Research map / Architectures and efficiency / Quantization
Optimal Brain Restoration for Joint Quantization and Sparsification of LLMs
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective (ICML 2025 · similarity 0.90)
- The Sparsity Whisperer (NeurIPS 2026 · similarity 0.90)
- Effective Interplay between Sparsity and Quantization: From Theory to Practice (ICLR 2025 · similarity 0.90)
- SqueezeLLM: Dense-and-Sparse Quantization (ICML 2024 · similarity 0.89)
- BiLLM: Pushing the Limit of Post-Training Quantization for LLMs (ICML 2024 · similarity 0.89)
- Compressing Large Language Models by Joint Sparsification and Quantization (ICML 2024 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
