… • We introduce a new distributional framework for adversarial training of neural networks, which
… We propose two types of adversarial networks based on this distributional approach, and …

… and using Distributional Adversarial Networks (DAN) [2]to generate new offspring. DAN
uses a new distributional framework for adversarial training of neural network and operates on …

… Typically, z is sampled from a prior p(z) such as the standard Gaussian or uniform distributions
as in the generative adversarial networks (GANs) [20]. In this work, we sample z from a …

Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

… using Distributional Reinforcement Learning (DRL) and the Generative Adversarial Network
(… We show how the GAN can efficiently assist the distributional RL-based-IDS in enhancing …

… For our final experiments, we consider distributional robustness in the context of Q-learning,
a model-free reinforcement learning technique. We consider Markov decision processes (…

… Generative models are no exception, but recent advances in generative adversarial networks
… We hypothesize that the degree of distributional shift is related to the breadth of the training …

… We propose local distributional smoothness (LDS), a new notion of smoothness for statistical
model that can be used as a regularization term to promote the smoothness of the model …

… autoencoder” (AAE), which is a probabilistic autoencoder that uses the recently proposed
generative adversarial networks (GAN) to perform variational inference by matching the …

… distributional convergence. In this paper, we address these questions in a broad and unified
setting by defining a notion of adversarial … In a generative adversarial network, the goal is to …