4 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 4 cited
Scaling Riemannian Diffusion Models
Aaron Lou, Minkai Xu, Stefano Ermon
Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds. Unfortunately, the additional geometric com…
stat.ML2023
Riemannian Residual Neural Networks
Isay Katsman, Eric Ming Chen, Sidhanth Holalkere +4
Recent methods in geometric deep learning have introduced various neural networks to operate over data that lie on Riemannian manifolds. Such networks are often necessary to learn…
stat.ML2023★ 3 cited
Reflected Diffusion Models
Aaron Lou, Stefano Ermon
Score-based diffusion models learn to reverse a stochastic differential equation that maps data to noise. However, for complex tasks, numerical error can compound and result in hig…