1.9k citations · 6.2k across the 179 of their papers we have counts for
17 papers · 1 filter
A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decay
Yuqing Wang, Ioannis G. Kevrekidis, Mikhail Belkin
Understanding generalization remains a central challenge in machine learning because it requires jointly considering data, architecture, and training dynamics. In this paper, we de…
Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling
Nikolaos Evangelou, Tianqi Cui, Juan M. Bello-Rivas +2
We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We…
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points
Gianluca Fabiani, Nikolaos Evangelou, Tianqi Cui +4
We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipp…
On-Manifold Projected Gradient Descent
Aaron Mahler, Tyrus Berry, Tom Stephens +4
This work provides a computable, direct, and mathematically rigorous approximation to the differential geometry of class manifolds for high-dimensional data, along with nonlinear p…
Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information
Saurabh Malani, Tom S. Bertalan, Tianqi Cui +3
Experimental data is often comprised of variables measured independently, at different sampling rates (non-uniform t between successive measurements); and at a specific time poi…
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning
Hector Vargas Alvarez, Gianluca Fabiani, Nikolaos Kazantzis +2
We present a physics-informed machine learning (PIML) scheme for the feedback linearization of nonlinear discrete-time dynamical systems. The PIML finds the nonlinear transformatio…