Christian Tyrchan

Computational Theory and Mathematics · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 14 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 46 citations into it; #1,952 of 12,182 by trust here, #574 by citations
• Artificial Intelligence — 36 citations into it
• Computational Chemistry & Drug Discovery — 35 citations into it; #122 of 425 by trust here, #132 by citations
• Molecular Biology — 32 citations into it; #2,000 of 17,773 by trust here, #3,803 by citations
• Generative Models — 14 citations into it; #547 of 1,956 by trust here, #491 by citations
• Model Architectures — 10 citations into it
• Optimization & Training — 7 citations into it
• Spectroscopy — 6 citations into it

Whom Christian Tyrchan trusts overall

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

1. Alex Graves
2. Sepp Hochreiter
3. J. Urgen Schmidhuber
4. Diederik P. Kingma
5. John B. O. Mitchell
6. Geoffrey E. Hinton
7. Ronald J. Williams
8. Tianqi Chen
9. David Weininger
10. Richard S. Sutton

Who trusts Christian Tyrchan overall

1. Sergey I. Nikolenko
2. Jiaxuan You
3. Rex Ying
4. Kristina Preuer
5. Anvita Gupta
6. Marwin H. S. Segler
7. Martin Simonovsky
8. Yibo Li
9. Kenneth Atz

Papers from Christian Tyrchan's perspective

Ordered by how strongly the researchers Christian Tyrchan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
8. Learning Multiple Layers of Features from Tiny Images
9. Generating Sequences With Recurrent Neural Networks
10. ImageNet: A large-scale hierarchical image database

Their papers

• Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017) — 1,386 citations
• Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017) — 98 citations
• Randomized SMILES strings improve the quality of molecular generative models (2019) — 340 citations