Keeping Up with Deep Learning - 19 Oct 2020
By: Frank Odom
Tags: Deep Learning, Artificial Intelligence, Machine Learning, Programming, Data Science

Keeping Up with Deep Learning — 19 Nov 2020

Deep learning papers, blog posts, Github repos, etc. that I liked this week.

Photo by Noah Silliman on Unsplash

Every week, I’ll be posting some of the projects in deep learning that caught my eye, ranging from research papers to blog posts and Github repos. All thoughts and opinions are my own. Follow me or check back next week for more. Enjoy!

Stylized Neural Painting

[Project Site][Paper][Github]

This project completely blew me away. Stylized Neural Painting uses deep learning to generate realistic painting artworks. Similar to previous methods like Neural Style Transfer, it can control the style of the generated artwork by providing a style image. But neural style generates stylized images on a pixel-by-pixel basis, whereas SNP actually predicts the brushstrokes needed to paint the image. It’s a really cool and unique approach, and I can’t wait to learn more about how the authors did it.

AI Dungeon

[Webpage]

AI Dungeon has been around for a while, but recently got some cool new features. It uses natural language models to generate interactive text adventures, similar to Dungeons & Dragons. Premium subscribers can now use the “Dragon” model (GPT-3) for improved story generation. I tried Multiplayer mode for the first time this week, and played several other games this past weekend. Highly recommend.

Phasic Policy Gradient

[ArXiv][Github]

A fantastically simple modification to the PPO algorithm, which improves on-policy training of actor-critic models in reinforcement learning.

Fourier Neural Operators

[Paper][Github]

Fourier Neural Operators are able to numerically solve partial differential equations (PDEs) many times faster than traditional PDE solvers. Using a mixture deep learning and Fourier transforms, the authors achieve state-of-the-art accuracy with over 1000x speed-up in some cases. This research has very big practical implications, because PDEs are so prevalent in the natural sciences (e.g. physics, chemistry, weather science).

FNO uses a cool trick called Fourier convolutions. If you haven’t already, check out my recent article on Fourier convolutions to learn more.

Long Range Arena

[ArXiv][Github]

Lately, we’ve seen an uptick in research focused on improving the efficiency of Transformers on long input sequences. Google published the Long Range Arena to establish a suite of benchmarks for transformer performance. Right now, this doesn’t look like the sexiest research topic. But I think it will pay dividends for the deep learning community in the near future.

Photo by Samule Sun on Unsplash

Batch Normalization in 3 Levels of Understanding

[Article]

I thought I understood batch normalization well enough, but this article proved me wrong. Batch normalization is ubiquitous in modern deep learning, so I highly recommend reading this if you’re a deep learning practitioner.

Photo by Jonathan Farber on Unsplash

Objectron Dataset

[Blog Post]

Objectron is a large-scale dataset for 3D object detection, which was recently released to the public by Google AI. It contains 15,000 short video clips with 3D bounding box annotations. Objectron could enable a range of future AR applications centered around everyday objects like mugs, books, shoes, etc. I’m excited by the new possibilities.

Subscribe

Follow me to get future weekly updates, as well as all of my other technical articles.