31–40 of 98 posts
Earlier quoted context omitted.
what's the reason it's boycotted? appeal to authority isn't very informative for those not keeping up with this stuff
Subscription fee requirements for access, mostly.
i.e. Why should people have to pay to view the results of what is often publicly funded research? Even more so why should researchers have to pay to publish work that the publishers profit off of but the researchers don't.
Earlier quoted context omitted.
what's the reason it's boycotted? appeal to authority isn't very informative for those not keeping up with this stuff
Subscription fee requirements for access, mostly.
Earlier quoted context omitted.
Subscription fee requirements for access, mostly.
Have any alternative journals in that area popped up that are both open-access and without submission fees?
Earlier quoted context omitted.
The ML field today is all about results. Get the high scores and figure out the math later. Not that there is anything wrong with this, we still at the stage where we're banging rocks together wondering what works and what doesn't, and the theories will come later.
So what is the meaning of "ML experts" if all they do is trial and loss experiments! Is Math PhD just used for hiring signal rather than actual requirements to do ML projects?
After all if intelligence comes out artificially, I hope it does not have rule.
This work was published in “Nature Machine Intelligence“, the journal boycotted by pretty much everyone who matters in ML. Reflects poorly on the authors, regardless of the actual merits of their finding.
As far as I can tell, the article isn't really of any interest either. Formulating a proposition that is independent of standard axioms is simple. Formulating it in the language of machine learn is an exercise. The main thing is the authors didn't provide any motivation for this to matter to the overall enterprise of machine learning, because there isn't motivation for this. It's just a novelty.
This work was published in “Nature Machine Intelligence“, the journal boycotted by pretty much everyone who matters in ML. Reflects poorly on the authors, regardless of the actual merits of their finding.
As far as I can tell, the article isn't really of any interest either. Formulating a proposition that is independent of standard axioms is simple. Formulating it in the language of machine learn is an exercise. The main thing is the authors didn't provide any motivation for this to matter to the overall enterprise of machine learning, because there isn't motivation for this. It's just a novelty.
Earlier quoted context omitted.
Subscription fee requirements for access, mostly.
and fees for publishing. i.e. Why should people have to pay to view the results of what is often publicly funded research? Even more so why should researchers have to pay to publish work that the publishers profit off of but the researchers don't.
Or maybe I’m just taking crazy pills..
Aggregability is NP-Hard: https://www.google.com/url?sa=t&source=web&rct=j&url=http://...
That is, even for linear systems, determining whether or not macrovariables (e.g. complete eigenvector sets, complete embeddings) exist for a given space is an NP-Hard problem. With linear systems serving as, effectively, a lower bound for ML problems (because, otherwise, why are you even ML'ing the thing?), whether or not a problem is "learnable" is, unsurprisingly, probably NP-Hard. Aggregability and learnability look staggeringly similar to me.
Demonstrating otherwise would be a huge result. Writing a paper confirming Kreinovich and Shpak in a slightly different domain is basically a "Water is Wet" paper.
Having not yet read the paper, I'm unaware of any new ground here.
One doesn't need to go back to Gödel for this to make it a "huh" moment. Instead, go back to 2006 to make it a "duh" moment. Aggregability is NP-Hard: https://www.google.com/url?sa=t&source=web&rct=j&url=http://... That is, even for linear systems, determining whether or not macrovariables (e.g. complete eigenvector sets, complete embeddings) exist for a given space is an NP-Hard problem. With linear systems serving…