Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …

We study the problem of learning from data representations that are invariant to
transformations, and at the same time selective, in the sense that two points have the same …

The wavelet scattering transform creates geometric invariants and deformation stability. In
multiple signal domains, it has been shown to yield more discriminative representations …

We introduce a novel training principle for generative probabilistic models that is an
alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSNs) …

The scattering transform is a multilayered wavelet-based architecture that acts as a model of
convolutional neural networks. Recently, several works have generalized the scattering …

In this work, we present and study Continuous Generative Neural Networks (CGNNs),
namely, generative models in the continuous setting: the output of a CGNN belongs to an …

In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders
(MGVAE), the first hierarchical generative model to learn and generate graphs in a …

Recently, hypergraph learning has shown great potential in a variety of classification tasks.
However, existing hypergraph neural networks lack flexibility in modeling and extracting …

Abstract Machine learning is increasingly targeting areas where input data cannot be
accurately described by a single vector, but can be modeled instead using the more flexible …

Adversarial training methods are frequently-used empirical defense methods against
adversarial examples. While many regularization techniques demonstrate effectiveness …