AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 41 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
It is found that networks with low initial weight variance converge to solutions in which different subtasks rely on largely overlapping weight subspaces, whereas high-variance networks implement subtasks in higher-dimensional, more segregated weight subspaces.
Spectral submanifolds can be used to reduce recurrent neural networks to low-dimensional models, revealing their core dynamics, and authors uncover robust structures underlying decision-making and working-memory tasks, providing predictions about the underlying behavior of neural computations.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.