12 citations · 33 across the 7 of their papers we have counts for
12 papers
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks
Aditya Kumar Akash, Sixu Li, Nicolás García Trillos
Based on the concepts of Wasserstein barycenter (WB) and Gromov-Wasserstein barycenter (GWB), we propose a unified mathematical framework for neural network (NN) model fusion and u…
Rates of Convergence for Regression with the Graph Poly-Laplacian
Nicolás García Trillos, Ryan Murray, Matthew Thorpe
In the (special) smoothing spline problem one considers a variational problem with a quadratic data fidelity penalty and Laplacian regularisation. Higher order regularity can be ob…
Clustering dynamics on graphs: from spectral clustering to mean shift through Fokker-Planck interpolation
Katy Craig, Nicolás García Trillos, Dejan Slepčev
In this work we build a unifying framework to interpolate between density-driven and geometry-based algorithms for data clustering, and specifically, to connect the mean shift algo…
Traditional and accelerated gradient descent for neural architecture search
Nicolas Garcia Trillos, Felix Morales, Javier Morales
In this paper we introduce two algorithms for neural architecture search (NASGD and NASAGD) following the theoretical work by two of the authors [5] which used the geometric struct…
Data-Driven Forward Discretizations for Bayesian Inversion
Daniele Bigoni, Yuming Chen, Nicolas Garcia Trillos +2
This paper suggests a framework for the learning of discretizations of expensive forward models in Bayesian inverse problems. The main idea is to incorporate the parameters governi…
Improved spectral convergence rates for graph Laplacians on epsilon-graphs and k-NN graphs
Jeff Calder, Nicolas Garcia Trillos
In this paper we improve the spectral convergence rates for graph-based approximations of Laplace-Beltrami operators constructed from random data. We utilize regularity of the cont…