Chuan Guo

Computer Vision and Pattern Recognition · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 201 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 80 citations into it; #9,461 of 21,575 by trust here, #10,334 by citations
• Control and Systems Engineering — 29 citations into it; #3,080 of 8,257 by trust here, #2,004 by citations
• Artificial Intelligence — 22 citations into it
• Generative Models — 19 citations into it
• Natural Language Processing — 14 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Signal Processing — 6 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Chuan Guo trusts overall

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

1. Diederik P. Kingma
2. Oriol Vinyals
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Aäron van den Oord
6. Alex Krizhevsky
7. Kaiming He
8. Ilya Sutskever
9. Yann LeCun
10. Alex Graves

Who trusts Chuan Guo overall

1. Guy Tevet
2. Biao Jiang
3. Mingyuan Zhang
4. Xin Chen
5. Jianrong Zhang
6. Mathis Petrovich
7. Ye Yuan
8. Yuxuan Mu
9. Yonatan Shafir
10. Brian Gordon

Papers from Chuan Guo's perspective

Ordered by how strongly the researchers Chuan Guo 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• Action2Motion: Conditioned Generation of 3D Human Motions (2020) — 642 citations
• Generating Diverse and Natural 3D Human Motions from Text (2022) — 1,034 citations
• MoMask: Generative Masked Modeling of 3D Human Motions (2023) — 445 citations
• TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts (2022) — 433 citations