What are the 10 must-reed papers on machine learning for a software engineer?

I’m a software engineer with 20 years of experience, deep understanding of the graphics pipeline and the linear algebra in computer graphics as well as some very very very basic experience with deep-learning (I know what a perceptron is, did some superficial modifications to stable diffusion, trained some yolo models, stuff like that).

I know that 10 papers don’t get you too far into the matter, but if you had to assemble a selection, what would you chose? (Can also be 20 but I thought no one will bother to write down this many).

Thanks in advance 🙂