Jin Huang

Artificial Intelligence · with Ling · 2003–2005

A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 13 citations into it; #4,779 of 35,345 by trust here, #10,319 by citations
• Statistical Learning Theory — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Information Systems — 7 citations into it

Whom Jin Huang trusts overall

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

1. Andrew P. Bradley
2. Usama M. Fayyad
3. Yi Lin
4. Yuhai Wu
5. Thorsten Joachims
6. Foster J. Provost
7. Cèsar Ferri
8. David J. Hand
9. Bernhard Schölkopf
10. J. Ross Quinlan

Who trusts Jin Huang overall

1. Octavio Loyola-González
2. Chakkrit Kla Tantithamthavorn
3. Victoria López
4. Alberto Fernández
5. Cèsar Ferri
6. Mikel Galar
7. Edoardo Vignotto
8. H. M.
9. Giuseppe Jurman
10. Masud Karim

Papers from Jin Huang's perspective

Ordered by how strongly the researchers Jin Huang 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Going deeper with convolutions
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Using AUC and accuracy in evaluating learning algorithms (2005) — 2,138 citations
• AUC: A Better Measure than Accuracy in Comparing Learning Algorithms (2003) — 491 citations
• Comparing naive Bayes, decision trees, and SVM with AUC and accuracy (2003) — 255 citations