Le Wu

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 148 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 191 citations into it; #19,536 of 35,345 by trust here, #12,387 by citations
• Information Systems — 184 citations into it; #4,013 of 8,771 by trust here, #1,964 by citations
• Computer Vision and Pattern Recognition — 100 citations into it
• Recommender Systems — 56 citations into it
• Management Science and Operations Research — 48 citations into it
• Graph Learning — 37 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Model Architectures — 22 citations into it

Whom Le Wu trusts overall

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

1. Steffen Rendle
2. Xiangnan He
3. Yehuda Koren
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ruslan Salakhutdinov
7. Xiang Wang
8. Diederik P. Kingma
9. Alex Krizhevsky
10. Ilya Sutskever

Who trusts Le Wu overall

1. Chen Gao
2. Junliang Yu
3. Shiwen Wu
4. Xiangnan He
5. Yu Zheng
6. Jianxin Chang
7. Hongzhi Yin
8. Fei Sun
9. Xiang Wang
10. Lianghao Xia

Papers from Le Wu's perspective

Ordered by how strongly the researchers Le Wu 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021) — 445 citations
• Fine-Grained Similarity Measurement between Educational Videos and Exercises (2020) — 20 citations
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach (2020) — 654 citations
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation (2020) — 350 citations
• A Neural Influence Diffusion Model for Social Recommendation (2019) — 566 citations
• A Reinforcement Learning Framework for Explainable Recommendation (2018) — 163 citations