Ralf Krestel

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 7 papers; trusts 235 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #17,550 of 35,345 by trust here, #18,186 by citations
• Natural Language Processing — 49 citations into it; #2,641 of 5,688 by trust here, #3,826 by citations
• Safety, Robustness & Interpretability — 44 citations into it; #1,161 of 2,251 by trust here, #1,673 by citations
• Information Systems — 39 citations into it; #6,677 of 8,771 by trust here, #5,942 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #17,600 of 21,575 by trust here, #16,367 by citations
• Communication — 15 citations into it
• Model Architectures — 13 citations into it
• AI Ethics, Fairness & Society — 12 citations into it

Whom Ralf Krestel trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Sepp Hochreiter
8. J. Urgen Schmidhuber
9. Diederik P. Kingma
10. Karen Simonyan

Who trusts Ralf Krestel overall

1. Hang Li
2. Hongzhi Yin
3. Guandong Xu
4. Jun Xu
5. Yangjun Zhang
6. Sophie Ahrens
7. Hayden S. Helm
8. Betty van Aken
9. Svetlana Kiritchenko
10. Bin Cui

Papers from Ralf Krestel's perspective

Ordered by how strongly the researchers Ralf Krestel 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018) — 250 citations
• Dynamic Channel and Layer Gating in Convolutional Neural Networks (2020) — 11 citations
• Offensive Language Detection Explained (2020) — 39 citations
• Explainable AI under contract and tort law: legal incentives and technical challenges (2020) — 125 citations
• Domain-specific word embeddings for patent classification (2019)
• Aggression Identification Using Deep Learning and Data Augmentation (2018) — 71 citations
• Latent dirichlet allocation for tag recommendation (2009) — 520 citations