline.com.au
