Osman Malik - Fast Randomized Matrix and Tensor Interpolative Decomposition Using CountSketch
In this talk I will present our recently developed fast randomized algorithm for matrix interpolative decomposition. If time permits, I will also say a few words about how our method can be applied to the tensor interpolative decomposition problem.
Our preprint paper is available at https://arxiv.org/abs/1901.10559
Ann-Casey Hughes - Discussion of paper “Learning the Grammar of Dance” by Joshua M. Stuart and Elizabeth Bradley, Dept Computer Science at CU
A common task required of a dancer or athlete is to move from one prescribed body posture to another in a manner that is consistent with a specific style. One can automate this task, for the purpose of computer animations, using simple machine-learning and search techniques. In particular, we find kinesiologically and stylistically consistent interpolation sequences between pairs of body postures
using graph-theoretic methods to learn the “grammar” of joint movement in a given corpus and then applying memory-bounded A*search to the resulting transition graphs—using an influence diagram that captures the topology of the human body in order to reduce the search space.
As a fairly right-brained person in a STEM world, I find it beautiful the way that art can intersect with mathematics. I grew up as a dancer, and I am intrigued with this paper and the idea that machine learning, something that most would consider to be such a rigid field, can be used to create what I consider to be the purest form of art.