Weiwei Liu

Artificial Intelligence · with Chen · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 87 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #24,733 of 35,345 by trust here, #25,809 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #15,201 of 21,575 by trust here, #19,310 by citations
• Information Systems — 14 citations into it
• Evaluation & Benchmarks — 13 citations into it; #2,210 of 3,147 by trust here, #2,971 by citations
• Representation & Self-Supervised Learning — 9 citations into it
• Signal Processing — 8 citations into it
• Optimization & Training — 7 citations into it
• Statistics and Probability — 6 citations into it

Whom Weiwei Liu trusts overall

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

1. Min-Ling Zhang
2. Zhi-Hua Zhou
3. Geoffrey E. Hinton
4. Brian Kulis
5. Alex Krizhevsky
6. Yoshua Bengio
7. Diederik P. Kingma
8. Ilya Sutskever
9. Thorsten Joachims
10. Jason Aaron Edward Weston

Who trusts Weiwei Liu overall

1. Yuncheng Li
2. Cheng-Kang Hsieh
3. Yale Song
4. Longqi Yang
5. Rui Gao
6. Jiebo Luo

Papers from Weiwei Liu's perspective

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

Their papers

• Large Margin Metric Learning for Multi-Label Prediction (2015) — 90 citations
• The Emerging Trends of Multi-Label Learning (2020) — 387 citations
• Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification (2019) — 41 citations