Tailin Liang

888 citations
4 papers · 513 indexed · 1 hit paper · h-index 2
Co-authors
Lei WangShi Shao-boJohn GlossnerXiaodong ZhangMayan MoudgillWei Huang
Topics
Advanced Neural Network Applications (3 papers)Tensor decomposition and applications (2 papers)Parallel Computing and Optimization Techniques (2 papers)
Journals
NeurocomputingACM Transactions on Embedded Computing Systems2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Partner nations
China

In The Last Decade

Tailin Liang

2 papers receiving 488 citations

Hit Papers

Pruning and quantization for deep neural network accelera...20212026202220242021100200300400500

Peers

Tailin Liang
Comparison fields: 5 of 87
  • Computer Vision and Pattern Recognition 247
  • Artificial Intelligence 230
  • Electrical and Electronic Engineering 96
  • Computer Networks and Communications 64
  • Signal Processing 40
Replace Shi Shao-bo with:
Shi Shao-bo China
Geng Yuan United States
Tinghuan Chen China
Yiming Hu China
Chao Zhu China
Cong Leng China
Mingxing Duan China
Tailin Liang relative to Shi Shao-bo China Shi Shao-bo's profile →
Citations per field
00.5×1.5×
Shi Shao-bo · 1×
Citations per year

Countries citing papers authored by Tailin Liang

Since Specialization
Citations

This map shows the geographic impact of Tailin Liang'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 Tailin Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tailin Liang more than expected).

Fields of papers citing papers by Tailin Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tailin Liang. 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 Tailin Liang. The network helps show where Tailin Liang may publish in the future.

Co-authorship network of co-authors of Tailin Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Tailin Liang. A scholar is included among the top collaborators of Tailin Liang 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 Tailin Liang. Tailin Liang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

4 of 4 papers shown
#WorkIndexed citations
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2 0
3
Pruning and quantization for deep neural network acceleration: A surveybreakdown →
508
4 4

About Tailin Liang

Tailin Liang is a scholar working on Computational Mathematics, Hardware and Architecture and Computer Vision and Pattern Recognition, having authored 4 papers that have together received 513 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Tensor decomposition and applications (2 papers) and Parallel Computing and Optimization Techniques (2 papers). The work is most often cited by research in Computational Mathematics (9 citations), Computer Vision and Pattern Recognition (247 citations) and Artificial Intelligence (230 citations). Tailin Liang has collaborated with scholars based in China. Frequent co-authors include Lei Wang, Shi Shao-bo, John Glossner, Xiaodong Zhang, Mayan Moudgill and Wei Huang. Their work appears in journals such as Neurocomputing, ACM Transactions on Embedded Computing Systems and 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE).

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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