Jian Zhou

Molecular Biology · with Troyanskaya · 2014–2018

A researcher in the Science corpus: 4 papers; trusts 36 researchers and is trusted by 79 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 61 citations into it; #424 of 17,773 by trust here, #4,273 by citations
• Computational Biology & Bioinformatics — 11 citations into it; #95 of 660 by trust here, #290 by citations
• Artificial Intelligence — 7 citations into it
• Statistics and Probability — 5 citations into it

Whom Jian Zhou trusts overall

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

1. Yoshua Bengio
2. Mona Singh
3. Burkhard Rost
4. Mark W. Schmidt
5. Geoffrey E. Hinton
6. W. Kabsch
7. Holger Hofling
8. Ruslan Salakhutdinov
9. Robert Tibshirani
10. Pradeep Ravikumar

Who trusts Jian Zhou overall

1. Daniel Quang
2. Saket Navlakha
3. Xiaoyong Pan
4. Akosua Busia
5. Jie Jessie Ren
6. Xiaohui S. Xie
7. Avanti Shrikumar
8. Chensi Cao
9. Z. Li
10. David R. Kelley

Papers from Jian Zhou's perspective

Ordered by how strongly the researchers Jian Zhou trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (2014) — 157 citations
• Predicting effects of noncoding variants with deep learning–based sequence model (2015) — 2,196 citations
• Global Quantitative Modeling of Chromatin Factor Interactions (2014) — 28 citations
• Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (2018) — 562 citations