Biological Sequence Modeling with Convolutional Kernel Networks

This thesis studies empirical properties of deep convolutional neural net- works, and in particular the Scattering Transform.. Indeed, the theoretical anal- ysis of the latter is

A decision tree obtained on the protein-protein interaction network using expression data, phylogenetic pro- files and localization data as inputs.. The tree size was determined

This leads to a new connection between ANN training and kernel methods: in the infinite-width limit, an ANN can be described in the function space directly by the limit of the NTK,

In the procedure highlighted by Cho and Saul, the hidden layers are greedily trained by first applying a kernel PCA projection followed by features selection based on mutual

Co-existing with and within the strong corporate culture at Saturn, we found work group subcultures whose socialization processes can be just as strong as those at the corporate

We study the inductive bias in such a learning regime by analyzing smoothness, stability and approximation properties for this kernel, and comparing it to other deep kernels

(2006) study regular- ization properties of dot-product kernels on the sphere using spherical harmonics, and Bach (2017a) derives eigenvalue decays for such dot-product kernels

Specifically, this perspective (i) provides a common umbrella for many existing regularization principles, including spectral norm and gradient penalties, or adversarial training,