3 papers
cs.LG2026
Flatland: The Adventures of Gradient Descent with Large Step Sizes
Leonardo Galli, Curtis Fox, Wiebke Bartolomaeus +2
The training of neural networks often entails objective functions that are not globally -smooth. For these functions, it is both theoretically and practically difficult to reply…
cs.LG2026
The Riemannian Geometry Associated to Gradient Flows of Linear Convolutional Networks
El Mehdi Achour, Kathlén Kohn, Holger Rauhut
We study geometric properties of the gradient flow for learning deep linear convolutional networks. For linear fully connected networks, it has been shown recently that the corresp…
math.OC2026
Convergence of gradient flow for learning convolutional neural networks
Jona-Maria Diederen, Holger Rauhut, Ulrich Terstiege
Convolutional neural networks are widely used in imaging and image recognition. Learning such networks from training data leads to the minimization of a non-convex function. This m…