Wang-Cheng Kang

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 545 researchers and is trusted by 71 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #3,609 of 35,345 by trust here, #9,557 by citations
• Computer Vision and Pattern Recognition — 81 citations into it; #3,948 of 21,575 by trust here, #6,485 by citations
• Information Systems — 57 citations into it; #287 of 8,771 by trust here, #1,203 by citations
• Recommender Systems — 27 citations into it
• Management Science and Operations Research — 26 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Model Architectures — 16 citations into it
• Marketing — 14 citations into it

Whom Wang-Cheng Kang trusts overall

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

1. Yehuda Koren
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Steffen Rendle
6. Alex Krizhevsky
7. Yoshua Bengio
8. Ruining He
9. Jimmy Ba
10. Ilya Sutskever

Who trusts Wang-Cheng Kang overall

1. Yupeng Hou
2. Jiacheng Li
3. Fei Sun
4. Fajie Yuan
5. Kun Zhou
6. Chen Ma
7. Julian McAuley
8. Wayne Xin Zhao
9. Liwei Wu
10. Xiangnan He

Papers from Wang-Cheng Kang's perspective

Ordered by how strongly the researchers Wang-Cheng Kang 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Feature Learning Based Deep Supervised Hashing with Pairwise Labels (2015) — 737 citations
• Learning to Embed Categorical Features without Embedding Tables for Recommendation (2020) — 88 citations
• Self-Attentive Sequential Recommendation (2018) — 3,858 citations
• Visually-Aware Fashion Recommendation and Design with Generative Image Models (2017) — 298 citations
• Translation-based Recommendation (2017) — 477 citations