1 citations · 1 across the 11 of their papers we have counts for
11 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 Game Theoretic Free Energy Analysis of Higher Order Synergy in Attention Heads of Large Language Models
Djamel Bouchaffra
Large language models rely on multihead attention, but interactions among heads remain poorly understood. We apply the Game Theoretic Free Energy Principle (GTFEP): a framework cas…
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.…
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…
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…
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…
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 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…
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…