Xuejun Liao

Artificial Intelligence · 2005–2016

A researcher in the Science corpus: 15 papers; trusts 109 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #7,052 of 35,345 by trust here, #10,065 by citations
• Representation & Self-Supervised Learning — 42 citations into it
• Probabilistic & Bayesian Methods — 38 citations into it; #364 of 1,525 by trust here, #265 by citations
• Statistics and Probability — 37 citations into it; #7,267 of 12,932 by trust here, #10,855 by citations
• Statistical Learning Theory — 20 citations into it
• Reinforcement Learning — 13 citations into it; #944 of 2,242 by trust here, #1,728 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #14,590 of 21,575 by trust here, #14,822 by citations
• Computational Mechanics — 11 citations into it; #9,192 of 10,001 by trust here, #7,884 by citations

Whom Xuejun Liao trusts overall

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

1. Michael I. Jordan
2. David John Cameron MacKay
3. Geoffrey E. Hinton
4. Bart J. Bakker
5. Zoubin Ghahramani
6. Radford M. Neal
7. Matthew J. Beal
8. Theodoros Evgeniou
9. Alan Genz
10. R. Caruana

Who trusts Xuejun Liao overall

1. Yu Zhang
2. Qiang Yang
3. Yanbo Xu
4. Gabriella Csurka
5. Lawrence Carin
6. Yu Zhang
7. Dit-Yan Yeung
8. Hal Daumé Iii
9. Mohammad Rostami
10. Jiayu Zhou

Papers from Xuejun Liao's perspective

Ordered by how strongly the researchers Xuejun Liao 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Logistic regression with an auxiliary data source (2005) — 226 citations
• Semisupervised Multitask Learning (2009) — 93 citations
• Multi-task Reinforcement Learning in Partially Observable Stochastic Environments (2009) — 108 citations
• Quadratically gated mixture of experts for incomplete data classification (2007) — 44 citations
• Coded aperture compressive temporal imaging (2013) — 412 citations
• Stick-Breaking Policy Learning in Dec-POMDPs (2015) — 31 citations
• Nonlinear Statistical Learning with Truncated Gaussian Graphical Models (2016) — 12 citations
• Integrated Non-Factorized Variational Inference (2013) — 10 citations
• Cross-Domain Multitask Learning with Latent Probit Models (2012) — 18 citations
• Nonparametric Bayesian feature selection for multi-task learning (2011) — 3 citations
• Active learning for semi-supervised multi-task learning (2009) — 13 citations
• Semi-Supervised Multitask Learning (2007) — 95 citations
• Multi-Task Learning for Classification with Dirichlet Process Priors (2007) — 621 citations
• Radial Basis Function Network for Multitask Learning (2005)
• Radial Basis Function Network for Multi-task Learning (2005) — 35 citations