Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response

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Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response

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Predicting the long-term collective behaviour of fish pairs with deep learning

Gas Sensing with Rotary-Wing Nano Aerial Vehicles

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Partial discharge localization in power transformer tanks using machine learning methods

The multimodality cell segmentation challenge: toward universal solutions

Enabling Uncertainty Estimation in Iterative Neural Networks

Digital Twins of Stone Masonry Buildings for Damage Assessment

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Machine Learning Security Against Data Poisoning: Are We There Yet?

On the Generalization of Stochastic Gradient Descent with Momentum

In-Sensor Passive Speech Classification with Phononic Metamaterials

Learning Informative Health Indicators Through Unsupervised Contrastive Learning