Daniel Ruckert Technical University of Munich

Computer Vision and Pattern Recognition · 2021

A researcher in the Science corpus: 1 paper; trusts 205 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 64 citations into it
• Artificial Intelligence — 56 citations into it
• Representation & Self-Supervised Learning — 34 citations into it

Whom Daniel Ruckert Technical University of Munich trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Ross B. Girshick
7. Yann LeCun
8. Ilya Sutskever
9. Karen Simonyan
10. Jimmy Ba

Papers from Daniel Ruckert Technical University of Munich's perspective

Ordered by how strongly the researchers Daniel Ruckert Technical University of Munich 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet Large Scale Visual Recognition Challenge
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Going deeper with convolutions

Their papers

• Joint Learning of Localized Representations from Medical Images and Reports (2021) — 135 citations