CoDeQ: End-to-End Joint Model Compression with Dead-Zone Quantizer for High-Sparsity and Low-Precision Networks
Published:Dec 15, 2025 04:53
•1 min read
•ArXiv
Analysis
This article introduces CoDeQ, a method for compressing neural networks. The focus is on achieving high sparsity and low precision, likely to improve efficiency and reduce computational costs. The use of a dead-zone quantizer suggests an approach to handle the trade-off between compression and accuracy. The source being ArXiv indicates this is a research paper, suggesting a technical and potentially complex subject matter.
Key Takeaways
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