Xiuyuan Cheng

Computer Vision and Pattern Recognition · 2012–2019

A researcher in the Science corpus: 6 papers; trusts 495 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 103 citations into it; #10,964 of 21,575 by trust here, #13,542 by citations
• Artificial Intelligence — 62 citations into it; #16,028 of 35,345 by trust here, #19,873 by citations
• Model Architectures — 28 citations into it; #2,202 of 2,278 by trust here, #2,009 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Efficiency & Compression — 18 citations into it
• Generative Models — 17 citations into it
• Statistics and Probability — 14 citations into it; #8,823 of 12,932 by trust here, #9,233 by citations
• Computational Mechanics — 12 citations into it

Whom Xiuyuan Cheng trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Stéphane Mallat
5. Yann LeCun
6. Kaiming He
7. Ruslan Salakhutdinov
8. Karen Simonyan
9. Diederik P. Kingma
10. Andrew Zisserman

Who trusts Xiuyuan Cheng overall

1. Zhou Fan
2. David W. Romero
3. Jimmy Ba
4. Andrea Montanari
5. Johannes Klicpera
6. Michael M. Bronstein
7. Ze Wang
8. Joan Bruna
9. Zichen Miao
10. Yann LeCun

Papers from Xiuyuan Cheng's perspective

Ordered by how strongly the researchers Xiuyuan Cheng 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Haar Scattering Networks (2015) — 31 citations
• Unsupervised Deep Haar Scattering on Graphs (2014) — 55 citations
• THE SPECTRUM OF RANDOM INNER-PRODUCT KERNEL MATRICES (2012) — 96 citations
• RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks (2018) — 47 citations
• Stochastic Conditional Generative Networks with Basis Decomposition (2019) — 19 citations
• DCFNet: Deep Neural Network with Decomposed Convolutional Filters (2018) — 73 citations