[p1esk]

You seem to be exceptionally well funded, and/or have few deadline constraints

I wish! :)

you get spammed with "good ideas"

Again, I wish!

In the subfield I'm focused on at the moment (efficient mapping of NN algorithms to specialized hardware, low precision computation, model compression) I don't see good ideas very often (fewer than one good paper a week). Previously I worked on music generation - also didn't really feel spammed with good ideas.

[nonbel]

I don't mean this to be adversarial, but what exactly is it you do that would not be sped up by checking someone else's results directly before fiddling around and then trying out your own implementation?

[p1esk]

But that's my point: their results are not that important to me.

As an example, recently I saw a paper on NN weight quantization, which had a very interesting idea, but the results were not impressive. I don't remember if they had any code published or not, but it didn't matter - I wanted to see what kind of results I'd get if I implemented it. Turned out it works really well, much better than what they reported in the paper.

[nonbel]

Here is an idea: inverse dropout.

How would you implement that?

[p1esk]

Link to paper?

[nonbel]

http://jmlr.org/papers/v15/srivastava14a.html

[p1esk]

You linked to the original dropout paper. What’s “inverse dropout”?