Variational auto-encoders (VAEs) are a powerful approach to unsupervised...
Probabilistic graphical modeling (PGM) provides a framework for formulat...
Generative adversarial networks (GANs) are a powerful approach to
unsupe...
Topic modeling analyzes documents to learn meaningful patterns of words....
Topic modeling analyzes documents to learn meaningful patterns of words....
Training deep generative models with maximum likelihood remains a challe...
Variational autoencoders (VAEs) learn distributions of high-dimensional ...
Recurrent neural networks (RNNs) are powerful models of sequential data....
Categorical distributions are ubiquitous in machine learning, e.g., in
c...
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-bas...
Variational inference (VI) is widely used as an efficient alternative to...
Probabilistic modeling is a powerful approach for analyzing empirical
in...