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The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented
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collaboration with other CEA teams, notably ; * Parallel and cluster computing environment and efficient LP/MILP algorithms for our large-scale models ; * Data structuring and storage solutions for model input
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resources to carry out your assignments. YOUR ASSIGNMENTS: The internship will develop and implement scalable, high‑performance algorithms for transient Lindblad dynamics tailored to the multi‑level Rydberg
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following areas: * Global economic analysis of long-term investment pathways , including the distribution of decarbonization effort across sectors and regions * Policy evaluation of global and sectoral
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Aggregation [9], DP-SGD [10]. Analyze trade-offs between privacy and robustness in different scenarios, including non-i.i.d. data distributions. [1] Zhu et al. Deep Leakage from Gradients. NeurIPS 2019. [2