Alex Ratner
Impact in
- Neurology top 10%
- Neuroinflammation and Neurodegeneration Mechanisms
- Developmental Neuroscience top 10%
Papers in
-
- Topic Modeling 5
- Text and Document Classification Technologies 2
- Advanced Graph Neural Networks 2
- Imbalanced Data Classification Techniques 2
- Machine Learning in Healthcare 2
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- Data Quality and Management 3
- Co-authors
- Daniel R. Hochbaum (1 shared paper)Rebeca Borges-Monroy (1 shared paper)Michael E. Greenberg (1 shared paper)Rapolas Žilionis (1 shared paper)Bernardo Luis Sabatini (1 shared paper)Keiramarie Robertson (1 shared paper)M. Aurel Nagy (1 shared paper)Marcelo Cicconet (1 shared paper)
- Journals
- Communications of the ACM (2 papers)The VLDB Journal (1 paper)Nature Neuroscience (1 paper)ACM SIGMOD Record (1 paper)Nature Communications (1 paper)
- Partner nations
- United StatesIsraelSwitzerland
In The Last Decade
Alex Ratner
14 papers receiving 934 citations
Alex Ratner's Hit Papers
Peers
Comparison fields: 5 of 123
- Neurology 107
- Developmental Neuroscience 44
- Artificial Intelligence 339
- Health Informatics 14
- Biophysics 33
Countries citing papers authored by Alex Ratner
This map shows the geographic impact of Alex Ratner'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 Alex Ratner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alex Ratner more than expected).
Fields of papers citing papers by Alex Ratner
This network shows the impact of papers produced by Alex Ratner. 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 Alex Ratner. The network helps show where Alex Ratner may publish in the future.
Co-authors
The 25 scholars most cited alongside Alex Ratner, 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 | 2017 | 358 | |
| 2 | Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes Hit paper breakdown → | 2023 | 157 |
| 3 | 2018 | 126 | |
| 4 | DeepDive: Declarative Knowledge Base Construction. | 2016 | 55 |
| 5 | 2016 | 53 | |
| 6 | 2019 | 53 | |
| 7 | 2017 | 47 | |
| 8 | 2016 | 37 | |
| 9 | 2018 | 25 | |
| 10 | 2019 | 14 | |
| 11 | 2020 | 12 | |
| 12 | 2019 | 11 | |
| 13 | 2018 | 5 | |
| 14 | 2018 | 1 |
About Alex Ratner
Alex Ratner is a scholar working on Artificial Intelligence, Management Science and Operations Research, Information Systems, Molecular Biology and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 954 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Data Quality and Management (3 papers), Text and Document Classification Technologies (2 papers), Biomedical Text Mining and Ontologies (2 papers), Advanced Graph Neural Networks (2 papers), Imbalanced Data Classification Techniques (2 papers), Machine Learning in Healthcare (2 papers) and Web Data Mining and Analysis (2 papers). The work is most often cited by research in Neurology (107 citations), Developmental Neuroscience (44 citations), Artificial Intelligence (339 citations), Health Informatics (14 citations) and Biophysics (33 citations). Alex Ratner has collaborated with scholars based in United States, Israel and Switzerland. Frequent co-authors include Daniel R. Hochbaum, Rebeca Borges-Monroy, Michael E. Greenberg, Rapolas Žilionis, Bernardo Luis Sabatini, Keiramarie Robertson, M. Aurel Nagy, Marcelo Cicconet, Lucas Cheadle and Allon Moshe Klein. Their work appears in journals such as Communications of the ACM, The VLDB Journal, Nature Neuroscience, ACM SIGMOD Record and Nature Communications.
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.