Johannes Schmidt-Hieber

According to our database, Johannes Schmidt-Hieber authored at least 16 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Dropout Regularization Versus l2-Penalization in the Linear Model.

Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber

J. Mach. Learn. Res., 2024

Understanding the Effect of GCN Convolutions in Regression Tasks.

Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat, Olga Klopp

CoRR, 2024

On the VC dimension of deep group convolutional neural networks.

Anna Sepliarskaia, Sophie Langer, Johannes Schmidt-Hieber

CoRR, 2024

Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models.

Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu

CoRR, 2024

2023

On Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization.

Masaaki Imaizumi, Johannes Schmidt-Hieber

IEEE Trans. Inf. Theory, February, 2023

Posterior Contraction for Deep Gaussian Process Priors.

Gianluca Finocchio, Johannes Schmidt-Hieber

J. Mach. Learn. Res., 2023

Hebbian learning inspired estimation of the linear regression parameters from queries.

Johannes Schmidt-Hieber, Wouter M. Koolen

CoRR, 2023

Convergence guarantees for forward gradient descent in the linear regression model.

Thijs Bos, Johannes Schmidt-Hieber

CoRR, 2023

Codivergences and information matrices.

Alexis Derumigny, Johannes Schmidt-Hieber

CoRR, 2023

Interpreting learning in biological neural networks as zero-order optimization method.

Johannes Schmidt-Hieber

CoRR, 2023

2022

On the inability of Gaussian process regression to optimally learn compositional functions.

Matteo Giordano, Kolyan Ray, Johannes Schmidt-Hieber

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

The Kolmogorov-Arnold representation theorem revisited.

Johannes Schmidt-Hieber

Neural Networks, 2021

Convergence rates of deep ReLU networks for multiclass classification.

Thijs Bos, Johannes Schmidt-Hieber

CoRR, 2021

2019

A comparison of deep networks with ReLU activation function and linear spline-type methods.

Konstantin Eckle, Johannes Schmidt-Hieber

Neural Networks, 2019

Deep ReLU network approximation of functions on a manifold.

Johannes Schmidt-Hieber

CoRR, 2019

2017

Nonparametric regression using deep neural networks with ReLU activation function.

Johannes Schmidt-Hieber

CoRR, 2017