Deep Learning's Generalization, Especially on structured discrete data

This front adapts from our legacy website and introduces a suite of deep learning tools we have developed for improve predictors' generalization, especially when on structured data types like text, graph, or sets. Please feel free to email me when you find my typos.

Background on why Generalization topics of Deep Learning are interesting?

Generalization refers to how a machine model adapts properly to new, previously unseen data.

Background on Representation Learning and Deep Learning

The performance of machine learning algorithms is largely dependent on the data representation (or features) on which they are applied. Deep learning aims at discovering learning algorithms that can find multiple levels of representations directly from data, with higher levels representing more abstract concepts. In recent years, the field of deep learning has lead to groundbreaking performance in many applications such as computer vision, speech understanding, natural language processing, and computational biology.

Why structured 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 structured data and structured 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

  • Please check out each item in our side-bar

We have focused on weak supervision in multiple of our papers along this line of research.



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