Xiangnan Kong

Artificial Intelligence · 2013–2020

A researcher in the Science corpus: 5 papers; trusts 194 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #31,578 of 35,345 by trust here, #16,166 by citations
• Computer Vision and Pattern Recognition — 37 citations into it
• Statistical and Nonlinear Physics — 20 citations into it
• Graph Learning — 15 citations into it
• Information Systems — 13 citations into it
• Model Architectures — 13 citations into it
• Molecular Biology — 13 citations into it; #16,939 of 17,773 by trust here, #15,850 by citations
• Efficiency & Compression — 12 citations into it

Whom Xiangnan Kong trusts overall

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

1. Robert E. Schapire
2. Ronald J. Williams
3. Vladimir Naumovich Vapnik
4. Zhi-Hua Zhou
5. Kaiming He
6. Geoffrey E. Hinton
7. Chih-Jen Lin
8. Yoav Freund
9. Min-Ling Zhang
10. Alex Krizhevsky

Who trusts Xiangnan Kong overall

1. Yanwei Fu
2. Yutao Zhang
3. Pengzhen Ren
4. Miao Xu
5. Min-Ling Zhang
6. Timothy M. Hospedales
7. Zhi-Hua Zhou
8. Jie Tang
9. Yun Xiao

Papers from Xiangnan Kong's perspective

Ordered by how strongly the researchers Xiangnan Kong trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Graph Classification using Structural Attention (2018) — 323 citations
• Transductive Multilabel Learning via Label Set Propagation (2013) — 178 citations
• EPNet: Learning to Exit with Flexible Multi-Branch Network (2020) — 61 citations
• Inferring anchor links across multiple heterogeneous social networks (2013) — 368 citations
• HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks (2013) — 329 citations