Chengxu Zhuang

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 1,005 researchers and is trusted by 149 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 174 citations into it; #2,945 of 21,575 by trust here, #4,623 by citations
• Artificial Intelligence — 121 citations into it; #11,559 of 35,345 by trust here, #6,468 by citations
• Representation & Self-Supervised Learning — 89 citations into it; #1,357 of 3,145 by trust here, #877 by citations
• Cognitive Neuroscience — 34 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Model Architectures — 24 citations into it
• Generative Models — 18 citations into it
• Optimization & Training — 11 citations into it

Whom Chengxu Zhuang trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Ross B. Girshick
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yoshua Bengio
7. Karen Simonyan
8. Andrew Zisserman
9. Ilya Sutskever
10. Yann LeCun

Who trusts Chengxu Zhuang overall

1. Yonglong Tian
2. Xiao Wang
3. Kaiming He
4. Olivier J. H'enaff
5. Nanxuan Zhao
6. A. Piergiovanni
7. Spyros Gidaris
8. Junnan Li
9. Yannis Kalantidis
10. Zhaowei Cai

Papers from Chengxu Zhuang's perspective

Ordered by how strongly the researchers Chengxu Zhuang 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Local Aggregation for Unsupervised Learning of Visual Embeddings (2019) — 465 citations
• Flexible Neural Representation for Physics Prediction (2018) — 266 citations
• Unsupervised Learning From Video With Deep Neural Embeddings (2019) — 65 citations
• Conditional Negative Sampling for Contrastive Learning of Visual Representations (2020) — 91 citations
• Unsupervised neural network models of the ventral visual stream (2020) — 414 citations