
Ask HN: What do you use to keep you update on ML/DL? - ReDeiPirati
Hi everyone! What do you use to navigate-in-the-noise and keep you update in this field? Excluding HN which type of resources do you recommend to check regularly?
======
mlthoughts2018
1\. Read software libraries first. If new techniques make it into standard
implementations of major libraries, it’s a solid signal the new technique is
actually worth your time.

2\. Don’t bother with arxiv / conferences / etc., until a paper is at least a
year old and remains highly cited & investigated. In DL especially, papers are
often slapdash permutations of existing ideas applied with one weird extra
trick that ekes out “SOTA” results in one benchmark. Architectural choices are
often not motivated by any evidence, and whatever scramble of grad student
descent was needed to arrive at the final architecture choice is conveniently
left out of the paper (and often would call the result into question from a
multiplicity of testing POV).

3\. Read what you enjoy. It shouldn’t feel hard to stay current in your main
area. If it is feeling hard, you’re trying to force it. Maybe pick a sub-
specialty you enjoy and only focus on that for a while.

4\. Forgive yourself for not staying current. You are busy, reading research
papers takes time & is not often the best use of time compared with competing
interests. This is normal. If some situation / job interview / academic
program is making you feel otherwise, it’s probably a red flag about that
thing.

~~~
croh
solid advice. thanks

------
p1esk
[http://www.arxiv-sanity.com/](http://www.arxiv-sanity.com/) Usually just top
hype/recent, plus specific keywords relevant to my research.

------
codingslave
I read
[https://old.reddit.com/r/MachineLearning/](https://old.reddit.com/r/MachineLearning/).
It's not perfect, but most high profile papers will be posted there. The
better ones will have a good number of comments, which helps me sift through
the garbage. I browse it on a daily basis, never really reading the papers,
just seeing what topics are being discussed most.

