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limited to the implantation of continuous-valued neural networks (e.g. multi-layer formal neural networks). The development of a new hardware substrate must be accompanied by a more ambitious technological
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of Higher Education and Research (MESR). PINNACLE: Physics-Informed Neural Networks for Accelerated Cloud Light-Scattering Emulation Artificial intelligence is profoundly transforming atmospheric
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely
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respect to an infinitesimal perturbation of the dataset, provide a rigorous framework to: - **Identify the most informative samples** among the predictions of a deep neural network (DNN), with the goal
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 2 months ago
, 3], significant challenges related to running complex AI algorithms such as Deep Neural Network (DNN) inference on lightweight platforms with limited computational power and relying on potentially
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of electrical topologies (AC, DC, three-phase) and will integrate both standard and atypical wear cases. On this basis, high-performance artificial intelligence models will be developed. By combining neural
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to modern genomic datasets that may involve hundreds of populations. He/she will also develop probabilistic GO models inspired from the Redundancy Analysis approach and extend it by introducing Neural
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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 2 months ago
. This issue can have safety implications, particularly in closed-loop setups. Physically Informed Machine Learning (PIML), and in particular Physics-Informed Neural Networks (PINN), are less dependent on data
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theoretical research is focused on embodied neuroAI, recognising that the body influences biological neural networks, the continuity of actions, and sensory inputs. Leveraging advancements in Drosophila genetic
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approaches that build processes by mutation operators [1], natural language processing techniques with recurrent short-term memory (LSTM) neural networks [2], and variational autoencoders (VAE