1 citations · 2 across the 6 of their papers we have counts for
6 papers
Approximation and Gradient Descent Training with Neural Networks
G. Welper
It is well understood that neural networks with carefully hand-picked weights provide powerful function approximation and that they can be successfully trained in over-parametrized…
Approximation Results for Gradient Descent trained Neural Networks
G. Welper
The paper contains approximation guarantees for neural networks that are trained with gradient flow, with error measured in the continuous -norm on the -d…
Approximation results for Gradient Descent trained Shallow Neural Networks in
R. Gentile, G. Welper
Two aspects of neural networks that have been extensively studied in the recent literature are their function approximation properties and their training by gradient descent method…
Non-Convex Compressed Sensing with Training Data
G. Welper
Efficient algorithms for the sparse solution of under-determined linear systems are known for matrices satisfying suitable assumptions like the restricted isometry pro…
Universality of Gradient Descent Neural Network Training
G. Welper
It has been observed that design choices of neural networks are often crucial for their successful optimization. In this article, we therefore discuss the question if it is always…
A Relaxation Argument for Optimization in Neural Networks and Non-Convex Compressed Sensing
G. Welper
It has been observed in practical applications and in theoretical analysis that over-parametrization helps to find good minima in neural network training. Similarly, in this articl…