
An Opinionated Guide to Machine Learning Research - hardmaru
http://joschu.net/blog/opinionated-guide-ml-research.html
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saeranv
This guy's time management is impressive. If you're diligently spending 1 day
a week exploring new research methods, writing and reviewing your lab
notebook, staying up-to-date on current SOTA research, writing SOTA algorithms
for review, reviewing textbooks, engaging in "personal development" time
(which apparently means general ML research) - when do you have time to
actually do your main research?

~~~
leereeves
One day a week hardly seems like enough to stay up to date on SOTA research
(at least, anything more than being aware it exists).

There are over 200 articles on the arXiv in stat.ML from this week.

[https://arxiv.org/list/stat.ML/recent](https://arxiv.org/list/stat.ML/recent)

~~~
denzil_correa
Not all 200 articles in stat.ML are SOTA research.

~~~
leereeves
Certainly not, but an 8 hour day dedicated to deciding _just_ that gives you
only 2 minutes per article.

~~~
backpropaganda
You only need a few seconds to read the title, and you can cut away 150 out of
those 200 with that. If it's a paper simply not in your area of research, you
don't need to bother. You can then read the abstract (30 seconds) for the
rest, and finally you'll pick 5-10 papers to read. You'd skim them with 5-10
mins per paper, and if you like what you see in 1-2 papers, you'll give it a
more through read. So this is 10 mins + 30 mins + 2 hours + buffer, which is
at best 3-4 hours.

Of course this requires a strong filter at the first stage. You can also set
aside time to investigate specific topics outside your work, but you won't do
that as often as you keep up with your own literature.

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platz
re: goal-driven vs idea-driven

Feynman's algorithm

\- keep a bunch of problems in your mind

\- every time you hear a new solution, test it against your twelve problems to
see whatever helps

and you can flip it around

\- keep a bunch of solutions in your mind

\- every time you hear a new problem, test it against your twelve solutions to
see whatever helps

so to optimize this, what you should do is

\- keep a half-dozen problems in your mind AND keep a half-dozen solutions in
your mind

\- then for each new problem, test it against your solutions

\- then for each new solution, test it against your problems

[https://www.youtube.com/watch?v=Z8KcCU-p8QA](https://www.youtube.com/watch?v=Z8KcCU-p8QA)

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master_yoda_1
Question to author: Why new and coming researcher has to be young? Why not an
old aged person can do research. I see entry level research position blatantly
discriminate based on age? If you have gender diversity then why not age
diversity?

