292 citations · 770 across the 46 of their papers we have counts for
5 papers · 1 filter
Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling
Niklas Dexheimer, Johannes Schmidt-Hieber
Forward gradient descent (FGD) has been proposed as a biologically more plausible alternative of gradient descent as it can be computed without backward pass. Considering the linea…
On the VC dimension of deep group convolutional neural networks
Anna Sepliarskaia, Sophie Langer, Johannes Schmidt-Hieber
We study the generalization capabilities of Group Convolutional Neural Networks (GCNNs) with ReLU activation function by deriving upper and lower bounds for their Vapnik-Chervonenk…
Understanding the Effect of GCN Convolutions in Regression Tasks
Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat +1
Graph Convolutional Networks (GCNs) have become a pivotal method in machine learning for modeling functions over graphs. Despite their widespread success across various application…
Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu
This paper proposes an asymptotic theory for online inference of the stochastic gradient descent (SGD) iterates with dropout regularization in linear regression. Specifically, we e…
Generative Modelling via Quantile Regression
Johannes Schmidt-Hieber, Petr Zamolodtchikov
We link conditional generative modelling to quantile regression. We propose a suitable loss function and derive minimax convergence rates for the associated risk under smoothness a…