14 citations · 58 across the 16 of their papers we have counts for
20 papers
Higher Order Gauge Equivariant CNNs on Riemannian Manifolds and Applications
Gianfranco Cortes, Yue Yu, Robin Chen +3
With the advent of group equivariant convolutions in deep networks literature, spherical CNNs with -equivariant layers have been developed to cope with data that ar…
Kernel Stein Discrepancy on Lie Groups: Theory and Applications
Xiaoda Qu, Xiran Fan, Baba C. Vemuri
Distributional approximation is a fundamental problem in machine learning with numerous applications across all fields of science and engineering and beyond. The key challenge in m…
Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space
Xiran Fan, Chun-Hao Yang, Baba C. Vemuri
Hyperbolic spaces have been quite popular in the recent past for representing hierarchically organized data. Further, several classification algorithms for data in these spaces hav…
A Framework for Improving the Characterization Scope of Stein's Method on Riemannian Manifolds
Xiaoda Qu, Baba C. Vemuri
Stein's method has been widely used to achieve distributional approximations for probability distributions defined in Euclidean spaces. Recently, techniques to extend Stein's metho…
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design
Xiran Fan, Chun-Hao Yang, Baba C. Vemuri
Hyperbolic neural networks have been popular in the recent past due to their ability to represent hierarchical data sets effectively and efficiently. The challenge in developing th…
VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds
Monami Banerjee, Rudrasis Chakraborty, Jose Bouza +1
Convolutional neural networks have been highly successful in image-based learning tasks due to their translation equivariance property. Recent work has generalized the traditional…