Johannes Schmidt-Hieber

Artificial Intelligence · 2013–2020

A researcher in the Science corpus: 5 papers; trusts 132 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #4,457 of 35,345 by trust here, #12,974 by citations
• Statistics and Probability — 58 citations into it; #1,354 of 12,932 by trust here, #3,102 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Model Architectures — 15 citations into it
• Computational Mechanics — 14 citations into it
• Statistical Learning Theory — 13 citations into it
• Scaling & Learning Theory — 13 citations into it
• Optimization & Training — 12 citations into it

Whom Johannes Schmidt-Hieber trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Dmitry Yarotsky
2. Alexandre B. Tsybakov
3. Alex Krizhevsky
4. Larry A. Wasserman
5. Andrew R. Barron
6. Kaiming He
7. Stergios B. Fotopoulos
8. László Györfi
9. Vera Kurková
10. Geoffrey E. Hinton

Who trusts Johannes Schmidt-Hieber overall

1. Taiji Suzuki
2. Dmitry Yarotsky
3. Nicholas G. Polson
4. Konstantin Eckle
5. Max H. Farrell
6. Ronald A. DeVore
7. Zhuoran Yang
8. Veronika Rocková
9. Ziming Liu
10. Tengyuan Liang

Papers from Johannes Schmidt-Hieber's perspective

Ordered by how strongly the researchers Johannes Schmidt-Hieber trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• On adaptive posterior concentration rates (2013) — 86 citations
• BAYESIAN LINEAR REGRESSION WITH SPARSE PRIORS (2014) — 414 citations
• Nonparametric regression using deep neural networks with ReLU activation function (2017) — 1,061 citations
• The Kolmogorov-Arnold representation theorem revisited (2020) — 302 citations
• A comparison of deep networks with ReLU activation function and linear spline-type methods (2018) — 370 citations