Rongmei Lin

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 336 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 149 citations into it; #20,386 of 21,575 by trust here, #13,269 by citations
• Artificial Intelligence — 115 citations into it; #32,876 of 35,345 by trust here, #17,776 by citations
• Optimization & Training — 52 citations into it
• Model Architectures — 47 citations into it
• Computational Mechanics — 26 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Generative Models — 15 citations into it
• Aerospace Engineering — 7 citations into it

Whom Rongmei Lin trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Weiyang Liu
5. Yoshua Bengio
6. Yann LeCun
7. Sergey Ioffe
8. X. Zhang
9. Christian Szegedy
10. Diederik P. Kingma

Who trusts Rongmei Lin overall

1. Weiyang Liu
2. Chao Yu
3. L. Yu
4. Zhen Liu
5. Jiming Liu
6. Xiaobo Wang
7. Jiankang Deng
8. Dilin Wang

Papers from Rongmei Lin's perspective

Ordered by how strongly the researchers Rongmei Lin 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Decoupled Networks (2018) — 87 citations
• Regularizing Neural Networks via Minimizing Hyperspherical Energy (2019) — 48 citations
• Learning with Hyperspherical Uniformity (2021) — 50 citations
• Orthogonal Over-Parameterized Training (2020) — 55 citations
• Learning towards Minimum Hyperspherical Energy (2018) — 172 citations
• A Deep Deterministic Policy Gradient Approach to Medication Dosing and Surveillance in the ICU (2018) — 44 citations