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Hao Tan

Hao Tan has 2 papers in our library, in 2019, most of them on Language models & pretraining; often with Mohit Bansal, Licheng Yu.

2 papers in our library · 2019

OpenAlex · ORCID · h-index 20 · 50 works · 4,076 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 540 citations in our library · ⭐ Paper Star rank #917

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

🎓Xiangtan UniversityXiangtan · China2026 · 1 papercurrent🎓University of North Carolina at Chapel HillChapel Hill · United States2017–2022 · 20 papers🏢Adobe SystemsSan Jose · United States2019–2025 · 6 papers🎓Sichuan UniversityChengdu · China2025 · 3 papers🔬State Key Laboratory of Hydraulics and Mountain River EngineeringChengdu2025 · 3 papers🎓Southern University of Science and TechnologyShenzhen · China2020–2021 · 3 papers🏢Dongfeng Motor GroupWuhan · China2017–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions 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.

💡 Concepts 1 Transformer and attention · 1 work

📚 Shelves 5 Arts and Humanities · 2 works Computer Science · 2 works Artificial Intelligence · 2 works Computer Vision and Pattern Recognition · 2 works Language and Linguistics · 2 works

🧭 Topics 1 Language models & pretraining · 1 work

 Works by year

2010s

20192 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LXMERT: Learning Cross-Modality Encoder Representations from Transformers (2019 · 2,730 citations)
    LXMERT addresses the problem of building a general pre-trained backbone for joint vision-and-language reasoning.
  2. Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout (2019 · 347 citations)
    The paper addresses instruction-guided navigation in the Room-to-Room (R2R) task, which is built on real Matterport3D indoor scans. Agents for this task usually lose much of their accuracy in houses they never saw during training. The authors propose a two-stage training scheme.

 Co-authors here

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

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 540 times (of the 141,840 papers whose bibliographies we read); 42 citing papers with a page here →.

Updated

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