Kang Li

Artificial Intelligence · with Gao · 2014

A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #24,644 of 35,345 by trust here, #19,018 by citations
• Representation & Self-Supervised Learning — 14 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Statistical and Nonlinear Physics — 8 citations into it; #9,109 of 10,127 by trust here, #5,625 by citations
• Probabilistic & Bayesian Methods — 7 citations into it

Whom Kang Li trusts overall

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

1. Geoffrey E. Hinton
2. Graham W. Taylor
3. Tijmen Tieleman
4. Peter D. Hoff
5. Lada A. Adamic
6. Alex Krizhevsky
7. Yoshua Bengio
8. Yann LeCun
9. Radford M. Neal
10. Lei Tang

Who trusts Kang Li overall

1. Saket Navlakha
2. Gabriella Csurka
3. Aditya Grover

Papers from Kang Li's perspective

Ordered by how strongly the researchers Kang Li 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• On handling negative transfer and imbalanced distributions in multiple source transfer learning (2014) — 131 citations
• LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data (2014) — 29 citations
• A Deep Learning Approach to Link Prediction in Dynamic Networks (2014) — 163 citations