Re:Some of the most important deep learning papers
by j2kun on Thursday August 25, 2016 @11:08AM
(#12359570)
> Academia rarely ranks things important to the application (aka industry) of a field of study.
"Rarely" is a strong word. I think the truth is closer to, "Academia has screwed up as a whole in a very small number of cases, but usually works just fine, and always self-corrects eventually."
Re:Some of the most important deep learning papers
by hueving on Thursday August 25, 2016 @12:54PM
(#12360531)
I'm not talking about screwups. I'm talking about fundamentally unaligned objectives between academia (advancing the edges of knowledge) and industry (making knowledge useful in an economic sense).
It's not a problem, it's just something you have to recognize so you don't have the wrong expectation.
Not really. Academia rarely ranks things important to the application (aka industry) of a field of study. An efficiency improvement that completely changes the economics of something would likely be rejected from an academic conference for being 'incremental'. Similarly, 'novel' work is much more highly rewarded in these conferences than improvements to existing techniques, experiment replications, or negative results, all of which are more important to industry.
Bibliometrics are garbage and represent little more than a popularity contest (and what is controversial). It's basically like if you took up votes and down votes on reddit and just had them both increment a single score. If they were worth anything, there wouldn't be such a thing as review papers that highlight important developments.