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Ricardo Henao

Ricardo Henao has 2 papers in our library, in 2018; often with Chunyuan Li, Dinghan Shen, Guoyin Wang.

2 papers in our library · 2018

OpenAlex · ORCID · Wikidata · h-index 44 · 328 works · 7,474 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 142 citations in our library · 1 more in our library without a page yet · ⭐ Paper Star rank #2460

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Duke UniversityDurham · United States2012–2026 · 258 papersEmployment 2017–2022current🏥Duke Medical CenterDurham · United States2013–2026 · 48 paperscurrent🎓King Abdullah University of Science and TechnologyJeddah · Saudi Arabia2020–2026 · 20 paperscurrent🎓Technical University of DenmarkKongens Lyngby · Denmark2008–2017 · 8 papersEducation 2008–2011Employment🎓University of CopenhagenCopenhagen · Denmark2009–2015 · 7 papers🎓Technological University of PereiraPereira · Colombia2005–2008 · 4 papersEmployment 2005–2008🏥Duke University HospitalDurham · United States2022–2023 · 2 papers🎓Universidad Nacional de ColombiaBogotá · Colombia1997–2011 · 1 paperEducation 1997–2002Education 2002–2004

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4 Artificial Intelligence · 2 works Arts and Humanities · 2 works Computer Science · 2 works Language and Linguistics · 2 works

 Works by year

2010s

20182 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Joint Embedding of Words and Labels for Text Classification (2018 · 464 citations)
    Most neural text classifiers use class labels only at the final prediction step, so labels do not shape how words or documents are represented.
  2. Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (2018 · 385 citations)
    Text-encoding research often relies on recurrent or convolutional compositional functions. These add many parameters and slow down training, yet their real benefit over plain word embeddings had not been measured carefully. The authors run a task-by-task comparison between LSTM and CNN encoders and Simple…

Also in our library

1 more work of theirs is in our library without a page here yet; it will appear as we publish them.

 Co-authors here

6 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 142 times (of the 141,840 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated

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