Christian Walter Peter Omlin

Artificial Intelligence · 1996–2021

A researcher in the Science corpus: 5 papers; trusts 95 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it; #15,850 of 35,345 by trust here, #24,417 by citations
• Safety, Robustness & Interpretability — 43 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Model Architectures — 18 citations into it
• Health Informatics — 15 citations into it
• Statistical Learning Theory — 8 citations into it
• Information Systems — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Christian Walter Peter Omlin trusts overall

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

1. Ronald J. Williams
2. C. Lee Giles
3. Sreerupa Das
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Chih-Jen Lin
7. J. Urgen Schmidhuber
8. Sepp Hochreiter
9. Yann LeCun
10. Jeffrey L. Elman

Who trusts Christian Walter Peter Omlin overall

1. J. Urgen Schmidhuber
2. Henrik Jacobsson
3. Hiroshi Tsukimoto
4. Giulia Vilone
5. Anwar Vahed
6. Ismail Abdel Hamid Taha
7. Gail Weiss
8. Narek Abroyan
9. C. Lee Giles
10. Qinglong Wang

Papers from Christian Walter Peter Omlin's perspective

Ordered by how strongly the researchers Christian Walter Peter Omlin 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. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Machine Learning Method for Extracting Symbolic Knowledge from Recurrent Neural Networks (2004) — 17 citations
• Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks (2009) — 66 citations
• Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities (2021) — 699 citations
• The Convolutional Tsetlin Machine (2019) — 104 citations
• Extraction of rules from discrete-time recurrent neural networks (1996)