Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by
its unnormalized (log-) probabilities. Over the past years, the machine learning community …

Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep
energy-based models are among competing likelihood-based frameworks for deep …

Deep neural networks have achieved remarkable success in computer vision tasks. Existing
neural networks mainly operate in the spatial domain with fixed input sizes. For practical …

Diffusion models are recent state-of-the-art methods for image generation and likelihood
estimation. In this work, we generalize continuous-time diffusion models to arbitrary …

This paper is a broad and accessible survey of the methods we have at our disposal for
Monte Carlo gradient estimation in machine learning and across the statistical sciences: the …

We propose a practical method for $ L_0 $ norm regularization for neural networks: pruning
the network during training by encouraging weights to become exactly zero. Such …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

Graph neural networks (GNNs) are a popular class of machine learning models that have
been successfully applied to a range of problems. Their major advantage lies in their ability …

Neural text generation models are often autoregressive language models or seq2seq
models. These models generate text by sampling words sequentially, with each word …