Flatiron Internal Conference: Flatiron-wide Algorithms and Mathematics

Session

Dimension Reduction and Factorization Short Talk

Nov 1, 2019, 9:15 AM

2nd Floor (Ingrid Daubechies Auditorium)

26. Randomized linear algebra and matrix approximation

Eftychios Pnevmatikakis (CCM)

11/1/19, 9:15 AM

The goal of this talk is to show how probabilistic methods can be used to accelerate standard matrix factorizations (e.g. SVD) with provable characteristics in terms of speed and accuracy.

27. Spectral Clustering and Dimensionality Reduction

Marina Spivak (CCM)

11/1/19, 9:40 AM

28. Clustering in low dimensions

Jeremy Magland (CCM)

11/1/19, 10:05 AM

I will focus on clustering data points in low dimensions (mostly 2d) and provide an overview of some popular clustering algorithms.

The accompanying live notebook is linked from my homepage: https://users.flatironinstitute.org/~magland

29. The why and how of nonnegative matrix factorization

Johannes Friedrich (CCB)

11/1/19, 11:35 AM

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of high-dimensional data as it automatically extracts sparse and meaningful features from a set of nonnegative data vectors. I first illustrate this property of NMF on some applications. Then I address the problem of solving NMF, which is NP-hard in general, and review some standard NMF algorithms. Finally, I...

30. Introduction to Tensor Network Methods

Katharine Hyatt (CCQ)

11/1/19, 12:00 PM

Tensor network methods are a family of variational algorithms used to simulate many body quantum systems in a variety of situations. With some brief motivation from physics, I'll explain why anyone would want to use these methods, why it is that they are so effective for certain classes of problems, and some extensions to other fields like machine learning.