arXiv (All)
open access
PRQuant: Permutation Residual Quantization for Low-Overhead Inference
Accuracy of Low-bit quantization of linear layers is often dominated by a small number of outliers. Although existing methods, such as smoothing, rotation, or residual-based approaches, may mitigate this problem, they often introduce new accuracy bottlenecks to weights. Besides, most of these techni
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Elastic Token Compression for Pixel-Space Diffusion Transformers#794089
- Elastic Token Compression for Pixel-Space Diffusion Transformers#790070
- Lynx: Progressive Speculative Quantization for accelerating KV Transfer in Long-Context Inference#332493
- OSC: Hardware Efficient W4A4 Quantization via Outlier Separation in Channel Dimension#13095
- Murmurations, Mestre--Nagao sums, and Convolutional Neural Networks for elliptic curves#1104984
Record · ID 1036790
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive