Zhen Liu
Computer Vision and Pattern Recognition · with Liu · 2017–2022
A researcher in the Science corpus: 5 papers; trusts 427 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 152 citations into it; #15,446 of 21,575 by trust here, #10,677 by citations
- Artificial Intelligence — 138 citations into it; #23,858 of 35,345 by trust here, #15,004 by citations
- Optimization & Training — 55 citations into it
- Model Architectures — 40 citations into it
- Representation & Self-Supervised Learning — 31 citations into it
- Computational Mechanics — 26 citations into it
- Signal Processing — 19 citations into it
- Generative Models — 13 citations into it
Whom Zhen Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Weiyang Liu
- Yoshua Bengio
- Yann LeCun
- Karen Simonyan
- Ilya Sutskever
- Diederik P. Kingma
- X. Zhang
Who trusts Zhen Liu overall
Papers from Zhen Liu's perspective
Ordered by how strongly the researchers Zhen Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Deep Hyperspherical Learning (2017) — 153 citations
- Regularizing Neural Networks via Minimizing Hyperspherical Energy (2019) — 48 citations
- Continual Learning by Modeling Intra-Class Variation (2022) — 19 citations
- Learning with Hyperspherical Uniformity (2021) — 50 citations
- Orthogonal Over-Parameterized Training (2020) — 55 citations