1 code implementation • 21 Feb 2018 • Emilie Dufresne, Parker B. Edwards, Heather A. Harrington, Jonathan D. Hauenstein
Topological data analysis (TDA) provides a growing body of tools for computing geometric and topological information about spaces from a finite sample of points.
Algebraic Topology Algebraic Geometry Numerical Analysis
no code implementations • 17 Oct 2018 • Dhagash Mehta, Tianran Chen, Tingting Tang, Jonathan D. Hauenstein
By using the viewpoint of modern computational algebraic geometry, we explore properties of the optimization landscapes of the deep linear neural network models.
no code implementations • 24 Jun 2020 • Edgar A. Bernal, Jonathan D. Hauenstein, Dhagash Mehta, Margaret H. Regan, Tingting Tang
This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions.
1 code implementation • 1 Feb 2022 • Emma R. Cobian, Jonathan D. Hauenstein, Fang Liu, Daniele E. Schiavazzi
We demonstrate the computational efficiency of the AdaAnn scheduler for variational inference with normalizing flows on a number of examples, including density approximation and parameter estimation for dynamical systems.
1 code implementation • 10 Jul 2023 • Yu Wang, Emma R. Cobian, Jubilee Lee, Fang Liu, Jonathan D. Hauenstein, Daniele E. Schiavazzi
Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions.
1 code implementation • 7 Aug 2023 • Sophia J. Abraham, Kehelwala D. G. Maduranga, Jeffery Kinnison, Zachariah Carmichael, Jonathan D. Hauenstein, Walter J. Scheirer
Traditional methods, like grid search and Bayesian optimization, often struggle to quickly adapt and efficiently search the loss landscape.