Xinchen Yan

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 6 papers; trusts 584 researchers and is trusted by 1,449 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 172 citations into it; #1,125 of 21,575 by trust here, #1,045 by citations
• Artificial Intelligence — 69 citations into it
• Generative Models — 40 citations into it; #132 of 1,956 by trust here, #102 by citations
• Model Architectures — 20 citations into it
• Multimodal Learning — 20 citations into it; #281 of 1,211 by trust here, #144 by citations
• Representation & Self-Supervised Learning — 20 citations into it; #1,817 of 3,145 by trust here, #1,090 by citations
• Evaluation & Benchmarks — 17 citations into it
• Aerospace Engineering — 16 citations into it; #2,427 of 6,421 by trust here, #922 by citations

Whom Xinchen Yan trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Kaiming He
8. Jimmy Ba
9. Ruslan Salakhutdinov
10. Karen Simonyan

Who trusts Xinchen Yan overall

1. Ming-Yu Liu
2. Shichen Liu
3. Jitendra Malik
4. Yoshua Bengio
5. Jiajun Wu
6. Jianwei Yang
7. Jun Shu
8. Jun-Yan Zhu
9. Zhe Gan
10. Ruslan Salakhutdinov

Papers from Xinchen Yan's perspective

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

Their papers

• Generative Adversarial Text to Image Synthesis (2016) — 3,433 citations
• Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision (2016) — 607 citations
• Attribute2Image: Conditional Image Generation from Visual Attributes (2015) — 808 citations
• Block-NeRF: Scalable Large Scene Neural View Synthesis (2022) — 1,104 citations
• Learning Grasping Interaction with Geometry-aware 3D Representations (2017) — 8 citations
• Learning Structured Output Representation using Deep Conditional Generative Models (2015) — 3,936 citations