The DOI matches its Crossref record.
Deep Compression of Pre-Trained Transformer Models
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Wang et al. [127] propose a technique where quantization and pruning steps are carried out in the same finetuning step on downstream tasks using the pretrained models.”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [127]
In the citing paper
“Their technique achieves 16x speedup over the unoptimized transformer models across language and vision tasks by employing 4-bit quantization and 50% weight pruning.”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [127]
Citations
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Still being checked.
Cited by
Sorted by citations
A Survey of Techniques for Optimizing Transformer Inference
Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154
Metadata
- Authors
- Wang, N., Liu, C.-C. (., Venkataramani, S., Sen, S., Chen, C.-Y., Maghraoui, K. E., Srinivasan, V. (., & Chang, L.
- Venue
- Advances in Neural Information Processing Systems 35
- Published
- 2022
- Pages
- 14140-14154
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133191540
- Cited by
- 5
- References
- 0
- Citations by year
- 2026: 5
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Keywords
- model compression, transformer models, pre-trained models, knowledge distillation, neural network pruning, inference efficiency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep Compression of Pre-Trained Transformer Models. https://app.cochat.ai/papers/10.52202/068431-1028
- The paper
- Wang, N., Liu, C.-C. (., Venkataramani, S., Sen, S., Chen, C.-Y., Maghraoui, K. E., Srinivasan, V. (., & Chang, L. (2022). Deep Compression of Pre-Trained Transformer Models. Advances in Neural Information Processing Systems 35. https://doi.org/10.52202/068431-1028
