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.
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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!
Continue readingCalibrate to Interpret
Trustworthy 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 readingMass Estimation of Planck Galaxy Clusters using Deep Learning
Galaxy 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 readingAutomatic Parameter Tuning for Big Data Pipelines
Big 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 readingMultimodal Classifier For Space Target Recognition
We 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 readingAMI-Class: Towards a Fully Automated Multi-view Image Classifier
In 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 readingPolicy-based Automated Compliance Checking
Under 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.
Continue readingAnomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol
Anomaly detection is a widely explored domain in machine learning. Many models are proposed in the literature, and compared through different metrics measured on various datasets.
The most popular metrics used to compare performances are F1-score, AUC and AVPR.
DAEMA: Denoising Autoencoder with Mask Attention
Missing data is a recurrent and challenging problem, especially when using machine learning algorithms for real-world applications. For this reason, missing data imputation has become an active research area, in which recent deep learning approaches have achieved state-of-the-art results. We propose DAEMA: Denoising Autoencoder with Mask Attention.
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