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A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, obtaining accuracies in the range of 87-90% while being more amenable to theoretical analysis.
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A downsampled variant of imagenet as an alternative to the CIFAR datasets
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Scaling the scattering transform: Deep hybrid networks
E. Oyallon, E. Belilovsky, and S. Zagoruyko · 2017
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Falkon: An optimal large scale kernel method
A. Rudi, L. Carratino, and L. Rosasco · 2017
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Greedy layerwise learning can scale to imagenet
E. Belilovsky, M. Eickenberg, and E. Oyallon · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
A. Jacot, F. Gabriel, and C. Hongler · 2018
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