11–17 of 17 posts
We've compiled a fairly extensive list of deep learning papers here: http://deeplearning4j.org/deeplearningpapers We also Tweet out new ones as they're published here: https://twitter.com/deeplearning4j
With this field advancing so fast, I guess if we could do something like this, that would be great: Maintain a running list of: - 3-5 most important papers in the last 3 months - 3-5 in the last 1 year (not all in the 3 month list would make into this list) - 3-5 in the last 5 years. I guess it's difficult for a small number of people to rank the papers. Maybe a hackernews or reddit style upvote/downvote system can b…
With this field advancing so fast, I guess if we could do something like this, that would be great: Maintain a running list of: - 3-5 most important papers in the last 3 months - 3-5 in the last 1 year (not all in the 3 month list would make into this list) - 3-5 in the last 5 years. I guess it's difficult for a small number of people to rank the papers. Maybe a hackernews or reddit style upvote/downvote system can b…
> I guess it's difficult for a small number of people to rank the papers. This is what's done every year at the AI conferences. No need for a new voting system. - 3-5 most important papers in the last 3 months: The "best paper award" deep learning papers of the most recent 1-2 AI conferences. - 3-5 in the last 1 year: Top cited deep learning papers from AI conferences this year. - 3-5 in the last 5 years: Top cited d…
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.
With this field advancing so fast, I guess if we could do something like this, that would be great: Maintain a running list of: - 3-5 most important papers in the last 3 months - 3-5 in the last 1 year (not all in the 3 month list would make into this list) - 3-5 in the last 5 years. I guess it's difficult for a small number of people to rank the papers. Maybe a hackernews or reddit style upvote/downvote system can b…
> I guess it's difficult for a small number of people to rank the papers. This is what's done every year at the AI conferences. No need for a new voting system. - 3-5 most important papers in the last 3 months: The "best paper award" deep learning papers of the most recent 1-2 AI conferences. - 3-5 in the last 1 year: Top cited deep learning papers from AI conferences this year. - 3-5 in the last 5 years: Top cited d…
Earlier quoted context omitted.
> I guess it's difficult for a small number of people to rank the papers. This is what's done every year at the AI conferences. No need for a new voting system. - 3-5 most important papers in the last 3 months: The "best paper award" deep learning papers of the most recent 1-2 AI conferences. - 3-5 in the last 1 year: Top cited deep learning papers from AI conferences this year. - 3-5 in the last 5 years: Top cited d…
>This is what's done every year at the AI conferences. No need for a new voting system. 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 conferenc…
"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."
Earlier quoted context omitted.
>This is what's done every year at the AI conferences. No need for a new voting system. 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 conferenc…
> 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."
It's not a problem, it's just something you have to recognize so you don't have the wrong expectation.