How To Keep Up With Machine Learning Research: 3 More Tools
Last year I wrote about how I keep up with machine learning research. ICYMI, here was the short list:
- Read the Seminal Papers
- Take Advantage of arXiv Sanity
- Subscribe to Papers with Code
One year later there is still a Sisyphean amount of machine learning research to keep up with. This calls for more tools to help curate the papers that are valuable to take a deep dive into.
Here are 3 tools you might use to do that:
Take Advantage of YouTube
More than half of people who use YouTube already use it to learn how to do something that they have never done before. It turns out that YouTube is also a perfect place to learn about machine learning research.
One of the best places to start is with Two Minute Papers. This channel does exactly what their name implies — they review papers in 2 minutes (ish). Curated by Károly Zsolnai-Fehér, the videos clearly and concisely discuss papers, the advancement(s) that they present, and why this matters.
If you’re more interested in listening to lecture-style presentations, then you might be interested in checking out Lex Fridman. The MIT researcher focuses on human-centered AI & autonomous vehicles.
Follow the Right Subreddits
Reddit bills itself as “the front page of the internet”. It does this by building user-curated communities. There is a community for nearly everything, so of course there is one for machine learning — /r/MachineLearning. Content on the /r/MachineLearning subreddit is organized into 4 categories:
- Discussion [D]
- News [N]
- Research [R]
- Project [P]
One way to find content here is by taking a look at the top posts (in the past hour, 24 hours, week, month, year, or all time). Some of the top (research) posts of all time include research on color transfer, deep image analogy, and deep image priors.
Some other subreddits that would be useful include /r/computervision, /r/LanguageTechnology, and /r/datascience.
Curate Your Twitter
Twitter is an excellent place to directly follow the people and companies that are directly doing the research. You can follow some of the stalwarts of the AI community directly — from Andrew Ng to Yann LeCun to Ian Goodfellow. You can check out a great list of accounts to follow here. Often, these thought leaders will directly share their work. For example, here is Ian Goodfellow sharing improvements in Generative Adversarial Networks over the past 4.5 years:
