Christoph Wehmeyer

Molecular Biology · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 9 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 98 citations into it; #2,845 of 17,773 by trust here, #6,609 by citations
• Materials Chemistry — 63 citations into it; #1,885 of 11,513 by trust here, #5,923 by citations
• Atomic and Molecular Physics, and Optics — 33 citations into it
• Statistical and Nonlinear Physics — 19 citations into it; #2,395 of 10,127 by trust here, #3,172 by citations
• Signal Processing — 7 citations into it

Whom Christoph Wehmeyer trusts overall

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

1. Frank No'e
2. Geoffrey E. Hinton
3. Michael R. Shirts
4. Vijay S. Pande
5. Diederik P. Kingma
6. Hao Wu
7. Ignasi Buch
8. Yann LeCun
9. G. de Fabritiis
10. Gregory R. Bowman

Who trusts Christoph Wehmeyer overall

1. Jared D. Willard
2. Frank No'e
3. Xiaowei Jia
4. Carlos Xavier Hernández
5. Steven L. Brunton
6. Brooke E. Husic
7. Shaoming Xu
8. Alexandre Tkatchenko
9. Michael S. Steinbach
10. Hannah K. Wayment-Steele

Papers from Christoph Wehmeyer's perspective

Ordered by how strongly the researchers Christoph Wehmeyer 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. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Going deeper with convolutions

Their papers

• Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics (2017) — 403 citations
• PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models. (2015) — 1,097 citations
• Machine Learning of Coarse-Grained Molecular Dynamics Force Fields (2018) — 484 citations
• Multiensemble Markov models of molecular thermodynamics and kinetics (2016) — 92 citations