7 citations · 8 across the 7 of their papers we have counts for
7 papers
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…
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…