Simon Malinowski
Simon Malinowski
Associate Professor, ISTIC, Univ. Rennes
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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
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
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
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
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
On time series classification with dictionary-based classifiers
J Large, A Bagnall, S Malinowski, R Tavenard
Intelligent Data Analysis 23 (5), 1073-1089, 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
Remaining useful life estimation based on discriminating shapelet extraction
S Malinowski, B Chebel-Morello, N Zerhouni
Reliability engineering & system safety 142, 279-288, 2015
Cost-aware early classification of time series
R Tavenard, S Malinowski
Machine Learning and Knowledge Discovery in Databases: European Conference …, 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
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
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
Learning DTW-preserving shapelets
A Lods, S Malinowski, R Tavenard, L Amsaleg
Advances in Intelligent Data Analysis XVI: 16th International Symposium, IDA …, 2017
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
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
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
Event and anomaly detection using tucker3 decomposition
HF Tork, R Morla, MB Oliveira, J Gama
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
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
Fault diagnosis in DSL networks using support vector machines
AK Marnerides, S Malinowski, R Morla, HS Kim
Computer Communications 62, 72-84, 2015
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