Valentin Liévin
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
Papers in
-
- Topic Modeling 2
- Speech Recognition and Synthesis 1
- Natural Language Processing Techniques 1
-
- Biomedical Text Mining and Ontologies 1
- Co-authors
- Ole Winther (6 shared papers)Christoffer Hother (2 shared papers)Lars Maaløe (2 shared papers)M. Fraccaro (1 shared paper)Simon Ott (1 shared paper)Milad Moradi (1 shared paper)Matthias Samwald (1 shared paper)Kaisa Elomaa (1 shared paper)
- Journals
- Scientific Data (1 paper)Patterns (1 paper)Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (2 papers)PLOS Digital Health (1 paper)
- Partner nations
- DenmarkAustriaSwitzerland
In The Last Decade
Valentin Liévin
6 papers receiving 179 citations
Valentin Liévin's Hit Papers
Peers
Comparison fields: 5 of 53
- Health Informatics 53
- Family Practice 6
- Artificial Intelligence 83
- Health Information Management 6
- Computer Vision and Pattern Recognition 26
Countries citing papers authored by Valentin Liévin
This map shows the geographic impact of Valentin Liévin'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 Valentin Liévin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Valentin Liévin more than expected).
Fields of papers citing papers by Valentin Liévin
This network shows the impact of papers produced by Valentin Liévin. 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 Valentin Liévin. The network helps show where Valentin Liévin may publish in the future.
Co-authors
The 10 scholars most cited alongside Valentin Liévin, 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 | Can large language models reason about medical questions? Hit paper breakdown → | 2024 | 113 |
| 2 | 2023 | 33 | |
| 3 | BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling | 2019 | 28 |
| 4 | 2023 | 6 | |
| 5 | Towards Hierarchical Discrete Variational Autoencoders | 2019 | 2 |
| 6 | Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds. | 2020 | 1 |
About Valentin Liévin
Valentin Liévin is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Health Informatics and Signal Processing, having authored 6 papers that have together received 183 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Artificial Intelligence in Healthcare and Education (1 paper), Genomics and Rare Diseases (1 paper), Speech Recognition and Synthesis (1 paper), Biomedical Text Mining and Ontologies (1 paper), Natural Language Processing Techniques (1 paper), Music and Audio Processing (1 paper) and Statistical Methods and Inference (1 paper). The work is most often cited by research in Health Informatics (53 citations), Family Practice (6 citations), Artificial Intelligence (83 citations), Health Information Management (6 citations) and Computer Vision and Pattern Recognition (26 citations). Valentin Liévin has collaborated with scholars based in Denmark, Austria and Switzerland. Frequent co-authors include Ole Winther, Christoffer Hother, Lars Maaløe, M. Fraccaro, Simon Ott, Milad Moradi, Matthias Samwald, Kaisa Elomaa, Deborah Elstein and Allan M. Lund. Their work appears in journals such as Scientific Data, Patterns, Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) and PLOS Digital Health.
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.