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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have
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imaging in medicine is a secondary target. Led by Prof. Wiaux, the project is supported by a unique team of international partners: Prof. Pesquet from the Digital Vision Centre at Université Paris-Saclay
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