This guy (Phil Wang, <a href="https://github.com/lucidrains">https://github.com/lucidrains</a>) seems to have the hobby to just implement all models and papers he finds interesting. See his GitHub page. He has 228 repos, and most of them are some implementation of some machine learning paper. Some of those repos are quite popular.
I don't understand how this got so many upvotes. It takes only one minute to read the code and realize that the model is not yet completely implemented. Sometimes I have the feeling that people upvote posts without even reading them...<p>Of course, it's good work, and knowing lucidrains trajectory it's probably going to be implemented in the following days/weeks. But I wonder how many people have at least opened the link before upvoting it.
This question is a tangent to your work. Having never used music LMs, and only being cursorily aware of them - how do you keep up with the sota in your field?
Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch.<p><a href="https://github.com/lucidrains/musiclm-pytorch/blob/main/musiclm.png">https://github.com/lucidrains/musiclm-pytorch/blob/main/musi...</a>