1 code implementation • 1 Feb 2024 • Reda Khoufache, Anisse Belhadj, Hanene Azzag, Mustapha Lebbah
In this paper, we introduce a novel Distributed Markov Chain Monte Carlo (MCMC) inference method for the Bayesian Non-Parametric Latent Block Model (DisNPLBM), employing the Master/Worker architecture.
1 code implementation • 18 Dec 2023 • Reda Khoufache, Mustapha Lebbah, Hanene Azzag, Etienne Goffinet, Djamel Bouchaffra
Dirichlet Process Mixture Models (DPMMs) are widely used to address clustering problems.
no code implementations • 19 May 2023 • Kodjo Mawuena Amekoe, Mohamed Djallel Dilmi, Hanene Azzag, Mustapha Lebbah, Zaineb Chelly Dagdia, Gregoire Jaffre
Apart from the high accuracy of machine learning models, what interests many researchers in real-life problems (e. g., fraud detection, credit scoring) is to find hidden patterns in data; particularly when dealing with their challenging imbalanced characteristics.
no code implementations • 15 Mar 2023 • Mohamed Djallel Dilmi, Hanene Azzag, Mustapha Lebbah
Genetic algorithms are a well-known example of bio-inspired heuristic methods.
no code implementations • 13 Jan 2022 • Mohammed Oualid Attaoui, Hanene Azzag, Mustapha Lebbah, Nabil Keskes
The experiments show the ability of our method to partition the data stream in arbitrarily shaped, compact, and well-separated clusters while optimizing the time and memory.
1 code implementation • 27 Sep 2021 • Pierre Le Jeune, Mustapha Lebbah, Anissa Mokraoui, Hanene Azzag
This training strategy encourages the network to adapt to new classes as it would at test time.
1 code implementation • ESANN 2019 2019 • Florent Forest, Mustapha Lebbah, Hanene Azzag, Jérôme Lacaille
In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map.