Tomi Silander
Impact in
- Artificial Intelligence top 2%
- Bayesian Modeling and Causal Inference
- Bayesian Methods and Mixture Models
- Machine Learning and Algorithms
- AI-based Problem Solving and Planning
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- Data Quality and Management
Papers in
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- Bayesian Modeling and Causal Inference 25
- Machine Learning and Algorithms 7
- Machine Learning and Data Classification 7
- Bayesian Methods and Mixture Models 6
- Gaussian Processes and Bayesian Inference 5
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- Data Quality and Management 6
- Co-authors
- Petri Myllymäki (21 shared papers)Petri Kontkanen (15 shared papers)Kirsi Tirri (17 shared papers)Teemu Roos (6 shared papers)Tze-Yun Leong (8 shared papers)Peter Grünwald (2 shared papers)Edward Glover (1 shared paper)Kalyan Pande (1 shared paper)
In The Last Decade
Tomi Silander
55 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 134
- Artificial Intelligence 597
- Management Science and Operations Research 157
- Statistics and Probability 89
- Virology 44
- Signal Processing 94
Countries citing papers authored by Tomi Silander
This map shows the geographic impact of Tomi Silander'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 Tomi Silander with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomi Silander more than expected).
Fields of papers citing papers by Tomi Silander
This network shows the impact of papers produced by Tomi Silander. 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 Tomi Silander. The network helps show where Tomi Silander may publish in the future.
Co-authors
The 25 scholars most cited alongside Tomi Silander, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 62 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2001 | 185 | |
| 2 | 2012 | 163 | |
| 3 | 2002 | 131 | |
| 4 | 2006 | 60 | |
| 5 | 2012 | 47 | |
| 6 | Comparing Predictive Inference Methods for Discrete Domains | 1997 | 43 |
| 7 | 2000 | 41 | |
| 8 | Factorized normalized maximum likelihood criterion for learning Bayesian network structures | 2008 | 35 |
| 9 | 2010 | 29 | |
| 10 | 2005 | 28 | |
| 11 | 2006 | 27 | |
| 12 | Online Feature Selection for Model-based Reinforcement Learning | 2013 | 27 |
| 13 | 2008 | 26 | |
| 14 | 2004 | 25 | |
| 15 | 2000 | 24 | |
| 16 | 2023 | 22 | |
| 17 | 2015 | 16 | |
| 18 | 1998 | 16 | |
| 19 | 2013 | 13 | |
| 20 | 2006 | 13 |
About Tomi Silander
Tomi Silander is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Vision and Pattern Recognition, Statistics and Probability and Signal Processing, having authored 62 papers that have together received 1.2k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (25 papers), Machine Learning and Algorithms (7 papers), Machine Learning and Data Classification (7 papers), Data Quality and Management (6 papers), Data Management and Algorithms (6 papers), Bayesian Methods and Mixture Models (6 papers), Fault Detection and Control Systems (5 papers) and Gaussian Processes and Bayesian Inference (5 papers). The work is most often cited by research in Artificial Intelligence (597 citations), Management Science and Operations Research (157 citations), Statistics and Probability (89 citations), Virology (44 citations) and Signal Processing (94 citations). Tomi Silander has collaborated with scholars based in Finland, Singapore and France. Frequent co-authors include Petri Myllymäki, Petri Kontkanen, Kirsi Tirri, Teemu Roos, Tze-Yun Leong, Peter Grünwald, Edward Glover, Kalyan Pande, Mark Craven and Christopher A. Bradfield. Their work appears in journals such as International Journal of Approximate Reasoning, Bioinformatics, ACM SIGPLAN Notices, Journal of Machine Learning Research and Statistics and Computing.
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.