Muhan Zhang

Artificial Intelligence · with Chen · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 145 researchers and is trusted by 77 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it; #3,103 of 35,345 by trust here, #5,390 by citations
• Graph Learning — 68 citations into it; #195 of 693 by trust here, #243 by citations
• Computer Vision and Pattern Recognition — 55 citations into it; #6,295 of 21,575 by trust here, #6,606 by citations
• Computational Theory and Mathematics — 19 citations into it
• Statistical and Nonlinear Physics — 19 citations into it; #681 of 10,127 by trust here, #1,638 by citations
• Generative Models — 17 citations into it
• Model Architectures — 17 citations into it
• Materials Chemistry — 16 citations into it

Whom Muhan Zhang trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Steffen Rendle
6. Ilya Sutskever
7. Yann LeCun
8. Ronan Collobert
9. Max Welling
10. Thomas Kipf

Who trusts Muhan Zhang overall

1. Dongkwan Kim
2. Haggai Maron
3. Christopher Morris
4. Federico Errica
5. Luca Franceschi
6. Jiaxuan You
7. Rex Ying
8. Jure Leskovec
9. Aynaz Taheri
10. Hongyang Gao

Papers from Muhan Zhang's perspective

Ordered by how strongly the researchers Muhan Zhang 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• An End-to-End Deep Learning Architecture for Graph Classification (2018) — 1,721 citations
• Link Prediction Based on Graph Neural Networks (2018) — 2,394 citations
• D-VAE: A Variational Autoencoder for Directed Acyclic Graphs (2019) — 238 citations
• Weisfeiler-Lehman Neural Machine for Link Prediction (2017) — 252 citations