Haoyang Zeng

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Molecular Biology — 11 citations into it; #9,323 of 17,773 by trust here, #9,727 by citations
• Optimization & Training — 9 citations into it
• Management Science and Operations Research — 8 citations into it
• Generative Models — 5 citations into it; #593 of 1,956 by trust here, #909 by citations

Whom Haoyang Zeng trusts overall

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

1. Alex Krizhevsky
2. Jian Zhou
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Tara N. Sainath
6. Yoshua Bengio
7. Ruslan Salakhutdinov
8. Abdel-rahman Mohamed
9. Alexander Rakhlin
10. Yann LeCun

Who trusts Haoyang Zeng overall

1. Constantinos Daskalakis
2. Kevin Kaichuang Yang
3. Alex Finnegan
4. Yanhui Chen
5. Mufti Mahmud
6. Tanlin Sun
7. Ali Pazokitoroudi
8. Gauthier Gidel
9. Zachary Wu
10. Jun S. Song

Papers from Haoyang Zeng's perspective

Ordered by how strongly the researchers Haoyang Zeng 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. GENERATIVE ADVERSARIAL NETS
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Convolutional neural network architectures for predicting DNA–protein binding (2016) — 477 citations
• Antibody complementarity determining region design using high-capacity machine learning (2019) — 172 citations
• Training GANs with Optimism (2017) — 593 citations