Juan Pino
- Artificial Intelligence top 1%
- Signal Processing top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems
- Experimental and Cognitive Psychology
- Topics
- Natural Language Processing Techniques (45 papers)Topic Modeling (29 papers)Speech Recognition and Synthesis (26 papers)
- Journals
- Applied Physics LettersCommunications in computer and information scienceEmpirical Methods in Natural Language Processing
- Partner nations
- United StatesIsraelUnited Kingdom
In The Last Decade
Juan Pino
51 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 61
- Artificial Intelligence 1.2k
- Signal Processing 289
- Computer Vision and Pattern Recognition 157
- Information Systems 33
- Experimental and Cognitive Psychology 32
Countries citing papers authored by Juan Pino
This map shows the geographic impact of Juan Pino'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 Juan Pino with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juan Pino more than expected).
Fields of papers citing papers by Juan Pino
This network shows the impact of papers produced by Juan Pino. 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 Juan Pino. The network helps show where Juan Pino may publish in the future.
Co-authorship network of co-authors of Juan Pino
This figure shows the co-authorship network connecting the top 25 collaborators of Juan Pino. A scholar is included among the top collaborators of Juan Pino based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Juan Pino. Juan Pino is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 9 | |
| 3 | 11 | |
| 4 | 18 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 12 | |
| 8 | 24 | |
| 9 | 8 | |
| 10 | 23 | |
| 11 | Cross-Modal Transfer Learning for Multilingual Speech-to-Text Translation. | 2 |
| 12 | Monotonic Multihead Attention | 22 |
| 13 | CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus | 27 |
| 14 | 58 | |
| 15 | The University of Cambridge Russian-English System at WMT13 | 6 |
| 16 | Hierarchical Phrase-Based Translation Grammars Extracted from Alignment Posterior Probabilities | 9 |
| 17 | The CUED HiFST System for the WMT10 Translation Shared Task | 2 |
| 18 | 12 | |
| 19 | 17 | |
| 20 | Measuring Hint Level in Open Cloze Questions | 6 |
About Juan Pino
Juan Pino is a scholar working on Artificial Intelligence, Signal Processing and Developmental and Educational Psychology, having authored 51 papers that have together received 1.3k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (45 papers), Topic Modeling (29 papers) and Speech Recognition and Synthesis (26 papers). The work is most often cited by research in Artificial Intelligence (1.2k citations), Signal Processing (289 citations) and Computer Vision and Pattern Recognition (157 citations). Juan Pino has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Changhan Wang, Jiatao Gu, Xutai Ma, Yun Tang, Michael Auli, Alexei Baevski, Alexis Conneau, Anne Wu, Philipp Koehn and Qiantong Xu. Their work appears in journals such as Applied Physics Letters, Communications in computer and information science and Empirical Methods in Natural Language Processing.
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.