Thinking Machines 发布第二篇 Connectionism 研究：Modular Manifolds
Sharing our second Connectionism research post on Modular Manifolds, a mathematical approach to refining training at each layer of the neural network
分享我们第二篇 Connectionism 研究文章，关于 Modular Manifolds——一种在神经网络每一层优化训练的数学方法
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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Sharing our second Connectionism research post on Modular Manifolds, a mathematical approach to refining training at each layer of the neural network