Xuejun Liao

66 papers receiving 1.9k citations

Peers

Xuejun Liao
Comparison fields: 5 of 107
  • Artificial Intelligence 781
  • Computer Vision and Pattern Recognition 636
  • Computational Mechanics 612
  • Biomedical Engineering 420
  • Aerospace Engineering 203
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Citations per field
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Citations per year

Countries citing papers authored by Xuejun Liao

Since Specialization
Citations

This map shows the geographic impact of Xuejun Liao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xuejun Liao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xuejun Liao more than expected).

Fields of papers citing papers by Xuejun Liao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xuejun Liao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xuejun Liao. The network helps show where Xuejun Liao may publish in the future.

Co-authorship network of co-authors of Xuejun Liao

This figure shows the co-authorship network connecting the top 25 collaborators of Xuejun Liao. A scholar is included among the top collaborators of Xuejun Liao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xuejun Liao. Xuejun Liao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Solving DEC-POMDPs by Expectation Maximization of Value Function
4
2
Nonlinear statistical learning with truncated Gaussian graphical models
1
3
Linear feature encoding for reinforcement learning
4
4
Variational Gaussian Copula Inference
7
5
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices
4
6 110
7 142
8 22
9
Online expectation maximization for reinforcement learning in POMDPs
2
10
Integrated Non-Factorized Variational Inference
1
11 302
12
The Infinite Regionalized Policy Representation
10
13
Learning to Explore and Exploit in POMDPs
8
14
Multi-task Reinforcement Learning in Partially Observable Stochastic Environments
48
15 32
16
Point-based policy iteration
17
17 76
18
Incremental least squares policy iteration for POMDPs
2
19
Radial Basis Function Network for Multi-task Learning
14
20
Target Identification from Multi-Aspect High Range-Resolution Radar Signatures Using a Hidden Markov Model
8

About Xuejun Liao

Xuejun Liao is a scholar working on Acoustics and Ultrasonics, Artificial Intelligence and Statistics and Probability, having authored 67 papers that have together received 2.0k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (14 papers), Sparse and Compressive Sensing Techniques (12 papers) and Blind Source Separation Techniques (9 papers). The work is most often cited by research in Acoustics and Ultrasonics (87 citations), Computational Mathematics (20 citations) and Computer Vision and Pattern Recognition (636 citations). Xuejun Liao has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Lawrence Carin, Ya Xue, Balaji Krishnapuram, Patrick Llull, David J. Brady, Xin Yuan, Guillermo Sapiro, Jianbo Yang, P. Runkle and David Kittle. Their work appears in journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Geoscience and Remote Sensing.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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