Qian Xu

Artificial Intelligence · with Yang · 2010–2021

A researcher in the Science corpus: 8 papers; trusts 70 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 121 citations into it; #32,398 of 35,345 by trust here, #25,809 by citations
• Molecular Biology — 52 citations into it; #4,356 of 17,773 by trust here, #10,047 by citations
• Representation & Self-Supervised Learning — 37 citations into it
• Graph Learning — 25 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Reinforcement Learning — 21 citations into it
• Statistical and Nonlinear Physics — 19 citations into it
• Optimization & Training — 18 citations into it

Whom Qian Xu trusts overall

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

1. Ajit Paul Singh
2. Kuo-Chen Chou
3. Laurent Jacob
4. Theodoros Evgeniou
5. Qiang Yang
6. Geoffrey J. Gordon
7. Tom Fawcett
8. R. Caruana
9. Christian Widmer
10. Geoffrey E. Hinton

Who trusts Qian Xu overall

1. Qiang Yang
2. Yu Zhang
3. Meghana Kshirsagar
4. Wouter M. Kouw
5. Alice Schoenauer Sebag
6. Jaime G. Carbonell
7. Garrett Wilson
8. Judith Klein-Seetharaman
9. Sebastian Ruder
10. Ben Tan

Papers from Qian Xu's perspective

Ordered by how strongly the researchers Qian Xu 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. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Survey of Transfer and Multitask Learning in Bioinformatics (2011) — 79 citations
• Protein-protein interaction prediction via Collective Matrix Factorization (2010) — 32 citations
• Multitask Learning for Protein Subcellular Location Prediction (2011) — 81 citations
• Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study (2010) — 85 citations
• FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection (2021) — 312 citations
• Graph Random Neural Networks for Semi-Supervised Learning on Graphs (2020) — 513 citations
• Federated Deep Reinforcement Learning (2019) — 106 citations
• Federated Reinforcement Learning (2019) — 84 citations