Growing axons: greedy learning of neural networks with application to function approximation

28 October 2019

Papers citing "Growing axons: greedy learning of neural networks with application to function approximation"

6 / 6 papers shown

Title
Parameter-varying neural ordinary differential equations with partition-of-unity networks Kookjin Lee N. Trask 22 2 0 01 Oct 2022
Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine Naxian Ni S. Dong 29 20 0 24 Apr 2022
SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs A. A. Ramabathiran P. Ramachandran PINN AI4CE 27 76 0 25 Feb 2021
Partition of unity networks: deep hp-approximation Kookjin Lee N. Trask Ravi G. Patel Mamikon A. Gulian E. Cyr 22 30 0 27 Jan 2021
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 34 29 0 11 Dec 2020
Deep Neural Network Approximation Theory Dennis Elbrächter Dmytro Perekrestenko Philipp Grohs Helmut Bölcskei 14 207 0 08 Jan 2019