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Collaborations and top research areas from the last five years
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An Analysis of the Mutual Information Upper Bound for Sensor-Subset Selection
Leroy, I., Saucan, A. A., Petetin, Y. & Clark, D., 1 Jan 2024, FUSION 2024 - 27th International Conference on Information Fusion. Institute of Electrical and Electronics Engineers Inc., (FUSION 2024 - 27th International Conference on Information Fusion).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Variance estimation for sequential Monte Carlo algorithms: A backward sampling approach
El Idrissi, Y. J., Corff, S. L. & Petetin, Y., 1 Feb 2024, In: Bernoulli. 30, 2, p. 911-935 25 p.Research output: Contribution to journal › Article › peer-review
Open Access -
A Probabilistic Semi-Supervised Approach with Triplet Markov Chains
Morales, K. & Petetin, Y., 1 Jan 2023, Proceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing, MLSP 2023. Comminiello, D. & Scarpiniti, M. (eds.). IEEE Computer Society, (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; vol. 2023-September).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data
Gangloff, H., Morales, K. & Petetin, Y., 1 Apr 2023, In: Computational Statistics and Data Analysis. 180, 107663.Research output: Contribution to journal › Article › peer-review
Open Access -
Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks From a System Theoretic Viewpoint
Desbouvries, F., Petetin, Y. & Salaun, A., 1 Jan 2023, In: IEEE Transactions on Signal Processing. 71, p. 4178-4191 14 p.Research output: Contribution to journal › Article › peer-review
Open Access -
A General Parametrization Framework for Pairwise Markov Models: An Application to Unsupervised Image Segmentation
Gangloff, H., Morales, K. & Petetin, Y., 1 Jan 2021, 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021. IEEE Computer Society, (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; vol. 2021-October).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Structured Variational Bayesian Inference for Gaussian State-Space Models with Regime Switching
Petetin, Y., Janati, Y. & Desbouvries, F., 1 Jan 2021, In: IEEE Signal Processing Letters. 28, p. 1953-1957 5 p.Research output: Contribution to journal › Article › peer-review
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Variational Bayesian Inference for Pairwise Markov Models
Morales, K. & Petetin, Y., 11 Jul 2021, 2021 IEEE Statistical Signal Processing Workshop, SSP 2021. IEEE Computer Society, p. 251-255 5 p. 9513755. (IEEE Workshop on Statistical Signal Processing Proceedings; vol. 2021-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Comparing the modeling powers of RNN and HMM
Salaun, A., Petetin, Y. & Desbouvries, F., 1 Dec 2019, Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019. Wani, M. A., Khoshgoftaar, T. M., Wang, D., Wang, H. & Seliya, N. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 1496-1499 4 p. 8999058. (Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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A Double Proposal Normalized Importance Sampling Estimator
Lamberti, R., Petetin, Y., Septier, F. & Desbouvries, F., 29 Aug 2018, 2018 IEEE Statistical Signal Processing Workshop, SSP 2018. Institute of Electrical and Electronics Engineers Inc., p. 253-257 5 p. 8450849. (2018 IEEE Statistical Signal Processing Workshop, SSP 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review