Esben Jannik Bjerrum

Computational Theory and Mathematics · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 48 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 75 citations into it; #401 of 12,182 by trust here, #1,184 by citations
• Materials Chemistry — 53 citations into it; #232 of 11,513 by trust here, #5,658 by citations
• Artificial Intelligence — 47 citations into it
• Computational Chemistry & Drug Discovery — 47 citations into it
• Molecular Biology — 46 citations into it; #488 of 17,773 by trust here, #6,960 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Model Architectures — 16 citations into it
• Natural Language Processing — 11 citations into it

Whom Esben Jannik Bjerrum trusts overall

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

1. J. Urgen Schmidhuber
2. Geoffrey E. Hinton
3. Xiaodong Cui
4. Peter Ertl
5. Sepp Hochreiter
6. Ilya Sutskever
7. Alex Krizhevsky
8. David Weininger
9. Yoshua Bengio
10. Andreas Mayr

Who trusts Esben Jannik Bjerrum overall

1. Garrett B. Goh
2. Peter Ertl
3. Luis Perez
4. Shion Honda
5. Yujia Li
6. Yuanqi Du
7. Laurianne David
8. Charles Siegel
9. Ross Irwin
10. Igor V. Tetko

Papers from Esben Jannik Bjerrum's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules (2017) — 351 citations
• Molecular Generation with Recurrent Neural Networks (RNNs) (2017) — 126 citations
• Chemformer: a pre-trained transformer for computational chemistry (2021) — 468 citations
• A de novo molecular generation method using latent vector based generative adversarial network (2019) — 356 citations
• Randomized SMILES strings improve the quality of molecular generative models (2019) — 340 citations
• AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning (2020) — 333 citations