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Tianyang Wang

Tianyang Wang has 2 papers in our library, in 2026, most of them on Efficient models & compression; often with Hao Xu, Xi Xiao, Chen Liu.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 11 · 42 works · 527 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 7 times in our library · ⭐ Paper Stars ranking: #10192

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

🎓Beijing University of TechnologyBeijing · China2024–2026 · 3 paperscurrent🏢CRRCBeijing · China2026 · 1 papercurrent🎓Xidian UniversityXi'an · China2026 · 1 papercurrent🎓Tianjin UniversityTianjin · China2015–2026 · 19 papers🔬Collaborative Innovation Center of Chemical Science and Engineering TianjinTianjin · China2015–2020 · 6 papers🎓University of BonnBonn · Germany2018–2025 · 5 papers🎓National University of SingaporeSingapore · Singapore2023–2025 · 3 papers🎓Harbin Normal UniversityHarbin · China2022–2023 · 3 papers🎓Nankai UniversityTianjin · China2026 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Latent semantic indexing · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance (2026 · 5 citations)
    AdaLoRA improves on LoRA by giving different weight matrices different ranks. It does this by expressing each update in SVD form (P, Λ, Q) and pruning unimportant singular values. The cost is slow convergence and heavy computation during training. HyperAdaLoRA addresses this. Instead of updating P, Λ and Q directly…
  2. Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation (2026 · 3 citations)
    The paper argues that LoRA has two weaknesses. First, it spends its small rank budget equally on every low-rank direction, even though many directions carry noise; the authors call this semantic drift. Second, it tunes each layer in isolation, so updates in neighbouring layers can point in unrelated directions; they…

 Co-authors here

7 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 7 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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