Efficient and Robust Learning on Non-Rigid Surfaces and Graphs

In this talk, I will describe several approaches for learning on curved surfaces undergoing non-rigid deformations. First, I will give a brief overview of intrinsic convolution methods, and then present a different approach based on learned diffusion. The key properties of this approach are its robustness to changes in discretization and efficiency in enabling long-range communication, both in terms of memory and time. I will then showcase several applications, ranging from RNA surface segmentation to non-rigid shape correspondence, and present a recent extension for learning on graphs.