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On the computational side, it is also important to note that kernel methods are now 100-1,000 faster than they were just three years ago. You may be interested by the KeOps library, which is to kernels and geometric ML what cuDNN is to convolutions. You could also have a look at GPyTorch and the Falkon solvers: the software bottlenecks that were holding back kernel methods are progressively being lifted. Million-scale datasets are now routinely processed in minutes/hours and billion-scale problems are starting to become tractable.
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