Xiaofeng He
Artificial Intelligence · 2001–2018
A researcher in the Science corpus: 8 papers; trusts 88 researchers and is trusted by 77 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 40 citations into it; #3,186 of 35,345 by trust here, #6,720 by citations
- Reinforcement Learning — 22 citations into it; #1,362 of 2,242 by trust here, #1,895 by citations
- Computer Vision and Pattern Recognition — 16 citations into it; #3,455 of 21,575 by trust here, #5,082 by citations
- Medical & Health AI — 12 citations into it; #106 of 239 by trust here, #193 by citations
- Statistics and Probability — 11 citations into it; #5,079 of 12,932 by trust here, #3,330 by citations
- Epidemiology — 8 citations into it; #1,618 of 2,148 by trust here, #2,128 by citations
- Molecular Biology — 7 citations into it; #1,381 of 17,773 by trust here, #9,270 by citations
- Health Information Management — 6 citations into it; #289 of 344 by trust here, #334 by citations
Whom Xiaofeng He trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Richard S. Sutton
- Hongyuan Zha
- Yoshua Bengio
- Andrew Y. Ng
- Chris H. Q. Ding
- Edward Choi
- Jianying Hu
- Alex Krizhevsky
- John Hartigan
Who trusts Xiaofeng He overall
- Chris H. Q. Ding
- Tao Li
- Francis R. Bach
- Hua Wang
- Feiping Nie
- Deng Cai
- Michael I. Jordan
- Wei Xu
- Charu C. Aggarwal
- Hongyuan Zha
Papers from Xiaofeng He's perspective
Ordered by how strongly the researchers Xiaofeng He trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering (2005) — 1,040 citations
- Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (2018) — 295 citations
- K-means clustering via principal component analysis (2004) — 1,702 citations
- R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization (2006) — 704 citations
- Bipartite graph partitioning and data clustering (2001) — 389 citations
- A min-max cut algorithm for graph partitioning and data clustering (2001) — 954 citations
- Personalized Prescription for Comorbidity (2018) — 33 citations
- Spectral Relaxation for K-means Clustering (2001) — 733 citations