Joe Suzuki

42 papers receiving 405 citations

Peers

Joe Suzuki
Comparison fields: 5 of 79
  • Artificial Intelligence 349
  • Computational Theory and Mathematics 116
  • Management Science and Operations Research 50
  • Computer Networks and Communications 44
  • Information Systems 37
Replace Thilo Mahnig with:
Thilo Mahnig France
Scott Davies United States
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Jan Lemeire Belgium
O. Uncu Canada
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Citations per field
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Countries citing papers authored by Joe Suzuki

Since Specialization
Citations

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

Fields of papers citing papers by Joe Suzuki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joe Suzuki

This figure shows the co-authorship network connecting the top 25 collaborators of Joe Suzuki. A scholar is included among the top collaborators of Joe Suzuki 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 Joe Suzuki. Joe Suzuki 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
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Branch and Bound for Regular Bayesian Network Structure Learing.
1
8 23
9
Discovering causal structures in binary exclusive-or skew acyclic models
3
10 0
11 3
12 3
13
A Fast Jacobian Group Arithmetic Scheme for Algebraic Curve Cryptography
1
14
Learning Bayesian Belief Networks Based on the MDL Principle : An Efficient Algorithm Using the Branch and Bound Technique
47
15
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties
37
16
A Relationship between Contex Tree Weighting and General Model Weighting Techniques for Tree Sources
0
17
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique.
28
18
Some Notes on Universal Noiseless Coding
6
19
Evaluations for Estimation of an Information Source Based on State Decomposition
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20
A Markov Chain Analysis on A Genetic Algorithm
38

About Joe Suzuki

Joe Suzuki is a scholar working on Statistics and Probability, Artificial Intelligence and Computational Theory and Mathematics, having authored 52 papers that have together received 449 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (23 papers), Machine Learning and Algorithms (10 papers) and Statistical Methods and Inference (8 papers). The work is most often cited by research in Artificial Intelligence (349 citations), Computational Theory and Mathematics (116 citations) and Statistics and Probability (35 citations). Joe Suzuki has collaborated with scholars based in Japan, United States and Australia. Frequent co-authors include Jun Kawahara, Shohei Shimizu, Boris Ryabko, Yoshinobu Kawahara, Flemming Topsøe, Akihiro Yamamoto, Takashi Washio, Yoshio Tanaka, Hiroki Sugano and Kuang‐Yao Lee. Their work appears in journals such as Scientific Reports, IEEE Transactions on Information Theory and IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics).

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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