32 citations · 49 across the 36 of their papers we have counts for
36 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…
A Collective Variational Principle Unifying Bayesian Inference, Game Theory, and Thermodynamics
Djamel Bouchaffra, Faycal Ykhlef, Mustapha Lebbah +1
Collective intelligence emerges across biological, physical, and artificial systems without central coordination, yet a unifying principle governing such behaviour remains elusive.…
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…
Energy-Regularized Spatial Masking: A Novel Approach to Enhancing Robustness and Interpretability in Vision Models
Tom Devynck, Bilal Faye, Djamel Bouchaffra +3
Deep convolutional neural networks achieve remarkable performance by exhaustively processing dense spatial feature maps, yet this brute-force strategy introduces significant comput…
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…
Prototype-Guided Diffusion: Visual Conditioning without External Memory
Bilal Faye, Hanane Azzag, Mustapha Lebbah
Diffusion models achieve state-of-the-art image generation but remain computationally costly due to iterative denoising. Latent-space models like Stable Diffusion reduce overhead y…
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…
MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote Sensing
Karim Radouane, Hanane Azzag, Mustapha lebbah
We propose a unified framework that integrates object detection (OD) and visual grounding (VG) for remote sensing (RS) imagery. To support conventional OD and establish an intuitiv…
Game Theory Meets Statistical Mechanics in Deep Learning Design
Djamel Bouchaffra, Fayçal Ykhlef, Bilal Faye +2
We present a novel deep graphical representation that seamlessly merges principles of game theory with laws of statistical mechanics. It performs feature extraction, dimensionality…
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…
OneEncoder: A Lightweight Framework for Progressive Alignment of Modalities
Bilal Faye, Hanane Azzag, Mustapha Lebbah
Cross-modal alignment Learning integrates information from different modalities like text, image, audio and video to create unified models. This approach develops shared representa…
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…
Lightweight Modular Parameter-Efficient Tuning for Open-Vocabulary Object Detection
Bilal Faye, Hanane Azzag, Mustapha Lebbah
Open-vocabulary object detection (OVD) extends recognition beyond fixed taxonomies by aligning visual and textual features, as in MDETR, GLIP, or RegionCLIP. While effective, these…
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…
Enhancing Neural Network Representations with Prior Knowledge-Based Normalization
Bilal Faye, Hanane Azzag, Mustapha Lebbah +1
Deep learning models face persistent challenges in training, particularly due to internal covariate shift and label shift. While single-mode normalization methods like Batch Normal…
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…
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…