Kaize Ding

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 56 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #26,887 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #19,320 of 21,575 by trust here, #20,756 by citations
• Information Systems — 25 citations into it
• Graph Learning — 22 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Statistical and Nonlinear Physics — 17 citations into it; #9,782 of 10,127 by trust here, #7,024 by citations
• Meta & Continual Learning — 12 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Kaize Ding trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Thomas Kipf
5. Alex Krizhevsky
6. Max Welling
7. Jimmy Ba
8. Kaiming He
9. Ilya Sutskever
10. Yann LeCun

Who trusts Kaize Ding overall

1. Yixin Liu
2. Guansong Pang
3. Jianheng Tang
4. Shirui Pan
5. Xiaoxiao Ma
6. Zhen Tan
7. Jaya Kalla
8. Chuan Zhou
9. Ruocheng Guo
10. Chunhua Shen

Papers from Kaize Ding's perspective

Ordered by how strongly the researchers Kaize Ding 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. 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

• Deep Anomaly Detection on Attributed Networks (2019) — 630 citations
• Next-item Recommendation with Sequential Hypergraphs (2020) — 313 citations
• Graph Few-shot Class-incremental Learning (2021) — 74 citations