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Category: Data science

Internships 2024

This document presents internships supervised by our consulting department or by our research & development department. Each project is an opportunity to feel both empowered and responsible for your own professional development and for your contribution to the company.

Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings

In this paper, we study graph representation learning and show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis.

SANGEA: Scalable and Attributed Network Generation

In this paper, we present SANGEA, a sizeable synthetic graph generation framework that extends the applicability of any SGG to large graphs. By first splitting the large graph into communities, SANGEA trains one SGG per community, then links the community graphs back together to create a synthetic large graph.

TS-Relax : Interprétation des représentations apprises pour les séries temporelles

Les modèles d’apprentissage de représentations sont de plus en plus utilisés, mais des modèles d’IA explicables et de confiance sont nécessaires. Ce travail présente l’adaptation aux séries temporelles d’une méthode d’interprétation de représentation initialement conçue pour les images.

Comparison of Machine Learning Approaches for POD24 Prediction

Early identification of patients with relapsing follicular lymphoma (FL) is critical but remains elusive. We initiated a collaboration between the academic CALYM Carnot Institute aiming at developing interpretable artificial intelligence (AI) models based on PET images to predict POD24.

A Fair Classifier Embracing Triplet Collapse

In this paper, we study the behaviour of the triplet loss and show that it can be exploited to limit the biases created and perpetuated by machine learning models.

IEEE Big Data 2022: the key takeaways

In December 2022, our research director Sabri Skhiri travelled to Osaka to attend IEEE Big Data 2022. He sums up the main trends, and shares his favourite talks and papers.

Dynamic Pairwise Wake Vortex Separations For Arrivals Using Predictive Machine Learning Models

Aircraft wake behaviour and meteorological information is monitored and processed using ML algorithms which determine the wake separation minimum reductions that can be safely applied between subsequent arriving aircraft.

Internships 2023

This document presents internships supervised by our software engineering department or by our research & development department. Each project is an opportunity to feel both empowered and responsible for your own professional development and for your contribution to the company.

AI For Aviation

Our team works with EUROCONTROL and WaPT to safely reduce wake separation between flights. Read on to read more about the two papers they recently published!