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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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: 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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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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full potential. Moreover, explaining AI decisions, referred to as eXplainable AI (XAI), is highly desirable in order to increase the trust and transparency in AI, safely use AI in the context of critical
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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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the intersection between biomedical engineering, complex systems and clinical neuroscience. NERV proposes new computational frameworks to analyze and model the spatiotemporal complexity of brain networks from