Lei Huang

Computer Vision and Pattern Recognition · with Liu · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 784 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 227 citations into it; #9,279 of 21,575 by trust here, #9,435 by citations
• Artificial Intelligence — 220 citations into it; #13,768 of 35,345 by trust here, #13,760 by citations
• Optimization & Training — 142 citations into it; #2,063 of 2,601 by trust here, #2,112 by citations
• Model Architectures — 89 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Statistical and Nonlinear Physics — 25 citations into it
• Computational Mechanics — 23 citations into it
• Generative Models — 23 citations into it

Whom Lei Huang trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Sergey Ioffe
7. Karen Simonyan
8. Christian Szegedy
9. Jimmy Ba
10. Yann LeCun

Who trusts Lei Huang overall

1. Cecilia Summers
2. Andrew Brock
3. Zhi Chen
4. Yi Zhou
5. Sungha Choi
6. Jie Qin
7. Michael J. Dinneen
8. Soham De
9. Fan Zhu
10. Breton L. Minnehan

Papers from Lei Huang's perspective

Ordered by how strongly the researchers Lei Huang 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

• S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (2021) — 24 citations
• Normalization Techniques in Training DNNs: Methodology, Analysis and Application (2020) — 457 citations
• Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks (2017) — 264 citations
• Centered Weight Normalization in Accelerating Training of Deep Neural Networks (2017) — 83 citations
• Decorrelated Batch Normalization (2018) — 217 citations
• Iterative Normalization: Beyond Standardization Towards Efficient Whitening (2019) — 188 citations