| POT: Python Optimal Transport R Flamary, N Courty, A Gramfort, MZ Alaya, A Boisbunon, S Chambon, ... Journal of Machine Learning Research 22 (78), 1-8, 2021 | 455 | 2021 |
| 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 | 391 | 2016 |
| Tslearn, A Machine Learning Toolkit for Time Series Data R Tavenard, J Faouzi, G Vandewiele, F Divo, G Androz, C Holtz, M Payne, ... Journal of Machine Learning Research 21 (118), 1-6, 2020 | 362* | 2020 |
| Searching in one billion vectors: re-rank with source coding H Jégou, R Tavenard, M Douze, L Amsaleg 2011 IEEE International Conference on Acoustics, Speech and Signal …, 2011 | 301 | 2011 |
| Optimal Transport for structured data with application on graphs T Vayer, L Chapel, R Flamary, R Tavenard, N Courty International Conference on Machine Learning 97, 2019 | 195* | 2019 |
| 1d-SAX: a Novel Symbolic Representation for Time Series S Malinowski, T Guyet, R Quiniou, R Tavenard International Symposium on Intelligent Data Analysis, 2013 | 106 | 2013 |
| Dynamic Time Warping under limited warping path length Z Zhang, R Tavenard, A Bailly, X Tang, P Tang, T Corpetti Information Sciences 393, 91-107, 2017 | 85 | 2017 |
| Sliced Gromov-Wasserstein V Titouan, R Flamary, N Courty, R Tavenard, L Chapel Advances in Neural Information Processing Systems, 14726-14736, 2019 | 82* | 2019 |
| Sensor networks for ambient intelligence EJ Pauwels, AA Salah, R Tavenard Multimedia Signal Processing, 2007. MMSP 2007. IEEE 9th Workshop on, 13-16, 2007 | 73 | 2007 |
| Fused Gromov-Wasserstein Distance for Structured Objects T Vayer, L Chapel, R Flamary, R Tavenard, N Courty Algorithms 13 (9), 212, 2020 | 56* | 2020 |
| On time series classification with dictionary-based classifiers J Large, A Bagnall, S Malinowski, R Tavenard Intelligent Data Analysis 23 (5), 1073-1089, 2019 | 48 | 2019 |
| Identifying seasonal patterns of phosphorus storm dynamics with dynamic time warping R Dupas, R Tavenard, O Fovet, N Gilliet, C Grimaldi, C Gascuel‐Odoux Water Resources Research 51 (11), 8868-8882, 2015 | 41 | 2015 |
| End-to-end learned early classification of time series for in-season crop type mapping M Rußwurm, N Courty, R Emonet, S Lefèvre, D Tuia, R Tavenard ISPRS Journal of Photogrammetry and Remote Sensing 196, 445-456, 2023 | 37* | 2023 |
| Dense Bag-of-Temporal-SIFT-Words for Time Series Classification A Bailly, S Malinowski, R Tavenard, L Chapel, T Guyet International Workshop on Advanced Analytics and Learning on Temporal Data …, 2015 | 37 | 2015 |
| Cost-Aware Early Classification of Time Series R Tavenard, S Malinowski European Conference on Machine Learning and Principles and Practice of …, 2016 | 36 | 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 | 30 | 2013 |
| Balancing clusters to reduce response time variability in large scale image search R Tavenard, H Jégou, L Amsaleg International Workshop on Content-Based Multimedia Indexing (CBMI 2011), 2011 | 29 | 2011 |
| T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data AA Salah, E Pauwels, R Tavenard, T Gevers Sensors 10 (8), 7496-7513, 2010 | 27 | 2010 |
| 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 |
| Learning DTW-Preserving Shapelets A Lods, S Malinowski, R Tavenard, L Amsaleg International Symposium on Intelligent Data Analysis, 198-209, 2017 | 23 | 2017 |
Simon MalinowskiAssociate Professor, ISTIC, Univ. RennesVerified email at irisa.fr
