Chuxu Zhang

Statistical and Nonlinear Physics · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 39 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 94 citations into it
• Artificial Intelligence — 70 citations into it
• Sociology and Political Science — 31 citations into it
• Graph Learning — 24 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Molecular Biology — 15 citations into it
• Management Science and Operations Research — 11 citations into it

Whom Chuxu Zhang trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Tomas Mikolov
5. Jimmy Ba
6. Ilya Sutskever
7. Alex Krizhevsky
8. Yann LeCun
9. Quoc V. Le
10. Evan Shelhamer

Who trusts Chuxu Zhang overall

1. Xiao Wang
2. Ziniu Hu
3. Shreshth Tuli
4. Sebastian Schmidl
5. Tong Zhao
6. Guansong Pang
7. Kukjin Choi
8. Yu-xin Zhang
9. Qingsong Lv
10. Astha Garg

Papers from Chuxu Zhang's perspective

Ordered by how strongly the researchers Chuxu 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Graph Few-shot Learning via Knowledge Transfer (2019) — 194 citations
• Heterogeneous Graph Neural Network (2019) — 1,630 citations
• A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data (2018) — 934 citations
• Dynamics of information diffusion and its applications on complex networks (2016) — 432 citations