32 citations · 43 across the 20 of their papers we have counts for
20 papers
Coalition Free Energy and Adaptive Precision in Multi-Agent Cooperation
Djamel Bouchaffra, Faycal Ykhlef, Mustapha Lebbah +1
Cooperative multi-agent systems require robust mechanisms for credit assignment under uncertainty. Here we introduce a variational framework, termed the Game-Theoretic Free Energy…
Adaptive Head Budgeting for Efficient Multi-Head Attention
Bilal Faye, Abdoulaye Mbaye, Hanane Azzag +1
Multi-head attention enables Transformers to capture diverse representations, but all attention heads are typically activated for every input, regardless of task complexity. For co…
NeuroGame Transformer: Gibbs-Inspired Attention Driven by Game Theory and Statistical Physics
Djamel Bouchaffra, Faycal Ykhlef, Hanene Azzag +2
Standard attention mechanisms in transformers are limited by their pairwise formulation, which hinders the modeling of higher-order dependencies among tokens. We introduce the Neur…
Value-Free Policy Optimization via Reward Partitioning
Bilal Faye, Hanane Azzag, Mustapha Lebbah
Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by di…
Manual Verbalizer Enrichment for Few-Shot Text Classification
Quang Anh Nguyen, Nadi Tomeh, Mustapha Lebbah +3
With the continuous development of pre-trained language models, prompt-based training becomes a well-adopted paradigm that drastically improves the exploitation of models for many…
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection
Kodjo Mawuena Amekoe, Mustapha Lebbah, Gregoire Jaffre +2
Real-world tabular learning production scenarios typically involve evolving data streams, where data arrives continuously and its distribution may change over time. In such a setti…
Adaptative Context Normalization: A Boost for Deep Learning in Image Processing
Bilal Faye, Hanane Azzag, Mustapha Lebbah +1
Deep Neural network learning for image processing faces major challenges related to changes in distribution across layers, which disrupt model convergence and performance. Activati…
Unsupervised Adaptive Normalization
Bilal Faye, Hanane Azzag, Mustapha Lebbah +1
Deep neural networks have become a staple in solving intricate problems, proving their mettle in a wide array of applications. However, their training process is often hampered by…
Supervised Batch Normalization
Bilal Faye, Mustapha Lebbah, Hanane Azzag
Batch Normalization (BN), a widely-used technique in neural networks, enhances generalization and expedites training by normalizing each mini-batch to the same mean and variance. H…
Lightweight Cross-Modal Representation Learning
Bilal Faye, Hanane Azzag, Mustapha Lebbah +1
Low-cost cross-modal representation learning is crucial for deriving semantic representations across diverse modalities such as text, audio, images, and video. Traditional approach…
Distributed MCMC inference for Bayesian Non-Parametric Latent Block Model
Reda Khoufache, Anisse Belhadj, Hanene Azzag +1
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…
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…
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…
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…
Transformer-based conditional generative adversarial network for multivariate time series generation
Abdellah Madane, Mohamed-djallel Dilmi, Florent Forest +3
Conditional generation of time-dependent data is a task that has much interest, whether for data augmentation, scenario simulation, completing missing data, or other purposes. Rece…
Improved Multi-objective Data Stream Clustering with Time and Memory Optimization
Mohammed Oualid Attaoui, Hanene Azzag, Mustapha Lebbah +1
The analysis of data streams has received considerable attention over the past few decades due to sensors, social media, etc. It aims to recognize patterns in an unordered, infinit…
Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images
Pierre Le Jeune, Mustapha Lebbah, Anissa Mokraoui +1
This paper proposes a few-shot method based on Faster R-CNN and representation learning for object detection in aerial images. The two classification branches of Faster R-CNN are r…
Algorithms for an Efficient Tensor Biclustering
Andriantsiory Dina Faneva, Mustapha Lebbah, Hanane Azzag +1
Consider a data set collected by (individuals-features) pairs in different times. It can be represented as a tensor of three dimensions (Individuals, features and times). The tenso…
Nearest Neighbor Median Shift Clustering for Binary Data
Gaël Beck, Tarn Duong, Mustapha Lebbah +1
We describe in this paper the theory and practice behind a new modal clustering method for binary data. Our approach (BinNNMS) is based on the nearest neighbor median shift. The me…
A Distributed and Approximated Nearest Neighbors Algorithm for an Efficient Large Scale Mean Shift Clustering
Gaël Beck, Tarn Duong, Mustapha Lebbah +2
In this paper we target the class of modal clustering methods where clusters are defined in terms of the local modes of the probability density function which generates the data. T…