29 citations · 225 across the 32 of their papers we have counts for
5 papers · 1 filter
Properly-weighted graph Laplacian for semi-supervised learning
Jeff Calder, Dejan Slepcev
The performance of traditional graph Laplacian methods for semi-supervised learning degrades substantially as the ratio of labeled to unlabeled data decreases, due to a degeneracy…
PDE Acceleration: A convergence rate analysis and applications to obstacle problems
Jeff Calder, Anthony Yezzi
This paper provides a rigorous convergence rate and complexity analysis for a recently introduced framework, called PDE acceleration, for solving problems in the calculus of variat…
Accelerated PDE's for efficient solution of regularized inversion problems
Minas Benyamin, Jeff Calder, Ganesh Sundaramoorthi +1
We further develop a new framework, called PDE Acceleration, by applying it to calculus of variations problems defined for general functions on , obtaining efficient…
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust
Chris Finlay, Jeff Calder, Bilal Abbasi +1
In this work we study input gradient regularization of deep neural networks, and demonstrate that such regularization leads to generalization proofs and improved adversarial robust…
The limit shape of convex hull peeling
Jeff Calder, Charles K Smart
We prove that the convex peeling of a random point set in dimension d approximates motion by the 1/(d + 1) power of Gaussian curvature. We use viscosity solution theory to interpre…