Charlotte Bunne

Molecular Biology · 2021–2022

A researcher in the Science corpus: 3 papers; trusts 33 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 67 citations into it
• Materials Chemistry — 35 citations into it
• Computational Theory and Mathematics — 28 citations into it
• Computational Biology & Bioinformatics — 22 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Artificial Intelligence — 20 citations into it
• Graph Learning — 11 citations into it
• Model Architectures — 10 citations into it

Whom Charlotte Bunne trusts overall

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

1. Diederik P. Kingma
2. Marco Cuturi
3. Leland McInnes
4. Max Welling
5. Geoffrey E. Hinton
6. Jimmy Ba
7. Yoshua Bengio
8. Alex Krizhevsky
9. Raphael J. L. Townshend
10. Yann LeCun

Who trusts Charlotte Bunne overall

1. Jiaqi Guan
2. Hannes Stärk
3. Zuobai Zhang

Papers from Charlotte Bunne's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Multi-Scale Representation Learning on Proteins (2022) — 122 citations
• Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021) — 174 citations
• Learning single-cell perturbation responses using neural optimal transport (2021) — 309 citations