Alex Ratner
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning and Data Classification
- Semantic Web and Ontologies
- Advanced Graph Neural Networks
- Data Stream Mining Techniques
Papers in
-
- Topic Modeling 6
- Text and Document Classification Technologies 2
- Imbalanced Data Classification Techniques 2
- Advanced Graph Neural Networks 2
- Data Stream Mining Techniques 1
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- Biomedical Text Mining and Ontologies 2
- Co-authors
- Christopher De (4 shared papers)Christopher Ré (5 shared papers)Sen Wu (4 shared papers)Jaeho Shin (3 shared papers)Feiran Wang (2 shared papers)Chih‐Kuan Yeh (1 shared paper)Ranjay Krishna (1 shared paper)Tomas Pfister (1 shared paper)
- Journals
- Communications of the ACM (2 papers)ACM SIGMOD Record (1 paper)Queue (1 paper)The VLDB Journal (1 paper)BMC Bioinformatics (1 paper)
- Partner nations
- United StatesSwitzerlandIsrael
In The Last Decade
Alex Ratner
11 papers receiving 358 citations
Alex Ratner's Hit Papers
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 278
- Health Informatics 11
- Management Science and Operations Research 59
- Information Systems and Management 21
- Information Systems 64
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 | Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes Hit paper breakdown → | 2023 | 108 |
| 2 | DeepDive: Declarative Knowledge Base Construction. | 2016 | 46 |
| 3 | 2019 | 46 | |
| 4 | 2016 | 42 | |
| 5 | 2017 | 41 | |
| 6 | 2016 | 33 | |
| 7 | 2018 | 22 | |
| 8 | 2020 | 15 | |
| 9 | 2019 | 13 | |
| 10 | 2018 | 5 | |
| 11 | 2018 | 1 |
About Alex Ratner
Alex Ratner is a scholar working on Artificial Intelligence, Molecular Biology, Information Systems, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 372 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Data Quality and Management (3 papers), Biomedical Text Mining and Ontologies (2 papers), Text and Document Classification Technologies (2 papers), Imbalanced Data Classification Techniques (2 papers), Advanced Graph Neural Networks (2 papers), Web Data Mining and Analysis (2 papers) and Data Stream Mining Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (278 citations), Health Informatics (11 citations), Management Science and Operations Research (59 citations), Information Systems and Management (21 citations) and Information Systems (64 citations). Alex Ratner has collaborated with scholars based in United States, Switzerland and Israel. Frequent co-authors include Christopher De, Christopher Ré, Sen Wu, Jaeho Shin, Feiran Wang, Chih‐Kuan Yeh, Ranjay Krishna, Tomas Pfister, Cheng-Yu Hsieh and Chunliang Li. Their work appears in journals such as Communications of the ACM, ACM SIGMOD Record, Queue, The VLDB Journal and BMC Bioinformatics.
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.