6 papers
When Labels Are Scarce: A Systematic Mapping of Label-Efficient Code Vulnerability Detection
Noor Khalal, Chakib Fettal, Lazhar Labiod +1
Machine-learning-based code vulnerability detection (CVD) has progressed rapidly, from deep program representations to pretrained code models and LLM-centered pipelines. Yet depend…
Classification EM-PCA for clustering and embedding
Zineddine Tighidet, Lazhar Labiod, Mohamed Nadif
The mixture model is undoubtedly one of the greatest contributions to clustering. For continuous data, Gaussian models are often used and the Expectation-Maximization (EM) algorith…
More Discriminative Sentence Embeddings via Semantic Graph Smoothing
Chakib Fettal, Lazhar Labiod, Mohamed Nadif
This paper explores an empirical approach to learn more discriminantive sentence representations in an unsupervised fashion. Leveraging semantic graph smoothing, we enhance sentenc…
Scalable Multi-view Clustering via Explicit Kernel Features Maps
Chakib Fettal, Lazhar Labiod, Mohamed Nadif
The proliferation of high-dimensional data from sources such as social media, sensor networks, and online platforms has created new challenges for clustering algorithms. Multi-view…
Graph Cuts with Arbitrary Size Constraints Through Optimal Transport
Chakib Fettal, Lazhar Labiod, Mohamed Nadif
A common way of partitioning graphs is through minimum cuts. One drawback of classical minimum cut methods is that they tend to produce small groups, which is why more balanced var…
Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)
Severine Affeldt, Lazhar Labiod, Mohamed Nadif
Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential w…