Wei Zhang
Wei Zhang has 2 papers in our library, in 2020–2021, most of them on Efficient models & compression; often with Lifeng Shang, Lu Hou, Qun Liu.
OpenAlex · ORCID · h-index 17 · 214 works · 1,252 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 139 times in our library · ⭐ Paper Stars ranking: #3841
Affiliations
🎓Space Engineering UniversityBeijing ·
China2019–2025 · 8 papers🏛️Chinese Academy of SciencesBeijing ·
China2007–2025 · 7 papers🎓Qilu University of TechnologyJinan ·
China2023–2025 · 3 papers🎓Shandong Academy of SciencesJinan ·
China2023–2025 · 3 papers🔬National Supercomputing Center in JinanJinan ·
China2024–2025 · 2 papers🎓Northeastern UniversityBoston ·
United States2025 · 2 papers🎓University of Chinese Academy of SciencesBeijing ·
China2019–2025 · 2 papers🎓Anhui University of Traditional Chinese MedicineHefei ·
China2025 · 1 paper🎓Northwest A&F UniversityYangling ·
China2025 · 1 paper🎓Northwest UniversityXi'an ·
China2025 · 1 paper
Concepts, shelves and topics
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works
🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 2 works
Works by year
2020s
20211 paperBinaryBERT: Pushing the Limit of BERT Quantization20201 paperTernaryBERT: Distillation-aware Ultra-low Bit BERT
Papers
- TernaryBERT: Distillation-aware Ultra-low Bit BERT (2020 · 213 citations)
Fine-tuned BERT models are large: BERT-base has 109M parameters, over 400 MB in 32-bit floats. This makes them hard to run on phones and other constrained hardware. Earlier quantization work kept accuracy at 8 bits. Going lower usually needed mixed precision, which some hardware handles poorly, or product… - BinaryBERT: Pushing the Limit of BERT Quantization (2021 · 165 citations)
Large pre-trained language models are hard to run on edge devices, and quantization shrinks them without redesigning the architecture.
Co-authors here
- Lifeng Shang ×2
- Lu Hou ×2
- Qun Liu ×2
- Xin Jiang ×2
- Michael R. Lyu
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2021
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2020
Cited by
Updated
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