Probabilistic methods for neural combinatorial optimization

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Publications associées (29)

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Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

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Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

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Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

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Incorporating Projective Geometry into Deep Learning

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EPFL2024