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Yohan Petetin
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2020 – today
- 2024
- [c18]Idyano Leroy, Augustin A. Saucan, Yohan Petetin, Daniel Clark:
An Analysis of the Mutual Information Upper Bound for Sensor-Subset Selection. FUSION 2024: 1-8 - 2023
- [j12]Hugo Gangloff, Katherine Morales, Yohan Petetin:
Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data. Comput. Stat. Data Anal. 180: 107663 (2023) - [j11]François Desbouvries, Yohan Petetin, Achille Salaün:
Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks From a System Theoretic Viewpoint. IEEE Trans. Signal Process. 71: 4178-4191 (2023) - [c17]Katherine Morales, Yohan Petetin:
A Probabilistic Semi-Supervised Approach with Triplet Markov Chains. MLSP 2023: 1-6 - [i3]Katherine Morales, Yohan Petetin:
A Probabilistic Semi-Supervised Approach with Triplet Markov Chains. CoRR abs/2309.03707 (2023) - 2022
- [i2]François Desbouvries, Yohan Petetin, Achille Salaün:
Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks from a system theoretic viewpoint. CoRR abs/2208.08175 (2022) - 2021
- [j10]Yohan Petetin, Yazid Janati, François Desbouvries:
Structured Variational Bayesian Inference for Gaussian State-Space Models With Regime Switching. IEEE Signal Process. Lett. 28: 1953-1957 (2021) - [c16]Hugo Gangloff, Katherine Morales, Yohan Petetin:
A General Parametrization Framework for Pairwise Markov Models: An Application to Unsupervised Image Segmentation. MLSP 2021: 1-6 - [c15]Katherine Morales, Yohan Petetin:
Variational Bayesian Inference for Pairwise Markov Models. SSP 2021: 251-255 - 2020
- [i1]Nicolas Aussel, Sophie Chabridon, Yohan Petetin:
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics. CoRR abs/2001.07504 (2020)
2010 – 2019
- 2019
- [c14]Achille Salaün, Yohan Petetin, François Desbouvries:
Comparing the Modeling Powers of RNN and HMM. ICMLA 2019: 1496-1499 - 2018
- [j9]Roland Lamberti, Yohan Petetin, François Desbouvries, François Septier:
Semi-Independent Resampling for Particle Filtering. IEEE Signal Process. Lett. 25(1): 130-134 (2018) - [c13]Nicolas Aussel, Yohan Petetin, Sophie Chabridon:
Improving Performances of Log Mining for Anomaly Prediction Through NLP-Based Log Parsing. MASCOTS 2018: 237-243 - [c12]Roland Lamberti, Yohan Petetin, François Septier, François Desbouvries:
A Double Proposal Normalized Importance Sampling Estimator. SSP 2018: 238-242 - 2017
- [j8]Roland Lamberti, Yohan Petetin, François Desbouvries, François Septier:
Independent Resampling Sequential Monte Carlo Algorithms. IEEE Trans. Signal Process. 65(20): 5318-5333 (2017) - [c11]Nicolas Aussel, Samuel Jaulin, Guillaume Gandon, Yohan Petetin, Eriza Fazli, Sophie Chabridon:
Predictive Models of Hard Drive Failures Based on Operational Data. ICMLA 2017: 619-625 - 2016
- [c10]Roland Lamberti, Yohan Petetin, François Septier, François Desbouvries:
Particle filters with independent resampling. ICASSP 2016: 3994-3998 - [c9]Roland Lamberti, Yohan Petetin, François Septier, François Desbouvries:
An improved SIR-based Sequential Monte Carlo algorithm. SSP 2016: 1-5 - 2015
- [j7]Yohan Petetin, François Desbouvries:
Bayesian Conditional Monte Carlo Algorithms for Nonlinear Time-Series State Estimation. IEEE Trans. Signal Process. 63(14): 3586-3598 (2015) - [c8]Yohan Petetin, Cyrille Laroche, Aurélien Mayoue:
Deep neural networks for audio scene recognition. EUSIPCO 2015: 125-129 - 2014
- [j6]Yohan Petetin, Mark R. Morelande, François Desbouvries:
Marginalized particle PHD filters for multiple object Bayesian filtering. IEEE Trans. Aerosp. Electron. Syst. 50(2): 1182-1196 (2014) - [j5]Noufel Abbassi, Stéphane Derrode, François Desbouvries, Yohan Petetin, Wojciech Pieczynski:
Filtrage statistique optimal rapide dans des systèmes linéaires à sauts non stationnaires. Traitement du Signal 31(3-4): 339-361 (2014) - [j4]Yohan Petetin, François Desbouvries:
A Class of Fast Exact Bayesian Filters in Dynamical Models With Jumps. IEEE Trans. Signal Process. 62(14): 3643-3653 (2014) - [c7]Yohan Petetin, François Desbouvries:
Exact Bayesian estimation in constrained Triplet Markov Chains. MLSP 2014: 1-6 - 2013
- [j3]Yohan Petetin, François Desbouvries:
Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis. Stat. Comput. 23(6): 759-775 (2013) - [j2]Yohan Petetin, François Desbouvries:
Bayesian Multi-Object Filtering for Pairwise Markov Chains. IEEE Trans. Signal Process. 61(18): 4481-4490 (2013) - 2012
- [c6]Yohan Petetin, François Desbouvries:
Multi-object filtering for pairwise Markov chains. ISSPA 2012: 348-353 - [c5]Yohan Petetin, François Desbouvries:
Marginalized PHD Filters for multi-target filtering. ISSPA 2012: 419-424 - [c4]Yohan Petetin, François Desbouvries:
A mixed GM/SMC implementation of the probability hypothesis density filter. ISSPA 2012: 425-430 - [c3]Yohan Petetin, François Desbouvries:
A semi-exact sequential Monte Carlo filtering algorithm in Hidden Markov Chains. ISSPA 2012: 595-600 - [c2]Yohan Petetin, François Desbouvries:
Further Rao-Blackwellizing an already Rao-Blackwellized algorithm for Jump Markov State Space Systems. ISSPA 2012: 706-711 - 2011
- [j1]François Desbouvries, Yohan Petetin, Boujemaa Ait-El-Fquih:
Direct, prediction- and smoothing-based Kalman and particle filter algorithms. Signal Process. 91(8): 2064-2077 (2011) - [c1]François Desbouvries, Yohan Petetin, Emmanuel Monfrini:
Optimal SIR algorithm vs. fully adapted auxiliary particle filter: A matter of conditional independence. ICASSP 2011: 3992-3995
Coauthor Index
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