Hongning Wang

Artificial Intelligence · 2010–2019

A researcher in the Science corpus: 7 papers; trusts 85 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #10,014 of 35,345 by trust here, #12,761 by citations
• Information Systems — 77 citations into it; #2,237 of 8,771 by trust here, #3,315 by citations
• Management Science and Operations Research — 34 citations into it; #3,727 of 11,946 by trust here, #4,118 by citations
• Recommender Systems — 32 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Reinforcement Learning — 17 citations into it
• Natural Language Processing — 13 citations into it; #2,386 of 5,688 by trust here, #3,478 by citations
• Information Retrieval & Search — 10 citations into it

Whom Hongning Wang trusts overall

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

1. Steffen Rendle
2. Peter Auer
3. Lihong Li
4. Yehuda Koren
5. David M. Blei
6. Nicolò Cesa-Bianchi
7. Michael I. Jordan
8. Atsuyoshi Nakamura
9. Chih-Jen Lin
10. C. Burges

Who trusts Hongning Wang overall

1. B. Liu
2. Lei Zhang
3. Kim Schouten
4. Yao Wu
5. Shiyu Chang
6. Zhiyuan Chen
7. Yongfeng Zhang
8. Zhiyong Cheng
9. Soujanya Poria
10. Konstantin Bauman

Papers from Hongning Wang's perspective

Ordered by how strongly the researchers Hongning Wang 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Latent aspect rating analysis on review text data: a rating regression approach (2010) — 747 citations
• Latent aspect rating analysis without aspect keyword supervision (2011) — 339 citations
• Factorization Bandits for Interactive Recommendation (2017) — 122 citations
• Explainable Recommendation via Multi-Task Learning in Opinionated Text Data (2018) — 237 citations
• Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems (2017) — 88 citations
• Learning Hidden Features for Contextual Bandits (2016) — 86 citations
• The FacT: Taming Latent Factor Models for Explainability with Factorization Trees (2019) — 88 citations