32 citations · 49 across the 36 of their papers we have counts for
20 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
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…
Multi-Slice Clustering for 3-order Tensor Data
Dina Faneva Andriantsiory, Joseph Ben Geloun, Mustapha Lebbah
Several methods of triclustering of three dimensional data require the specification of the cluster size in each dimension. This introduces a certain degree of arbitrariness. To ad…
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation
Etienne Goffinet, Anthony Coutant, Mustapha Lebbah +2
Autonomous driving systems validation remains one of the biggest challenges car manufacturers must tackle in order to provide safe driverless cars. The high complexity stems from s…
Selecting the Number of Clusters with a Stability Trade-off: an Internal Validation Criterion
Alex Mourer, Florent Forest, Mustapha Lebbah +2
Model selection is a major challenge in non-parametric clustering. There is no universally admitted way to evaluate clustering results for the obvious reason that no ground truth i…
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…