Weizhe Hua

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 187 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #22,874 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 67 citations into it; #14,120 of 21,575 by trust here, #10,905 by citations
• Model Architectures — 45 citations into it; #2,161 of 2,278 by trust here, #1,548 by citations
• Efficiency & Compression — 43 citations into it
• Optimization & Training — 7 citations into it

Whom Weizhe Hua trusts overall

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

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

Who trusts Weizhe Hua overall

1. Yizeng Han
2. Rameswar Panda
3. S. Cong
4. Yulin Wang
5. Zhihang Yuan
6. Songlin Yang
7. Jinting Chen
8. Chuanjian Liu
9. Harsh Mehta
10. Gao Huang

Papers from Weizhe Hua's perspective

Ordered by how strongly the researchers Weizhe Hua 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. 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. Going deeper with convolutions

Their papers

• Channel Gating Neural Networks (2018) — 207 citations
• Transformer Quality in Linear Time (2022) — 357 citations
• Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating (2019) — 48 citations
• Reverse Engineering Convolutional Neural Networks Through Side-channel Information Leaks (2018) — 298 citations