Andreas Mardt

Molecular Biology · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 31 citations into it; #5,289 of 17,773 by trust here, #8,791 by citations
• Artificial Intelligence — 20 citations into it
• Statistical and Nonlinear Physics — 19 citations into it; #3,658 of 10,127 by trust here, #2,370 by citations
• Materials Chemistry — 15 citations into it; #3,098 of 11,513 by trust here, #7,093 by citations
• Spectroscopy — 12 citations into it
• Atomic and Molecular Physics, and Optics — 8 citations into it
• Computational Mechanics — 8 citations into it
• Model Architectures — 8 citations into it

Whom Andreas Mardt trusts overall

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

1. Frank No'e
2. Igor Mezi'c
3. Hao Wu
4. Gregory R. Bowman
5. Diederik P. Kingma
6. Geoffrey E. Hinton
7. Vinod Nair
8. Christof Schutte
9. Vijay S. Pande
10. Marc'Aurelio Ranzato

Who trusts Andreas Mardt overall

1. Frank No'e
2. Hao Wu
3. Jared D. Willard
4. Steven L. Brunton
5. Naoya Takeishi
6. Seyed Mohamad Moosavi
7. Kathleen P. Champion
8. Xiaowei Jia
9. Alexandre Tkatchenko
10. Bethany Lusch

Papers from Andreas Mardt's perspective

Ordered by how strongly the researchers Andreas Mardt 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. Gradient-based learning applied to document recognition
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Rectified Linear Units Improve Restricted Boltzmann Machines

Their papers

• VAMPnets for deep learning of molecular kinetics (2017) — 682 citations
• Deep Generative Markov State Models (2018) — 68 citations