S. Malinowski

发表

Error Recovery Properties and Soft Decoding of Quasi-Arithmetic Codes

Hervé Jégou, Christine Guillemot, Simon Malinowski, 2008, EURASIP J. Adv. Signal Process..

Trellis state aggregation for soft decoding of variable length codes

H. Jégou, C. Guillemot, S. Malinowski, 2005, IEEE Workshop on Signal Processing Systems Design and Implementation, 2005..

Clustering flood events from water quality time series using Latent Dirichlet Allocation model

Jean-Marc Odobez, Rémi Emonet, Romain Tavenard, 2013 .

1d-SAX: A Novel Symbolic Representation for Time Series

Romain Tavenard, Thomas Guyet, Rene Quiniou, 2013, IDA.

On the performance of arithmetic and quasi arithmetic codes for JSCC

Enrico Magli, Gabriella Olmo, Marco Grangetto, 2009 .

Software - Software of the past four years

Christine Guillemot, Teddy Furon, Gagan Rath, 2008 .

Graph-based image gradients aggregated with random forests

Zenilton K. G. Patrocínio, Ewa Kijak, S. Malinowski, 2022, Pattern Recognit. Lett..

On the synchronisation recovery and soft decoding of Variable Length Codes

Hervé Jégou, Christine Guillemot, Simon Malinowski, 2006 .

Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers

Simon Malinowski, Romain Tavenard, Thomas Guyet, 2020, AALTD@PKDD/ECML.

Day-ahead time series forecasting: application to capacity planning pdf

Vincent Lemaire, Thomas Guyet, Laurence Rozé, 2018, ArXiv.

Cost-Aware Early Classification of Time Series

Romain Tavenard, Simon Malinowski, R. Tavenard, 2016, ECML/PKDD.

Data Augmentation for Time Series Classification using Convolutional Neural Networks

Romain Tavenard, Simon Malinowski, Arthur Le Guennec, 2016 .

Fault diagnosis in DSL networks using support vector machines

Hyong S. Kim, Ricardo Morla, Angelos K. Marnerides, 2015, Comput. Commun..

On the comprehension of DSL SyncTrap events in IPTV networks

Miguel R. D. Rodrigues, Hyong S. Kim, Ricardo Morla, 2013, 2013 IEEE Symposium on Computers and Communications (ISCC).

Direct Remaining Useful Life Estimation Based on Support Vector Regression

Brigitte Chebel-Morello, Noureddine Zerhouni, Farhat Fnaiech, 2017, IEEE Transactions on Industrial Electronics.

Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

Brigitte Chebel-Morello, Simon Malinowski, Farhat Fnaiech, 2015 .

On time series classification with dictionary-based classifiers

James Large, Anthony J. Bagnall, Romain Tavenard, 2019, Intell. Data Anal..

From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers pdf

James Large, Anthony J. Bagnall, Romain Tavenard, 2018, ArXiv.

Toward a Framework for Seasonal Time Series Forecasting Using Clustering

Alexandre Termier, Simon Malinowski, Thomas Guyet, 2019, IDEAL.

Feature selection for fault detection systems: application to the Tennessee Eastman process

Brigitte Chebel-Morello, Noureddine Zerhouni, Mouloud Denai, 2011, 2011 IEEE International Conference on Automation Science and Engineering.

Evaluation of Bag-of-Word Performance for Time Series Classification Using Discriminative SIFT-Based Mid-Level Representations

Raquel Almeida, Silvio Jamil Ferzoli Guimarães, Zenilton Kleber Gonçalves do Patrocínio, 2018, CIARP.

Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization pdf

Romain Tavenard, Elisa Fromont, Simon Malinowski, 2019, ArXiv.

Efficient Temporal Kernels Between Feature Sets for Time Series Classification

Benjamin Bustos, Romain Tavenard, Heider Sanchez, 2017, ECML/PKDD.

Dense Bag-of-Temporal-SIFT-Words for Time Series Classification

Simon Malinowski, Romain Tavenard, Thomas Guyet, 2016, ArXiv.

Augmentation for Time Series Classification using Convolutional Neural Networks

Arthur Le Guennec, R. Tavenard, S. Malinowski, 2016 .

Bag-of-Temporal-SIFT-Words for Time Series Classification

Romain Tavenard, Thomas Guyet, Simon Malinowski, 2015, AALTD@PKDD/ECML.

Learning DTW-Preserving Shapelets

Laurent Amsaleg, Romain Tavenard, Simon Malinowski, 2017, IDA.

Temporal Disaggregation of the Cumulative Grass Growth

T. Guyet, Anne-Isabelle Graux, S. Malinowski, 2022, International Conferences on Pattern Recognition and Artificial Intelligence.

RUL prediction based on a new similarity-instance based approach

Brigitte Chebel-Morello, Noureddine Zerhouni, Simon Malinowski, 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

Impact of Data Cleansing for Urban Bus Commercial Speed Prediction

J. Jézéquel, S. Malinowski, Gauthier Lyan, 2021, SN Computer Science.

Event and Anomaly Detection Using Tucker3 Decomposition pdf

Hadi Fanaee-T, João Gama, Simon Malinowski, 2014, ArXiv.

Combining convolutional side-outputs for road image segmentation

Simon Malinowski, Ewa Kijak, Silvio Jamil Ferzoli Guimarães, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

BRIEF-Based Mid-Level Representations for Time Series Classification

Simon Malinowski, Silvio Jamil Ferzoli Guimarães, Zenilton Kleber Gonçalves do Patrocínio, 2019, CIARP.

Localized Random Shapelets

Simon Malinowski, Romain Tavenard, Xavier Renard, 2019, AALTD@PKDD/ECML.