The website introduces a suite of deep learning tools we have developed for learning patterns and making predictions on discrete data, like text, graph, or sets. Feel free to submit pull requests when you find my typos.

Background of Representation Learning and Deep Learning


Why Discrete Data is Interesting?

Deep learning constructs networks of parameterized functional modules and is trained from reference examples using gradient-based optimization [Lecun19].

Since it is hard to estimate gradients through functions of discrete random variables, researching on how to make deep learning behave well on discrete data and discrete representation interests us. Developing such techniques are an active research area. We focus on investigating interpretable and scalable techniques for doing so.

Relevant Papers we published



Thanks for reading!