Hi everyone,
Just a friendly reminder that our first theory seminar of the year is tomorrow,
2-3pm in CS 3310. Hope to see many of you there!
I will be presenting on âA Near Linear Time Algorithm for the Chamfer Distance.â
Abstract: I will describe a fast algorithm to approximate the Chamfer distance, a commonly used proxy for the more computationally demanding Earth-Mover (Optimal Transport) Distance. For
any two point sets A and B of size n, the Chamfer distance from A to B is defined as CH(A, B) = sum over x in A of d(x, B), where d(x, B) is the minimum distance from x to any point in B. In other words, it is the sum of nearest neighbor distances from A to
B. The Chamfer distance is a popular measure of dissimilarity between point clouds, used in many machine learning, computer vision, and graphics applications, and admits a straightforward O(n^2)-time brute force algorithm. However, the quadratic dependence
on n in the running time makes the naive approach intractable for large datasets. I will present a (1+epsilon)-approximate algorithm for estimating the Chamfer distance with a near-linear running time. Our experiments demonstrate that it is both accurate and
fast on large high-dimensional datasets.
Please let me know if you are interested in giving a talk at some point in the semester (students are welcome as well!).
Jerry also asked me to pass along another talk that maybe of interest to the group.
"In the next ML lunch meeting (Sep 20th),
Professor Jordan Ellenberg (MATH)
will tell us about his research on Machine Learning for Mathematics. See you all Friday
at 12:30pm in CS 1221!
Title: What does machine
learning have to offer mathematics?
Abstract: Iâll talk a little bit on what Iâve learned, drawn from my own work and others, about the current state of affairs in
the use of machine learning techniques to advance progress in pure mathematics. I consider this a very interesting test realm for ideas about humans and machines can collaborate and I will try to convince you to feel the same!"
Best,
Sandeep
|