Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Abstract We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical
systems. GCANs are based on symmetry group transformations using geometric (Clifford) …

Before we start thinking about (deep) generative modeling, let us consider a simple
example. Imagine we have trained a deep neural network that classifies images (x∈ ℤ D) of …

Normalizing flows and variational autoencoders are powerful generative models that can
represent complicated density functions. However, they both impose constraints on the …

Training convolutional neural networks with a Lipschitz constraint under the $ l_ {2} $ norm
is useful for provable adversarial robustness, interpretable gradients, stable training, etc …

In learning with recurrent or very deep feed-forward networks, employing unitary matrices in
each layer can be very effective at maintaining long-range stability. However, restricting …

Training convolutional neural networks (CNNs) with a strict 1-Lipschitz constraint under the l
{2} norm is useful for adversarial robustness, interpretable gradients and stable training. 1 …

Normalizing flows model complex probability distributions using maps obtained by
composing invertible layers. Special linear layers such as masked and 1 {\texttimes} 1 …

The robustness of neural networks against input perturbations with bounded magnitude
represents a serious concern in the deployment of deep learning models in safety-critical …

Normalizing flows model probability distributions by learning invertible transformations that
transfer a simple distribution into complex distributions. Since the architecture of ResNet …