Jonas Lederer

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it
• Graph Learning — 17 citations into it
• Safety, Robustness & Interpretability — 17 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Materials Chemistry — 14 citations into it

Whom Jonas Lederer trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Grgoire Montavon
4. Yann LeCun
5. Karen Simonyan
6. Yoshua Bengio
7. Alex Krizhevsky
8. Kristof T. Schütt
9. Ilya Sutskever
10. Jimmy Ba

Papers from Jonas Lederer's perspective

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

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

Their papers

• Higher-Order Explanations of Graph Neural Networks via Relevant Walks (2020) — 310 citations