Astonishing - deep networks are just kernel machines, regardless of architecture: https://news.cs.washington.edu/2020/12/02/uncovering-secrets-of-the-black-box-pedro-domingos-author-of-the-master-algorithm-shines-new-light-on-the-inner-workings-of-deep-learning-models/ …
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"Tantalizingly, it may also be extensible to [...] symbolic and genetic learning." Could you expand on the application of kernel machines to symbolic learning, or point to any work exploring this direction?
