Jörg Kurt Wegner

Computational Theory and Mathematics · 2004–2022

A researcher in the Science corpus: 9 papers; trusts 53 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 91 citations into it; #5,644 of 12,182 by trust here, #1,254 by citations
• Molecular Biology — 55 citations into it; #5,149 of 17,773 by trust here, #7,657 by citations
• Materials Chemistry — 54 citations into it; #4,785 of 11,513 by trust here, #7,472 by citations
• Computational Chemistry & Drug Discovery — 51 citations into it; #94 of 425 by trust here, #136 by citations
• Artificial Intelligence — 44 citations into it; #28,359 of 35,345 by trust here, #32,362 by citations
• Computer Vision and Pattern Recognition — 30 citations into it
• Model Architectures — 24 citations into it
• Representation & Self-Supervised Learning — 23 citations into it

Whom Jörg Kurt Wegner trusts overall

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

1. Geoffrey E. Hinton
2. Vladimir Naumovich Vapni
3. Yoshua Bengio
4. Ron Kohavi
5. George H. John
6. Francis R. Bach
7. Chih-Jen Lin
8. Alex Krizhevsky
9. Evgeny Byvatov
10. Michael I. Jordan

Who trusts Jörg Kurt Wegner overall

1. Nils M. Kriege
2. Fredrik D. Johansson
3. Dougal Maclaurin
4. D. Basak
5. Thomas Unterthiner
6. Andreas Mayr
7. Fangping Wan
8. Xifeng Yan

Papers from Jörg Kurt Wegner's perspective

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

Their papers

• The Blue Obelisk—Interoperability in Chemical Informatics (2006) — 381 citations
• Large-scale comparison of machine learning methods for drug target prediction on ChEMBL (2018) — 442 citations
• Optimal assignment kernels for attributed molecular graphs (2005) — 190 citations
• A geometric deep learning approach to predict binding conformations of bioactive molecules (2021) — 180 citations
• Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks (2022) — 90 citations
• Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets (2013) — 113 citations
• Towards Optimal Descriptor Subset Selection with Support Vector Machines in Classification and Regression (2004) — 39 citations
• Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction (2021) — 10 citations
• Deep Learning as an Opportunity in Virtual Screening (2015) — 183 citations