| Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network JB Ali, B Chebel-Morello, L Saidi, S Malinowski, F Fnaiech Mechanical Systems and Signal Processing 56, 150-172, 2015 | 570 | 2015 |
| Data augmentation for time series classification using convolutional neural networks A Le Guennec, S Malinowski, R Tavenard ECML/PKDD workshop on advanced analytics and learning on temporal data, 2016 | 567 | 2016 |
| Direct remaining useful life estimation based on support vector regression R Khelif, B Chebel-Morello, S Malinowski, E Laajili, F Fnaiech, N Zerhouni IEEE Transactions on industrial electronics 64 (3), 2276-2285, 2016 | 425 | 2016 |
| 1d-sax: A novel symbolic representation for time series S Malinowski, T Guyet, R Quiniou, R Tavenard International Symposium on Intelligent Data Analysis, 273-284, 2013 | 127 | 2013 |
| RUL prediction based on a new similarity-instance based approach R Khelif, S Malinowski, B Chebel-Morello, N Zerhouni 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE …, 2014 | 99 | 2014 |
| On time series classification with dictionary-based classifiers J Large, A Bagnall, S Malinowski, R Tavenard Intelligent Data Analysis 23 (5), 1073-1089, 2019 | 71 | 2019 |
| Overlapped quasi-arithmetic codes for distributed video coding X Artigas, S Malinowski, C Guillemot, L Torres 2007 IEEE International Conference on Image Processing 2, II-9-II-12, 2007 | 55 | 2007 |
| Remaining useful life estimation based on discriminating shapelet extraction S Malinowski, B Chebel-Morello, N Zerhouni Reliability engineering & system safety 142, 279-288, 2015 | 50 | 2015 |
| Cost-aware early classification of time series R Tavenard, S Malinowski Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016 | 44 | 2016 |
| Dense bag-of-temporal-SIFT-words for time series classification A Bailly, S Malinowski, R Tavenard, L Chapel, T Guyet Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop …, 2016 | 42 | 2016 |
| Feature selection for fault detection systems: application to the Tennessee Eastman process B Chebel-Morello, S Malinowski, H Senoussi Applied Intelligence 44 (1), 111-122, 2016 | 41 | 2016 |
| Clustering flood events from water quality time series using Latent Dirichlet Allocation model AH Aubert, R Tavenard, R Emonet, A De Lavenne, S Malinowski, T Guyet, ... Water Resources Research 49 (12), 8187-8199, 2013 | 35 | 2013 |
| Distributed coding using punctured quasi-arithmetic codes for memory and memoryless sources S Malinowski, X Artigas, C Guillemot, L Torres IEEE Transactions on Signal Processing 57 (10), 4154-4158, 2009 | 34 | 2009 |
| Learning DTW-preserving shapelets A Lods, S Malinowski, R Tavenard, L Amsaleg Advances in Intelligent Data Analysis XVI: 16th International Symposium, IDA …, 2017 | 33 | 2017 |
| Combining convolutional side-outputs for road image segmentation FAL Reis, R Almeida, E Kijak, S Malinowski, SJF Guimaraes, ... 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 27 | 2019 |
| Bag-of-temporal-sift-words for time series classification A Bailly, S Malinowski, R Tavenard, T Guyet, L Chapel ECML/PKDD workshop on advanced analytics and learning on temporal data, 2015 | 26 | 2015 |
| Event and anomaly detection using tucker3 decomposition HF Tork, R Morla, MB Oliveira, J Gama | 26* | 2012 |
| Synchronization recovery and state model reduction for soft decoding of variable length codes S Malinowski, H Jegou, C Guillemot IEEE transactions on information theory 53 (1), 368-377, 2006 | 21 | 2006 |
| Learning interpretable shapelets for time series classification through adversarial regularization Y Wang, R Emonet, E Fromont, S Malinowski, E Menager, L Mosser, ... arXiv preprint arXiv:1906.00917, 2019 | 18 | 2019 |
| Fault diagnosis in DSL networks using support vector machines AK Marnerides, S Malinowski, R Morla, HS Kim Computer Communications 62, 72-84, 2015 | 18 | 2015 |
Romain TavenardUniv. Rennes 2Verified email at univ-rennes2.fr
