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.
Continue readingIn 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.
Responsible AI comes with the challenge of implementation. This survey aims to bridge the gap between principles and practice through a study of different approaches taken in the literature and the proposition of a foundational framework.
Continue readingLes 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.
Continue readingEarly 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.
Continue readingIn 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.
Continue readingAircraft 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.
Continue readingOur 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!
Continue readingTrustworthy machine learning is driving a large number of the ML community works in order to improve ML acceptance and adoption. In this paper, we show a first link between uncertainty and explainability, by studying the relation between calibration and interpretation.
Continue readingGalaxy cluster masses can be inferred indirectly using measurements from X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical observations. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation of the cluster masses from the Planck PSZ2 catalogue of galaxy clusters using a machine-learning method.
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