Zhankui He
Artificial Intelligence · 2018–2024
A researcher in the Science corpus: 4 papers; trusts 240 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 89 citations into it
- Information Systems — 65 citations into it; #2,936 of 8,771 by trust here, #2,912 by citations
- Computer Vision and Pattern Recognition — 56 citations into it; #11,798 of 21,575 by trust here, #15,162 by citations
- Management Science and Operations Research — 18 citations into it; #4,471 of 11,946 by trust here, #3,357 by citations
- Recommender Systems — 17 citations into it
- Model Architectures — 16 citations into it
- Representation & Self-Supervised Learning — 13 citations into it
- Optimization & Training — 10 citations into it; #1,931 of 2,601 by trust here, #1,585 by citations
Whom Zhankui He trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Kaiming He
- Ilya Sutskever
- Xiangnan He
- Christian Szegedy
- Yann LeCun
- Diederik P. Kingma
- Karen Simonyan
Who trusts Zhankui He overall
- Xiangnan He
- Xiang Wang
- Zhiqiang Shen
- Le Wu
- Aminu Da’u
- Devesh Walawalkar
- Jianping Gou
- Inseop Chung
- Xu Chen
- Changlin Li
Papers from Zhankui He's perspective
Ordered by how strongly the researchers Zhankui He trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Adversarial Personalized Ranking for Recommendation (2018) — 434 citations
- NAIS: Neural Attentive Item Similarity Model for Recommendation (2018) — 531 citations
- Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2024) — 345 citations
- MEAL: Multi-Model Ensemble via Adversarial Learning (2018) — 157 citations