Chiyu "Max" Jiang

Computer Vision and Pattern Recognition · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 99 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 79 citations into it
• Computational Mechanics — 52 citations into it; #8,989 of 10,001 by trust here, #2,574 by citations
• Computer Graphics and Computer-Aided Design — 41 citations into it; #1,428 of 1,584 by trust here, #539 by citations
• 3D Vision & Pose — 38 citations into it
• Generative Models — 28 citations into it
• Geology — 18 citations into it
• Artificial Intelligence — 16 citations into it
• Aerospace Engineering — 14 citations into it

Whom Chiyu "Max" Jiang trusts overall

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

1. M. Kazhdan
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. Hao Su
10. Zhiqin Chen

Who trusts Chiyu "Max" Jiang overall

1. Songyou Peng
2. Yinbo Chen
3. Julian Chibane
4. Michael Niemeyer
5. Biao Zhang
6. Matthew Tancik
7. Can Wang
8. Stephen Lombardi
9. Zi-Xin Zou
10. Vincent Sitzmann

Papers from Chiyu "Max" Jiang's perspective

Ordered by how strongly the researchers Chiyu "Max" Jiang 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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 Implicit Grid Representations for 3D Scenes (2020) — 622 citations
• MotionDiffuser: Controllable Multi-Agent Motion Prediction Using Diffusion (2023) — 305 citations
• Shape As Points: A Differentiable Poisson Solver (2021) — 300 citations