Hongwei Wang

Artificial Intelligence · The Web Conference · 2017–2020

A researcher in the Science corpus: 13 papers; trusts 269 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 294 citations into it; #6,901 of 35,345 by trust here, #4,923 by citations
• Information Systems — 154 citations into it; #1,056 of 8,771 by trust here, #764 by citations
• Graph Learning — 106 citations into it
• Computer Vision and Pattern Recognition — 103 citations into it
• Representation & Self-Supervised Learning — 91 citations into it
• Recommender Systems — 63 citations into it; #81 of 233 by trust here, #61 by citations
• Statistical and Nonlinear Physics — 63 citations into it
• Natural Language Processing — 42 citations into it

Whom Hongwei Wang trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Alex Krizhevsky
6. Steffen Rendle
7. Tomas Mikolov
8. Yann LeCun
9. Ruslan Salakhutdinov
10. Kaiming He

Who trusts Hongwei Wang overall

1. Fuzheng Zhang
2. Xiaohan Zou
3. Xiang Wang
4. Chuhan Wu
5. Xing Xie
6. Qingyu Guo
7. Shiwen Wu
8. Miao Zhao
9. Fangzhao Wu
10. Xiangnan He

Papers from Hongwei Wang's perspective

Ordered by how strongly the researchers Hongwei Wang 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GraphGAN: Graph Representation Learning with Generative Adversarial Nets (2017) — 698 citations
• Unifying Graph Convolutional Neural Networks and Label Propagation (2020) — 198 citations
• Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning (2020) — 382 citations
• Knowledge Graph Convolutional Networks for Recommender Systems (2019) — 1,150 citations
• Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation (2019) — 536 citations
• Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (2019) — 660 citations
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018) — 1,246 citations
• DKN: Deep Knowledge-Aware Network for News Recommendation (2018) — 1,195 citations
• AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods (2018) — 73 citations
• Ripple Network: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018) — 34 citations
• Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017) — 32 citations
• Collaborative Filtering-Based Recommendation of Online Social Voting (2017) — 82 citations
• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017) — 343 citations