[nl]

> But for the many papers describing novel ML models, how does that work? They seem to use images that diagram out the different layers of the model. But is that truly "universal" the way that a psuedo-code algorithm is universal? As in, if the authors use PyTorch (or whatever), can I take the exact model they describe in their paper and apply it in MyFavoriteMLToolkit and achieve similar results?

Generally, yes.

If they are standard, well-known layers that exist in both PyTorch and TF you can take a paper that was implemented in one and implement in the other and expect similar results (assuming you know a reasonable number of details[1]).

If they are non-standard layers it can be hard. There are lots of details that you need to port and even with access to the source code it can be easy to miss things.

[1] Here's an example of how things are implemented differently - you can still get the same result, but you need to know what you are doing: https://stackoverflow.com/questions/60079783/difference-betw...