Yang Shen

Molecular Biology · Neural Information Processing Systems · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 71 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 163 citations into it
• Artificial Intelligence — 86 citations into it
• Computational Biology & Bioinformatics — 59 citations into it
• Materials Chemistry — 51 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Computational Theory and Mathematics — 40 citations into it
• Representation & Self-Supervised Learning — 37 citations into it
• Graph Learning — 31 citations into it

Whom Yang Shen trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kaiming He
7. Max Welling
8. Yann LeCun
9. Weihua Hu
10. Thomas Kipf

Papers from Yang Shen's perspective

Ordered by how strongly the researchers Yang Shen 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks (2020) — 80 citations
• Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019) — 104 citations
• DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks (2018) — 441 citations
• Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas (2018) — 959 citations
• De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN) (2019) — 11 citations
• Graph Contrastive Learning Automated (2021) — 632 citations
• Graph Contrastive Learning with Augmentations (2020) — 2,886 citations