6 citations · 14 across the 7 of their papers we have counts for
8 papers
A Geometric Perspective for High-Dimensional Multiplex Graphs
Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa
High-dimensional multiplex graphs are characterized by their high number of complementary and divergent dimensions. The existence of multiple hierarchical latent relations between…
Graph Attention Network for Camera Relocalization on Dynamic Scenes
Mohamed Amine Ouali, Mohamed Bouguessa, Riadh Ksantini
We devise a graph attention network-based approach for learning a scene triangle mesh representation in order to estimate an image camera position in a dynamic environment. Previou…
TopoDetect: Framework for Topological Features Detection in Graph Embeddings
Maroun Haddad, Mohamed Bouguessa
TopoDetect is a Python package that allows the user to investigate if important topological features, such as the Degree of the nodes, their Triangle Count, or their Local Clusteri…
Exploring the Representational Power of Graph Autoencoder
Maroun Haddad, Mohamed Bouguessa
While representation learning has yielded a great success on many graph learning tasks, there is little understanding behind the structures that are being captured by these embeddi…
Context Matters: Self-Attention for Sign Language Recognition
Fares Ben Slimane, Mohamed Bouguessa
This paper proposes an attentional network for the task of Continuous Sign Language Recognition. The proposed approach exploits co-independent streams of data to model the sign lan…
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift
Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini
Clustering using deep autoencoders has been thoroughly investigated in recent years. Current approaches rely on simultaneously learning embedded features and clustering the data po…