17 citations · 58 across the 11 of their papers we have counts for
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cs.LG2022
Identifying latent distances with Finslerian geometry
Alison Pouplin, David Eklund, Carl Henrik Ek +1
Riemannian geometry provides us with powerful tools to explore the latent space of generative models while preserving the underlying structure of the data. The latent space can be…
math.AG2022★ 1 cited
Computing geometric feature sizes for algebraic manifolds
Sandra Di Rocco, Parker B. Edwards, David Eklund +2
We introduce numerical algebraic geometry methods for computing lower bounds on the reach, local feature size, and the weak feature size of the real part of an equidimensional and…