Weibo Liu

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 3 papers; trusts 85 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 69 citations into it; #20,269 of 21,575 by trust here, #5,890 by citations
• Artificial Intelligence — 54 citations into it; #33,092 of 35,345 by trust here, #8,893 by citations
• Signal Processing — 23 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Model Architectures — 11 citations into it; #2,108 of 2,278 by trust here, #874 by citations
• Natural Language Processing — 10 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Speech & Audio — 6 citations into it

Whom Weibo Liu trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Abdel-rahman Mohamed
6. Ruslan Salakhutdinov
7. Li Deng
8. J. Urgen Schmidhuber
9. Ilya Sutskever
10. Radford M. Neal

Who trusts Weibo Liu overall

1. Iqbal H. Sarker
2. Aminu Da’u
3. Yongquan Yang
4. Hakan Gunduz
5. Maciej A. Mazurowski
6. Mohamed Amine Ferrag
7. Mohammed Ali Al-Garadi
8. S. Jinnai
9. Oludare Isaac Abiodun
10. Billy Pik Lik Lau

Papers from Weibo Liu's perspective

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

Their papers

• A Small-Sized Object Detection Oriented Multi-Scale Feature Fusion Approach With Application to Defect Detection (2022) — 553 citations
• Facial expression recognition via learning deep sparse autoencoders (2018) — 450 citations
• A survey of deep neural network architectures and their applications (2017) — 3,019 citations