5 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 2 cited
How does training shape the Riemannian geometry of neural network representations?
Jacob A. Zavatone-Veth, Sheng Yang, Julian A. Rubinfien +1
In machine learning, there is a long history of trying to build neural networks that can learn from fewer example data by baking in strong geometric priors. However, it is not alwa…
cs.LG2022★ 5 cited
The Numerical Stability of Hyperbolic Representation Learning
Gal Mishne, Zhengchao Wan, Yusu Wang +1
Given the exponential growth of the volume of the ball w.r.t. its radius, the hyperbolic space is capable of embedding trees with arbitrarily small distortion and hence has receive…