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The keyword is "high impact". The link you provided is only ranked by publication count, it doesn't consider the impact factor of journal/conferences. If you lo
by BiasRegularizer 7y ago
The keyword is "high impact". The link you provided is only ranked by publication count, it doesn't consider the impact factor of journal/conferences. If you look at stats from ICML this year[0], the only Chinese institution TsingHua is ranked #13 in the top #20 list.
Take a look at recent major advances in deep learning: information bottle neck, mask-rcnn, transformer, normalizing flow, all originated from US/Canadian/EU institutions. Chinese academia has very serious systematic issues that discourages innovation and encourages quantity over quality.
https://medium.com/@dcharrezt/icml-2019-stats-4ba18fbc6543 https://medium.com/@dcharrezt/icml-2019-stats-4ba18fbc6543
- govg 7y agoSure, but at the same time there is a real bias towards established methods and research groups. It's hard to find traction as a newly established group doing original research - and "impact factors" are highly overrated since what ends up happening is the paper with most publicity and flash surrounding it is deemed more impactful. The fact that they went from almost zero representation at the international venues to such a high number should be indication that they are on the rise. In addition, I think the sheer number of students and resources at their disposal will ensure China have a continuous pool of talent to draw from. The US has become increasingly hostile and competitive for ML researchers in terms of academic pressure, and returning to China has been incentivized for a while now.
- Linq123 7y ago>Chinese academia has very serious systematic issues that discourages innovation and encourages quantity over quality. Can you elaborate on that?