| Explainable artificial intelligence (xai) on timeseries data: A survey T Rojat, R Puget, D Filliat, J Del Ser, R Gelin, N Díaz-Rodríguez arXiv preprint arXiv:2104.00950, 2021 | 180 | 2021 |
| Explainable artificial intelligence (xai) on timeseries data: A survey. arXiv 2021 T Rojat, R Puget, D Filliat, J Del Ser, R Gelin, N Díaz-Rodríguez arXiv preprint arXiv:2104.00950, 0 | 15 | |
| Hierarchical label partitioning for large scale classification R Puget, N Baskiotis 2015 IEEE International Conference on Data Science and Advanced Analytics …, 2015 | 5 | 2015 |
| Time series prediction using disentangled latent factors P Cribier-Delande, R Puget, V Guigue, L Denoyer ESANN 2020-28th European Symposium on Artificial Neural Networks …, 2020 | 2 | 2020 |
| Time series prediction & generation from disentangled latent factors: new opportunities for smart cities P Cribier-Delande, R Puget, C Noûs, V Guigue, L Denoyer 2020 IEEE 23rd International Conference on Intelligent Transportation …, 2020 | 1 | 2020 |
| Sequential Dynamic Classification for Large Scale Multiclass Problems R Puget, N Baskiotis, P Gallinari Extreme Classification Workshop at ICML, 2015 | 1 | 2015 |
| Scalable Learnability Measure for Hierarchical Learning in Large Scale Multi-Class Classification R Puget, N Baskiotis, P Gallinari WSDM Workshop Web-Scale Classification: Classifying Big Data from the Web, 2014 | 1 | 2014 |
| Attention mechanism on disentangled contextual factor representations for time series generation P Cribier-Delande, R Puget, V Guigue, L Denoyer | | 2020 |
| Étude de la classification dans un très grand nombre de catégories R Puget Université Pierre et Marie Curie-Paris VI, 2016 | | 2016 |
| Time Series Prediction from Multiple Factors P Cribier-Delande, R Puget, V Guigue, L Denoyer | | |
