I like building awesome things with awesome people.
This article explores a seq2seq architecture for solving simple probability problems in Deepmind's Mathematics Dataset. A transformer is used to map questions to intermediate steps, while an external symbolic calculator evaluates intermediate expressions. This approach emulates how a student might solve math problems, by setting up intermediate equations, using a calculator to solve them, and…
I show that adversarial robustness makes neural style transfer work on a non-VGG architecture.
In this post, I talk about using World Models to train agents to paint with real painting software. This includes my thought process, approach, failures, and some future work in this direction I’m excited about.
Recently, Magenta, Google’s “open source research project exploring the role of machine learning as a tool in the creative process” gave me the opportunity to write a blog post on their platform about my recent work porting arbitrary style transfer to the browser. Check it out here!