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ORCIDJohannes Schmidt-Hieber
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- affiliation: University of Twente, The Netherlands
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2020 – today
- 2024
[j5]Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber:
Dropout Regularization Versus l2-Penalization in the Linear Model. J. Mach. Learn. Res. 25: 204:1-204:48 (2024)- [i12]Jiaqi Li, Johannes Schmidt-Hieber, Wei Biao Wu:
Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models. CoRR abs/2409.07434 (2024) - 2023
- [j4]Gianluca Finocchio, Johannes Schmidt-Hieber:
Posterior Contraction for Deep Gaussian Process Priors. J. Mach. Learn. Res. 24: 66:1-66:49 (2023) - [j3]Masaaki Imaizumi
, Johannes Schmidt-Hieber:
On Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization. IEEE Trans. Inf. Theory 69(2): 1203-1223 (2023) - [i11]Johannes Schmidt-Hieber:
Interpreting learning in biological neural networks as zero-order optimization method. CoRR abs/2301.11777 (2023) - [i10]Alexis Derumigny, Johannes Schmidt-Hieber:
Codivergences and information matrices. CoRR abs/2303.08122 (2023) - [i9]Thijs Bos, Johannes Schmidt-Hieber:
Convergence guarantees for forward gradient descent in the linear regression model. CoRR abs/2309.15001 (2023) - [i8]Johannes Schmidt-Hieber, Wouter M. Koolen:
Hebbian learning inspired estimation of the linear regression parameters from queries. CoRR abs/2311.03483 (2023) - 2022
- [c1]Matteo Giordano, Kolyan Ray, Johannes Schmidt-Hieber:
On the inability of Gaussian process regression to optimally learn compositional functions. NeurIPS 2022 - [i7]Masaaki Imaizumi, Johannes Schmidt-Hieber:
On generalization bounds for deep networks based on loss surface implicit regularization. CoRR abs/2201.04545 (2022) - [i6]Matteo Giordano, Kolyan Ray, Johannes Schmidt-Hieber:
On the inability of Gaussian process regression to optimally learn compositional functions. CoRR abs/2205.07764 (2022) - 2021
- [j2]Johannes Schmidt-Hieber:
The Kolmogorov-Arnold representation theorem revisited. Neural Networks 137: 119-126 (2021) - [i5]Thijs Bos, Johannes Schmidt-Hieber:
Convergence rates of deep ReLU networks for multiclass classification. CoRR abs/2108.00969 (2021) - 2020
- [i4]Johannes Schmidt-Hieber:
The Kolmogorov-Arnold representation theorem revisited. CoRR abs/2007.15884 (2020)
2010 – 2019
- 2019
- [j1]Konstantin Eckle, Johannes Schmidt-Hieber:
A comparison of deep networks with ReLU activation function and linear spline-type methods. Neural Networks 110: 232-242 (2019) - [i3]Johannes Schmidt-Hieber:
Deep ReLU network approximation of functions on a manifold. CoRR abs/1908.00695 (2019) - 2018
- [i2]Konstantin Eckle, Johannes Schmidt-Hieber:
A comparison of deep networks with ReLU activation function and linear spline-type methods. CoRR abs/1804.02253 (2018) - 2017
- [i1]Johannes Schmidt-Hieber:
Nonparametric regression using deep neural networks with ReLU activation function. CoRR abs/1708.06633 (2017)
Coauthor Index
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