Stefan Woerner

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 60 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it; #12,663 of 35,345 by trust here, #17,412 by citations
• Computational Theory and Mathematics — 44 citations into it
• Atomic and Molecular Physics, and Optics — 39 citations into it; #1,611 of 7,135 by trust here, #5,314 by citations
• Generative Models — 12 citations into it
• Optimization & Training — 12 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Finance — 9 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Stefan Woerner trusts overall

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

1. Yazhen Wang
2. Iroon Polytechniou
3. Ashley Montanaro
4. Paul Glasserman
5. Lov K. Grover
6. Roman Orus
7. Gilles Brassard
8. Alexander S. Green
9. Terry Rudolph
10. Diederik P. Kingma

Who trusts Stefan Woerner overall

1. Daniel J. Egger
2. Christa Zoufal
3. Roman Orus
4. Nikitas Stamatopoulos
5. Aurélien Lucchi
6. Samuel Mugel
7. Jakub Marecek
8. Claudio Gambella
9. Enrique Lizaso
10. Yue Sun

Papers from Stefan Woerner's perspective

Ordered by how strongly the researchers Stefan Woerner 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The power of quantum neural networks (2020) — 1,167 citations
• Quantum Generative Adversarial Networks for learning and loading random distributions (2019) — 616 citations
• Quantum Computing for Finance: State-of-the-Art and Future Prospects (2020) — 329 citations
• Warm-starting quantum optimization (2020) — 384 citations
• Option Pricing using Quantum Computers (2019) — 328 citations
• Quantum risk analysis (2018) — 351 citations