Andreas Zell

Artificial Intelligence · 2004–2019

A researcher in the Science corpus: 7 papers; trusts 228 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #15,524 of 35,345 by trust here, #28,481 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #11,428 of 21,575 by trust here, #20,271 by citations
• Computational Theory and Mathematics — 27 citations into it; #8,123 of 12,182 by trust here, #3,339 by citations
• Molecular Biology — 22 citations into it; #7,324 of 17,773 by trust here, #12,008 by citations
• Model Architectures — 13 citations into it
• Computational Chemistry & Drug Discovery — 12 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Spectroscopy — 10 citations into it

Whom Andreas Zell trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Kaiming He
4. Barret Zoph
5. Geoffrey E. Hinton
6. Quoc V. Le
7. Yoshua Bengio
8. Karen Simonyan
9. Ilya Sutskever
10. Yann LeCun

Who trusts Andreas Zell overall

1. Colin White
2. Nils M. Kriege
3. Yash Savani
4. Fredrik D. Johansson
5. Sam Nolen

Papers from Andreas Zell's perspective

Ordered by how strongly the researchers Andreas Zell 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Optimal assignment kernels for attributed molecular graphs (2005) — 190 citations
• Interpreting linear support vector machine models with heat map molecule coloring (2011) — 54 citations
• Large-Scale Learning of Structure-Activity Relationships Using a Linear Support Vector Machine and Problem-Specific Metrics (2011) — 33 citations
• jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints (2011) — 92 citations
• Towards Optimal Descriptor Subset Selection with Support Vector Machines in Classification and Regression (2004) — 39 citations
• Prune and Replace NAS (2019) — 13 citations
• A Free–Wilson‐like Approach to Analyze QSAR Models Based on Graph Decomposition Kernels (2010)