Marco Rudolph

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 31 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Generative Models — 12 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Computer Networks and Communications — 7 citations into it
• Industrial and Manufacturing Engineering — 7 citations into it; #911 of 2,521 by trust here, #1,336 by citations

Whom Marco Rudolph trusts overall

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

1. Diederik P. Kingma
2. Yann LeCun
3. Kaiming He
4. Karen Simonyan
5. Geoffrey E. Hinton
6. Jimmy Ba
7. Alex Krizhevsky
8. Yoshua Bengio
9. Max Welling
10. Andrew Zisserman

Who trusts Marco Rudolph overall

1. Kilian Batzner
2. Denis A. Gudovskiy
3. Yang Zou
4. Chun-Liang Li
5. Karsten Roth
6. Jongheon Jeong
7. Jiawei Yu1
8. Latha Pemula
9. Lars Heckler
10. Shun Ishizaka

Papers from Marco Rudolph's perspective

Ordered by how strongly the researchers Marco Rudolph trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows (2020) — 442 citations
• Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection (2021) — 329 citations