Zhongqi Lu

Artificial Intelligence · 2011–2016

A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it; #22,488 of 35,345 by trust here, #15,004 by citations
• Information Systems — 19 citations into it; #5,570 of 8,771 by trust here, #7,107 by citations
• Computer Vision and Pattern Recognition — 14 citations into it; #20,524 of 21,575 by trust here, #10,500 by citations
• Recommender Systems — 12 citations into it
• Reinforcement Learning — 10 citations into it
• Management Science and Operations Research — 6 citations into it; #7,916 of 11,946 by trust here, #8,906 by citations
• Evaluation & Benchmarks — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Zhongqi Lu trusts overall

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

1. Ruslan Salakhutdinov
2. Yehuda Koren
3. Thomas Hofmann
4. Richard S. Sutton
5. Andriy Mnih
6. Christopher J. C. H. Watkins
7. Geoffrey E. Hinton
8. Volodymyr Mnih
9. Lihong Li
10. Damien Ernst

Who trusts Zhongqi Lu overall

1. M. M. Afsar
2. Trafford Crump
3. Chuhan Wu
4. Gabriella Csurka
5. Behrouz Homayoun Far

Papers from Zhongqi Lu's perspective

Ordered by how strongly the researchers Zhongqi Lu 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• Heterogeneous Transfer Learning for Image Classification (2011) — 385 citations
• Content-Based Collaborative Filtering for News Topic Recommendation (2015) — 156 citations
• Partially Observable Markov Decision Process for Recommender Systems (2016) — 30 citations