Wei Gao

Artificial Intelligence · with Zhou · 2011–2019

A researcher in the Science corpus: 4 papers; trusts 33 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #17,260 of 35,345 by trust here, #17,776 by citations
• Statistical Learning Theory — 18 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Statistics and Probability — 15 citations into it
• Optimization & Training — 13 citations into it
• Computational Mechanics — 8 citations into it; #8,800 of 10,001 by trust here, #8,258 by citations
• Evaluation & Benchmarks — 8 citations into it; #1,923 of 3,147 by trust here, #2,743 by citations
• Signal Processing — 8 citations into it

Whom Wei Gao trusts overall

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

1. Thorsten Joachims
2. Tong Zhang
3. Robert E. Schapire
4. Yoram Singer
5. Ambuj Tewari
6. Koby Crammer
7. Iain M. Johnstone
8. Andre´ Elisseeff
9. J. Ross Quinlan
10. Ali Rahimi

Who trusts Wei Gao overall

1. Harish Guruprasad Ramaswamy
2. Tianbao Yang
3. Shivani Agarwal
4. Min-Ling Zhang
5. Yiming Ying
6. Zhi-Hua Zhou
7. Anton Osokin
8. Carlo Ciliberto
9. Peng Zhao

Papers from Wei Gao's perspective

Ordered by how strongly the researchers Wei Gao 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Efficient Label Contamination Attacks Against Black-Box Learning Models (2017) — 74 citations
• On the Consistency of Multi-Label Learning (2011) — 155 citations
• One-Pass AUC Optimization (2013) — 195 citations
• Weighted Oblique Decision Trees (2019) — 31 citations