ML in general seems to have a really low standard for citing related and earlier work. In part I think that is because a lot of papers are based on relatively p

ML in general seems to have a really low standard for citing related and earlier work. In part I think that is because a lot of papers are based on relatively poorly developed foundations or empirical evidence. A prime example are the "Neural ... " papers, which in my view largely took existing research in the optimal control / differential equations literature, prepended the word "Neural" and provided some "interesting" application. This would be fine, if there was some proper context given for the work, but unfortunately that wasn't always the case there.