Ruiting Lian
Impact in
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- Neural dynamics and brain function
- Artificial Intelligence top 10%
- AI-based Problem Solving and Planning
- Neural Networks and Applications
- Cognitive Science and Mapping
- Reinforcement Learning in Robotics
Papers in
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- AI-based Problem Solving and Planning 4
- Natural Language Processing Techniques 2
- Reinforcement Learning in Robotics 2
- Topic Modeling 2
- Cognitive Science and Mapping 1
- Evolutionary Algorithms and Applications 1
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- Computability, Logic, AI Algorithms 4
- Co-authors
- Ben Goertzel (8 shared papers)Hugo de Garis (3 shared papers)Itamar Arel (2 shared papers)Shuo Chen (1 shared paper)Michael S. Ross (2 shared papers)Linas Vepštas (2 shared papers)Fabrício Alves Barbosa da Silva (1 shared paper)Dingjie Wang (1 shared paper)
- Journals
- Neurocomputing (3 papers)Communication in Statistics- Theory and Methods (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
In The Last Decade
Ruiting Lian
8 papers receiving 202 citations
Peers
Comparison fields: 5 of 47
- Cognitive Neuroscience 103
- Artificial Intelligence 114
- Computational Theory and Mathematics 33
- Cellular and Molecular Neuroscience 31
- Electrical and Electronic Engineering 67
Countries citing papers authored by Ruiting Lian
This map shows the geographic impact of Ruiting Lian'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 Ruiting Lian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruiting Lian more than expected).
Fields of papers citing papers by Ruiting Lian
This network shows the impact of papers produced by Ruiting Lian. 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 Ruiting Lian. The network helps show where Ruiting Lian may publish in the future.
Co-authors
The 13 scholars most cited alongside Ruiting Lian, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 104 | |
| 2 | 2010 | 93 | |
| 3 | 2013 | 9 | |
| 4 | World survey of artificial brains, Part II: Biologically inspired cognitive architectures | 2010 | 7 |
| 5 | 2010 | 6 | |
| 6 | 2010 | 6 | |
| 7 | 2011 | 2 | |
| 8 | 2019 | 1 |
About Ruiting Lian
Ruiting Lian is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Cognitive Neuroscience, Management Science and Operations Research and Electrical and Electronic Engineering, having authored 8 papers that have together received 228 indexed citations. Recurring topics across this work include Computability, Logic, AI Algorithms (4 papers), AI-based Problem Solving and Planning (4 papers), Natural Language Processing Techniques (2 papers), Reinforcement Learning in Robotics (2 papers), Topic Modeling (2 papers), Cognitive Science and Mapping (1 paper), Neurobiology of Language and Bilingualism (1 paper) and Evolutionary Algorithms and Applications (1 paper). The work is most often cited by research in Cognitive Neuroscience (103 citations), Artificial Intelligence (114 citations), Computational Theory and Mathematics (33 citations), Cellular and Molecular Neuroscience (31 citations) and Electrical and Electronic Engineering (67 citations). Ruiting Lian has collaborated with scholars based in China, Hong Kong and France. Frequent co-authors include Ben Goertzel, Hugo de Garis, Itamar Arel, Shuo Chen, Michael S. Ross, Linas Vepštas, Fabrício Alves Barbosa da Silva, Dingjie Wang, Shuo Chen and Gino Yu. Their work appears in journals such as Neurocomputing, Communication in Statistics- Theory and Methods and Proceedings of the AAAI Conference on Artificial Intelligence.
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.