Ill mention a few I tend to go back for sheer joy. I'm a data scientist, so the ones I really like tend to be statistics focused since (I don't do DL):

- Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes [1] Brought to me by [this](https://simplystatistics.org/posts/2017-09-04-deep-dive-ogat...) blogpost a while ago, for me it shows what a really good statistical analysis based on a model developed for the task at hand looks like. Great stuff.

- Conducting highly principled data science: A statistician’s job and joy [2] Meng is such a good writter. This one really puts into perspective the job of good principles when tackling any kind of task that a data scientist will face. Principles, theoretical principles, are what avoids messing up a task.

1. https://amstat.tandfonline.com/doi/abs/10.1080/01621459.1988... 2. https://www.sciencedirect.com/science/article/pii/S016771521...

I read The Dynamo paper [1] early in my career and its use of consistent hashing inspired me to use similar approaches in systems I've developed.

1. https://s3.amazonaws.com/systemsandpapers/papers/amazon-dyna...

English is not my native language. I read this paper a few times over past decade but never quite understood how consistent hashing actually works at scale as nodes go offline and get replaced.