Lilian Weng 谈开放科学与社区协作
Looking through those little hidden gem stories in the footnote, you will find it so inspiring that researchers with interests on the same topic are able to wor
翻看脚注里那些隐藏的珍宝故事，你会发现，对同一话题感兴趣的研究者们能够跨越角色与地域携手推进一个领域，这多么令人振奋。这就是开放科学与社区的力量。
查看原帖Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices. https://t.co/PGG4zy3u23 We explore a fundamental understanding of the geometry of neural network…
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Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices. https://t.co/PGG4zy3u23 We explore a fundamental understanding of the geometry of neural network optimization.
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Looking through those little hidden gem stories in the footnote, you will find it so inspiring that researchers with interests on the same topic are able to work together to advance a field despite their roles and locations. This is the power of open science and community.