13 citations · 35 across the 15 of their papers we have counts for
4 papers · 1 filter
Enabling equation-free modeling via diffusion maps
Tracy Chin, Jacob Ruth, Clayton Sanford +3
Equation-free modeling aims at extracting low-dimensional macroscopic dynamics from complex high-dimensional systems that govern the evolution of microscopic states. This algorithm…
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem
Clayton Sanford, Vaggos Chatziafratis
Given a target function , how large must a neural network be in order to approximate ? Recent works examine this basic question on neural network \textit{expressivity} from t…
Support vector machines and linear regression coincide with very high-dimensional features
Navid Ardeshir, Clayton Sanford, Daniel Hsu
The support vector machine (SVM) and minimum Euclidean norm least squares regression are two fundamentally different approaches to fitting linear models, but they have recently bee…
On the Approximation Power of Two-Layer Networks of Random ReLUs
Daniel Hsu, Clayton Sanford, Rocco A. Servedio +1
This paper considers the following question: how well can depth-two ReLU networks with randomly initialized bottom-level weights represent smooth functions? We give near-matching u…