Jiang Wang

Materials Chemistry · with Clementi · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 101 citations into it
• Molecular Biology — 90 citations into it
• Computational Theory and Mathematics — 36 citations into it
• Computational Chemistry & Drug Discovery — 30 citations into it
• Statistical and Nonlinear Physics — 14 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Jiang Wang trusts overall

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

1. Leo Breiman
2. W. Noid
3. K. Müller
4. Frank No'e
5. Richard Maclin
6. Kristof T. Schütt
7. Siewert-Jan Marrink
8. Vladimir Naumovich Vapnik
9. Diederik P. Kingma
10. Florian Müller-Plathe

Who trusts Jiang Wang overall

1. Sam Greydanus
2. Stefan Klus
3. Miles Cranmer
4. Brooke E. Husic
5. Misko Dzamba
6. Frank No'e

Papers from Jiang Wang's perspective

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

• Machine Learning of Coarse-Grained Molecular Dynamics Force Fields (2018) — 484 citations
• Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach. (2020) — 49 citations
• Coarse graining molecular dynamics with graph neural networks. (2020) — 219 citations