Convolutional neural networks have been extremely successful in the image recognition
domain because they ensure equivariance with respect to translations. There have been many …

We present a unified framework to study graph kernels, special cases of which include the
random walk (Gärtner et al., 2003; Borgwardt et al., 2005) and marginalized (Kashima et al., …

Recent work by Cohen et al. has achieved state-of-the-art results for learning spherical
images in a rotation invariant way by using ideas from group representation theory and …

The application of kernel-based learning algorithms has, so far, largely been confined to
realvalued data and a few special data types, such as strings. In this paper we propose a …

We introduce a class of interatomic potential models that can be automatically generated
from data consisting of the energies and forces experienced by atoms, as derived from …

We review some recently published methods to represent atomic neighborhood environments,
and analyze their relative merits in terms of their faithfulness and suitability for fitting …

We introduce a family of kernels on graphs based on the notion of regularization operators.
This generalizes in a natural way the notion of regularization and Greens functions, as …

We describe N-body networks, a neural network architecture for learning the behavior and
properties of complex many body physical systems. Our specific application is to learn atomic …

The advantages of discriminative learning algorithms and kernel machines are combined with
generative modeling using a novel kernel between distributions. In the probability product …

We propose Cormorant, a rotationally covariant neural network architecture for learning the
behavior and properties of complex many-body physical systems. We apply these networks …