On the universality of deep learning
Web16 de fev. de 2024 · We prove a universality theorem for learning with random features. ... [22] El Amine Seddik M., Louart C., Tamaazousti M., and Couillet R., “ Random matrix theory proves that deep learning representations of GAN-data behave as Gaussian mixtures,” 2024, arXiv:2001.08370. Web14 de mar. de 2024 · Keywords: deep learning, convolutional neural net works, deep distributed con- volutional neural netw orks, universality , filter mask Mathematics Subject Classification 2000: 68Q32, 68T05
On the universality of deep learning
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WebLimits on what neural networks trained by noisy gradient descent can efficiently learn are proved whenever GD training is equivariant, which holds for many standard architectures and initializations. We prove limitations on what neural networks trained by noisy gradient descent (GD) can efficiently learn. Our results apply whenever GD training is … WebOne major challenge is the task of taking a deep learning model, typically trained in a Python environment such as TensorFlow or PyTorch, and enabling it to run on an …
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