26 citations · 38 across the 8 of their papers we have counts for
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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…
cs.CV2023★ 4 cited
Denoising Diffusion Bridge Models
Linqi Zhou, Aaron Lou, Samar Khanna +1
Diffusion models are powerful generative models that map noise to data using stochastic processes. However, for many applications such as image editing, the model input comes from…
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…