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Zhijian Liu

Zhijian Liu has 2 papers in our library, in 2020–2023, most of them on Language models & pretraining, GPU & parallel computing; often with Song Han, Chuang Gan, Haotian Tang.

2 papers in our library · 2020–2023

OpenAlex · ORCID · Wikidata · h-index 24 · 73 works · 6,163 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 292 times in our library · ⭐ Paper Stars ranking: #3190

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

🎓Massachusetts Institute of TechnologyCambridge · United States2018–2025 · 39 papers🏢NvidiaSanta Clara · United States2024–2026 · 10 papers🎓University of California San DiegoSan Diego · United States2025–2026 · 7 papers🏢NvidiaReading · United Kingdom2025 · 6 papers

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 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 2Research subjects and languages the work is about.GPU & parallel computing · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20231 paperLongLoRA: Efficient Fine-tuning of Long-Context Large Language Models20201 paperHAT: Hardware-Aware Transformers for Efficient Natural Language Processing

 Papers

The most cited first (OpenAlex and our library).

  1. HAT: Hardware-Aware Transformers for Efficient Natural Language Processing (2020 · 290 citations)
    Transformer models for machine translation are too slow on constrained devices. For example, Transformer-Big needs about 13G FLOPs and roughly 20 seconds on a Raspberry Pi to translate a 30-word sentence. The authors make two observations: FLOPs poorly predict real latency, and the architecture that runs fastest…
  2. LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models (2023 · 265 citations)
    Extending the context window of a pre-trained LLM is usually very costly, because self-attention cost grows quadratically with sequence length.

 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 292 times (of the 141,840 papers whose bibliographies we read); 24 citing papers with a page here →.

Updated

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