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on heuristics, approximation algorithms, and optimization techniques to generate practical solutions that only reach local minima. Some common approaches include: (1) Iterative refinement methods that improve
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redefinition of behavioral features or pose challenges in their detection. The projects To address these challenges, we propose developing a Bayesian Program Synthesis (BPS) methodology for generating synthetic
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a complex and highly technical environment, OR safety requires the simultaneous management of numerous flows (material resources, human resources, technical environment, etc.) to ensure smooth
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. Côte d’Azur & INRIA), will be focused on the development and the understanding of deep latent variables models for unsupervised learning with massive heterogenous data. Although deep learning methods and
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the understanding of deep latent variables models for unsupervised learning with massive and evolving heterogenous data. Although deep learning methods and their statistical extensions, the deep latent
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PyTorch or TensorFlow is a plus. Useful Information/Bibliography: [1] Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. Federated learning: Challenges, methods, and future directions. IEEE
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enabling trustworthy AI adoption through methods and tools for compliance, readiness, and performance evaluation. In the field of Smart Cities, we lead the operations of the CitCom.ai project. CitCom.ai is
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methods to decipher the transformation of sounds both at the peripheral and central levels. Project summary The aim of this project is to study how information about sound frequency or intensity
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advice and guidance on the use of existing analysis methods and tools. Keeping up-to-date with the latest technologies and analysis methods. Regularly monitoring relevant literature and publications, and
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating