The Kafka Summit brought together industry experts, developers, and enthusiasts to discuss the latest advancements and practical applications of event streaming and microservices. In this article, our CTO Sabri Skhiri delves into some of the keynotes and talks that took place during the summit.
Continue readingThe recent Privacy Enhancing Technologies (PETs) London Conference convened stakeholders across Legal, IT, and various business domains to delve into the evolving data privacy and security landscape. With a focus on collaborating and sharing data through cutting-edge PETs, the summit provides valuable insights into how organisations can optimise the value of their data while ensuring privacy and security. Here, we distil the key themes and insights shared during the conference, shedding light on the interesting talks.
Continue readingOur CTO, Sabri Skhiri, recently travelled to Sorrento for IEEE Big Data 2023. In this article, Sabri explores for you the various keynotes and talks that took place during the conference, highlighting the noteworthy insights and the practical applications shared by industry leaders.
Continue readingPresent study highlights the significance of dataset size in ICI microbiota models and presents a methodology to enhance the performances of a multi-cohort-based ML approach.
Continue readingThis 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.
Continue readingIn 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.
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.
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.
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