Zheng Wang

Molecular Biology · with Cheng · 2009–2019

A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 54 citations into it; #5,623 of 17,773 by trust here, #7,980 by citations
• Materials Chemistry — 20 citations into it; #3,355 of 11,513 by trust here, #7,310 by citations
• Computational Biology & Bioinformatics — 14 citations into it; #398 of 660 by trust here, #539 by citations
• Artificial Intelligence — 9 citations into it
• Computational Theory and Mathematics — 6 citations into it; #11,023 of 12,182 by trust here, #6,165 by citations

Whom Zheng Wang trusts overall

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

1. Christopher K. I. Williams
2. Yuhai Wu
3. Yang Zhang
4. Thorsten Joachims
5. Hongyi Zhou
6. Jianlin Cheng
7. Pierre Baldi
8. John Moult
9. Jeffrey Skolnick
10. K. Simons

Who trusts Zheng Wang overall

1. Renzhi Cao
2. Badri Adhikari
3. Debswapna Bhattacharya
4. Jianlin Cheng
5. Robin Pearce
6. Mirko Torrisi
7. Andriy Kryshtafovych
8. Sitao Wu
9. Yang Zhang
10. Zhiyong Wang

Papers from Zheng Wang's perspective

Ordered by how strongly the researchers Zheng 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. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Statistical Learning Theory
6. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions.
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Gradient-based learning applied to document recognition
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• NNcon: improved protein contact map prediction using 2D-recursive neural networks (2009) — 164 citations
• Evaluating the absolute quality of a single protein model using structural features and support vector machines (2009) — 103 citations
• Visualizing structure and transitions in high-dimensional biological data (2019) — 1,097 citations