David Alvarez-Melis
- Health Informatics top 10%
- Artificial Intelligence top 5%
- Topic Modeling 3
- Advanced Text Analysis Techniques 2
- Natural Language Processing Techniques 2
- Machine Learning and Data Classification 2
- Machine Learning and Algorithms 1
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- Handwritten Text Recognition Techniques 1
- General Social Sciences top 10%
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- Machine Learning in Materials Science 1
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- Software Engineering Research 1
- Co-authors
- Tommi JaakkolaMartin SaveskiTatsunori HashimotoStefanie JegelkaAndreas KrauseCharlotte BunneGuang-He LeeWengong Jin
- Partner nations
- United StatesSwitzerland
In The Last Decade
David Alvarez-Melis
8 papers receiving 343 citations
Peers
Comparison fields: 5 of 79
- Health Informatics 18
- Artificial Intelligence 300
- Computer Vision and Pattern Recognition 77
- General Social Sciences 8
- Computational Mathematics 1
Countries citing papers authored by David Alvarez-Melis
This map shows the geographic impact of David Alvarez-Melis'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 David Alvarez-Melis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Alvarez-Melis more than expected).
Fields of papers citing papers by David Alvarez-Melis
This network shows the impact of papers produced by David Alvarez-Melis. 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 David Alvarez-Melis. The network helps show where David Alvarez-Melis may publish in the future.
Co-authorship network
The 8 scholars most cited alongside David Alvarez-Melis, 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 | 2021 | 34 | |
| 2 | Towards Robust, Locally Linear Deep Networks | 2019 | 1 |
| 3 | Towards Optimal Transport with Global Invariances | 2019 | 4 |
| 4 | 2019 | 2 | |
| 5 | 2019 | 14 | |
| 6 | 2018 | 231 | |
| 7 | Tree-structured decoding with doubly-recurrent neural networks | 2017 | 42 |
| 8 | 2016 | 32 |
About David Alvarez-Melis
David Alvarez-Melis is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 8 papers that have together received 360 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Advanced Text Analysis Techniques (2 papers), Natural Language Processing Techniques (2 papers), Machine Learning and Data Classification (2 papers), Handwritten Text Recognition Techniques (1 paper), Machine Learning in Materials Science (1 paper), Software Engineering Research (1 paper) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Health Informatics (18 citations), Artificial Intelligence (300 citations) and Computer Vision and Pattern Recognition (77 citations). David Alvarez-Melis has collaborated with scholars based in United States and Switzerland. Frequent co-authors include Tommi Jaakkola, Martin Saveski, Tatsunori Hashimoto, Stefanie Jegelka, Andreas Krause, Charlotte Bunne, Guang-He Lee and Wengong Jin.
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.