Wenpeng Hu

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 6 papers; trusts 507 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 239 citations into it; #13,000 of 35,345 by trust here, #18,186 by citations
• Computer Vision and Pattern Recognition — 140 citations into it; #13,133 of 21,575 by trust here, #13,542 by citations
• Meta & Continual Learning — 82 citations into it; #410 of 1,041 by trust here, #291 by citations
• Representation & Self-Supervised Learning — 69 citations into it
• Model Architectures — 26 citations into it
• Optimization & Training — 21 citations into it
• Natural Language Processing — 19 citations into it
• Reinforcement Learning — 18 citations into it

Whom Wenpeng Hu trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Kaiming He
5. Diederik P. Kingma
6. Yoshua Bengio
7. Ilya Sutskever
8. Ross B. Girshick
9. Marc'Aurelio Ranzato
10. Karen Simonyan

Who trusts Wenpeng Hu overall

1. Gyuhak Kim
2. Zixuan Ke
3. Bing Liu
4. Qi Qin
5. Yiduo Guo
6. Da-Wei Zhou
7. Yaoyao Liu
8. Dongyan Zhao
9. Changnan Xiao
10. Liyuan Wang

Papers from Wenpeng Hu's perspective

Ordered by how strongly the researchers Wenpeng Hu 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• CMG: A Class-Mixed Generation Approach to Out-of-Distribution Detection (2022) — 15 citations
• Adaptive Orthogonal Projection for Batch and Online Continual Learning (2022) — 57 citations
• Continual Learning by Using Information of Each Class Holistically (2021) — 66 citations
• HRN: A Holistic Approach to One Class Learning (2020) — 71 citations
• Using the Past Knowledge to Improve Sentiment Classification (2020) — 11 citations
• Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation (2018) — 150 citations