Marco Schäfer

Molecular Biology · 2021

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 21 citations into it
• Computational Biology & Bioinformatics — 16 citations into it
• Artificial Intelligence — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Graph Learning — 6 citations into it

Whom Marco Schäfer trusts overall

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

1. Maxat Kulmanov
2. Alex Krizhevsky
3. Gregory R. Koch
4. Ulrike von Luxburg
5. S. Parasuraman
6. Kaiming He
7. Geoffrey E. Hinton
8. Thomas Kipf
9. Ilya Sutskever
10. Yoshua Bengio

Papers from Marco Schäfer's perspective

Ordered by how strongly the researchers Marco Schäfer 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures (2021) — 124 citations