Qiang Wu
Artificial Intelligence · with Sun · 2007–2018
A researcher in the Science corpus: 7 papers; trusts 29 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 32 citations into it; #34,343 of 35,345 by trust here, #17,110 by citations
- Statistical Learning Theory — 26 citations into it
- Statistics and Probability — 18 citations into it; #11,321 of 12,932 by trust here, #10,275 by citations
- Computer Vision and Pattern Recognition — 14 citations into it
- Applied Mathematics — 11 citations into it; #5,385 of 7,620 by trust here, #5,850 by citations
- Mathematical Physics — 10 citations into it; #4,492 of 8,562 by trust here, #5,088 by citations
- Information Systems — 8 citations into it; #8,490 of 8,771 by trust here, #3,402 by citations
- Optimization & Training — 7 citations into it; #2,558 of 2,601 by trust here, #1,585 by citations
Whom Qiang Wu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Steve Smale
- Ding-Xuan Zhou
- Jerome H. Friedman
- Charles A. Micchelli
- Vladimir Naumovich Vapnik
- Massimiliano Pontil
- Olivier Bousquet
- Yuhai Wu
- Theodoros Evgeniou
- Kalervo Järvelin
Who trusts Qiang Wu overall
- Jianfeng Gao
- Hang Li
- Hongwei Sun
- Chris J. Oates
- Li Zhou
- Zeynep Akkalyoncu Yilmaz
- Tie-Yan Liu
- Michel Galley
- Mark A. Girolami
- Virgile Landeiro
Papers from Qiang Wu's perspective
Ordered by how strongly the researchers Qiang Wu 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)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Adapting boosting for information retrieval measures (2010) — 463 citations
- Regularized least square regression with dependent samples (2010) — 69 citations
- Inference and Prediction (2018) — 68 citations
- Learning the coordinate gradients (2011) — 13 citations
- Application of integral operator for regularized least-square regression (2009) — 44 citations
- A note on application of integral operator in learning theory (2009) — 38 citations
- McRank: Learning to Rank Using Multiple Classification and Gradient Boosting (2007) — 585 citations