5 citations · 14 across the 6 of their papers we have counts for
6 papers
How to reveal the rank of a matrix?
Anil Damle, Silke Glas, Alex Townsend +1
We study algorithms called rank-revealers that reveal a matrix's rank structure. Such algorithms form a fundamental component in matrix compression, singular value estimation, and…
Leveraging the Hankel norm approximation and block-AAA algorithms in reduced order modeling
Annan Yu, Alex Townsend
Large-scale linear, time-invariant (LTI) dynamical systems are widely used to characterize complicated physical phenomena. We propose a two-stage algorithm to reduce the order of a…
Tuning Frequency Bias in Neural Network Training with Nonuniform Data
Annan Yu, Yunan Yang, Alex Townsend
Small generalization errors of over-parameterized neural networks (NNs) can be partially explained by the frequency biasing phenomenon, where gradient-based algorithms minimize the…
On the stability of unevenly spaced samples for interpolation and quadrature
Annan Yu, Alex Townsend
Unevenly spaced samples from a periodic function are common in signal processing and can often be viewed as a perturbed equally spaced grid. In this paper, we analyze how the uneve…
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks
Annan Yu, Chloé Becquey, Diana Halikias +2
The standard Universal Approximation Theorem for operator neural networks (NNs) holds for arbitrary width and bounded depth. Here, we prove that operator NNs of bounded width and a…
H-chromatic symmetric functions
Nancy Mae Eagles, Angèle M. Foley, Alice Huang +2
We introduce -chromatic symmetric functions, , which use the -coloring of a graph to define a generalization of Stanley's chromatic symmetric functions. We say…