Huiwen Chang

Computer Vision and Pattern Recognition · 2020–2023

A researcher in the Science corpus: 7 papers; trusts 678 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 289 citations into it; #9,920 of 21,575 by trust here, #2,552 by citations
• Generative Models — 180 citations into it; #1,787 of 1,956 by trust here, #418 by citations
• Artificial Intelligence — 129 citations into it
• Evaluation & Benchmarks — 41 citations into it
• Model Architectures — 30 citations into it; #2,019 of 2,278 by trust here, #1,182 by citations
• Computer Graphics and Computer-Aided Design — 27 citations into it
• Optimization & Training — 27 citations into it
• Statistical and Nonlinear Physics — 25 citations into it

Whom Huiwen Chang trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Ilya Sutskever
7. Yann LeCun
8. Karen Simonyan
9. Oriol Vinyals
10. Christian Szegedy

Who trusts Huiwen Chang overall

1. Lijun Yu
2. José Lezama
3. Shanghua Gao
4. Tianhong Li
5. Keyu Tian
6. Agrim Gupta
7. Qihang Yu
8. Jingfeng Yao
9. Yi Jiang
10. Itai Gat

Papers from Huiwen Chang's perspective

Ordered by how strongly the researchers Huiwen Chang 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

• Palette: Image-to-Image Diffusion Models (2021) — 2,308 citations
• MaskGIT: Masked Generative Image Transformer (2022) — 1,238 citations
• Muse: Text-To-Image Generation via Masked Generative Transformers (2023) — 783 citations
• AutoFlow: Learning a Better Training Set for Optical Flow (2021) — 137 citations
• MAGVIT: Masked Generative Video Transformer (2022) — 394 citations
• Improved Masked Image Generation with Token-Critic (2022) — 74 citations
• Distortion Agnostic Deep Watermarking (2020) — 217 citations