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interpreting the correlations and causalities identified. Data-driven AI approaches guided by models will initiate a well-known virtuous cycle process ranging from explanation (as a capability of AI tools
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that optimizes the machine learning process, making it more accessible and efficient for users with varying levels of expertise [1]. AutoML leverages algorithms and computational capabilities to automate key
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addressed to handle real-life HPC programs. How to parallelize the whole process? How to reduce the overall complexity? A trace-based solution could also be investigated. Validation. The approach will be
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physical model of the EEG headset on the one hand, and on a term learned on residuals by an AI model on the other. Prediction of fMRI activity using EEG signal : The main idea is to model EEG and fMRI
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process [ 10 ]. Principales activités Objectives The main goal of the PhD project is to elucidate the role of mesoscales network structures in generalizable artificial intelligence. Speci cally
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: messages are lost because of physical phenomena such as external interference and multi-path fading. A second challenge is that, in some use cases such as swarm robotics, real-time constraints come into play
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: variability in fabrication process parameters, latent defects or even environmental stress. One of the overlooked aspects is the role that HW faults can have in AI decisions. Indeed, there is a common belief