32 citations · 49 across the 36 of their papers we have counts for
7 papers · 1 filter
Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learning
Reda Khoufache, Mustapha Lebbah, Hanene Azzag +2
Dirichlet Process Mixture Models (DPMMs) are widely used to address clustering problems. Their main advantage lies in their ability to automatically estimate the number of clusters…
Parallel Computation of Multi-Slice Clustering of Third-Order Tensors
Dina Faneva Andriantsiory, Camille Coti, Joseph Ben Geloun +1
Machine Learning approaches like clustering methods deal with massive datasets that present an increasing challenge. We devise parallel algorithms to compute the Multi-Slice Cluste…
Self-Reinforcement Attention Mechanism For Tabular Learning
Kodjo Mawuena Amekoe, Mohamed Djallel Dilmi, Hanene Azzag +3
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…
DBSCAN of Multi-Slice Clustering for Third-Order Tensors
Dina Faneva Andriantsiory, Joseph Ben Geloun, Mustapha Lebbah
Several methods for triclustering three-dimensional data require the cluster size or the number of clusters in each dimension to be specified. To address this issue, the Multi-Slic…
Epigenetics Algorithms: Self-Reinforcement-Attention mechanism to regulate chromosomes expression
Mohamed Djallel Dilmi, Hanene Azzag, Mustapha Lebbah
Genetic algorithms are a well-known example of bio-inspired heuristic methods. They mimic natural selection by modeling several operators such as mutation, crossover, and selection…
Multiway clustering of 3-order tensor via affinity matrix
Dina Faneva Andriantsiory, Joseph Ben Geloun, Mustapha Lebbah
We propose a new method of multiway clustering for 3-order tensors via affinity matrix (MCAM). Based on a notion of similarity between the tensor slices and the spread of informati…
Context Normalization Layer with Applications
Bilal Faye, Mohamed-Djallel Dilmi, Hanane Azzag +2
Normalization is a pre-processing step that converts the data into a more usable representation. As part of the deep neural networks (DNNs), the batch normalization (BN) technique…