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.
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.
Continue readingBig data frameworks generally constitute a pipeline, each having a different role. This makes tuning big data pipelines an important yet difficult task given the size of the search space. We propose to use a deep reinforcement learning algorithm to tune a fraud detection big data pipeline.
Continue readingWe propose a multi-modal framework to tackle the SPARK Challenge by classifying satellites using RGB and depth images. Our framework is mainly based on Auto-Encoders to embed the two modalities in a common latent space in order to exploit redundant and complementary information between the two types of data.
Continue readingIn this paper, we propose an automated framework for multi-view image classification tasks. The proposed framework is able to, all at once, train a model to find a common latent representation and perform data imputation, choose the best classifier and tune all necessary hyper-parameters.
Continue readingUnder the GDPR requirements and privacy-by-design guidelines, access control for personal data should not be limited to a simple role-based scenario. For the processing to be compliant, additional attributes, such as the purpose of processing or legal basis, should be verified against an established data processing agreement or policy.
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