Senwei Liang

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 588 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 110 citations into it; #13,258 of 21,575 by trust here, #17,258 by citations
• Artificial Intelligence — 87 citations into it; #21,326 of 35,345 by trust here, #26,682 by citations
• Model Architectures — 48 citations into it
• Optimization & Training — 25 citations into it
• Meta & Continual Learning — 11 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Natural Language Processing — 6 citations into it
• Reinforcement Learning — 5 citations into it

Whom Senwei Liang trusts overall

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

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

Who trusts Senwei Liang overall

1. Zhongzhan Huang
2. Lei Huang
3. Mingfu Liang
4. Wei He

Papers from Senwei Liang's perspective

Ordered by how strongly the researchers Senwei Liang 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

• AlterSGD: Finding Flat Minima for Continual Learning by Alternative Training (2021) — 8 citations
• Efficient Attention Network: Accelerate Attention by Searching Where to Plug (2020) — 12 citations
• DIANet: Dense-and-Implicit Attention Network (2019) — 66 citations
• Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise (2019) — 66 citations