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
Cites
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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