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[Discussion] Tips on getting started implementing ML papers in code? (alien.top)

submitted 11 months ago by ExitWest7@alien.top to c/machinelearning@academy.garden

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I've been diving a lot deeper into some interesting neural network papers recently and I'm looking to try and implement some of the models detailed in the papers. In general, I know that many papers include the code or I can just google the code to implement the model but I want to push myself to start implementing from scratch more.

Could anyone offer some tips on how they got started or gained the skills to be able to implement a model effectively within a few hours? Any advice would be much appreciated!

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[–] Avistian@alien.top 1 points 11 months ago (1 children)

Out of curiosity - What do you do, when you are missing some key information?

[–] DollyNorman@alien.top 1 points 11 months ago (2 children)

I think this is why implementing is such a useful learning tool. Papers I tried to implement I probably read cover-to-cover 10 or 20 times, as opposed just skimming abstract, method, results.

When missing key info, after searching the paper a few times:

my first step was see if I could find anything on paperswithcode or github.
failing that, google searches. see if I could find anything on forums, stack exchange sites, reddit.
uni library or academic paper web search engines
see if any papers that cite the paper I'm implementing give some clue
last resort was to look into papers they cite.

That was all the tricks I had, keen to know any more.

[–] Ifkaluva@alien.top 1 points 11 months ago (1 children)

What do you do if you find a reference implementation? Just run it? Try to implement from scratch?

[–] DollyNorman@alien.top 1 points 11 months ago

I tried to copy as much as possible from the paper, then fill in the blanks with how I would have solved it. I only had a partial solution but I still learnt heaps more than just reading the paper.

[–] DryArmPits@alien.top 1 points 11 months ago

You can also contact the authors to ask them. Most of us are not monsters and will happily talk about our work. :)