activity
20182023
collaborators

7 papers

cs.LG2023

SensLI: Sensitivity-Based Layer Insertion for Neural Networks

Leonie Kreis, Evelyn Herberg, Frederik Köhne +2

The training of neural networks requires tedious and often manual tuning of the network architecture. We propose a systematic approach to inserting new layers during the training p…

cs.LG2023

Time Regularization in Optimal Time Variable Learning

Evelyn Herberg, Roland Herzog, Frederik Köhne

Recently, optimal time variable learning in deep neural networks (DNNs) was introduced in arXiv:2204.08528. In this manuscript we extend the concept by introducing a regularization…

cs.LG2023

Lecture Notes: Neural Network Architectures

Evelyn Herberg

These lecture notes provide an overview of Neural Network architectures from a mathematical point of view. Especially, Machine Learning with Neural Networks is seen as an optimizat…

math.OC2022

An Optimal Time Variable Learning Framework for Deep Neural Networks

Harbir Antil, Hugo Díaz, Evelyn Herberg

Feature propagation in Deep Neural Networks (DNNs) can be associated to nonlinear discrete dynamical systems. The novelty, in this paper, lies in letting the discretization paramet…

math.OC2021

Variational discretization of one-dimensional elliptic optimal control problems with BV functions based on the mixed formulation

Evelyn Herberg, Michael Hinze

We consider optimal control of an elliptic two-point boundary value problem governed by functions of bounded variation (BV). The cost functional is composed of a tracking term for…

math.OC2020

Variational discretization approach applied to an optimal control problem with bounded measure controls

Evelyn Herberg, Michael Hinze

We consider a parabolic optimal control problem with an initial measure control. The cost functional consists of a tracking term corresponding to the observation of the state at fi…

math.OC2018

Maximal discrete sparsity in parabolic optimal control with measures

Evelyn Herberg, Michael Hinze, Henrik Schumacher

We consider variational discretization of a parabolic optimal control problem governed by space-time measure controls. For the state discretization we use a Petrov-Galerkin method…