Research map / Learning theory and algorithms / Sample complexity
Model-Preserving Adaptive Rounding
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
- GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling (NeurIPS 2026 · similarity 0.87)
- Optimal and Approximate Adaptive Stochastic Quantization (NeurIPS 2024 · similarity 0.87)
- QBB: Quantization with Binary Bases for LLMs (NeurIPS 2024 · similarity 0.86)
- Understanding the Unfairness in Network Quantization (ICML 2025 · similarity 0.85)
- LOTION: Smoothing the Optimization Landscape for Quantized Training (NeurIPS 2026 · similarity 0.85)
- A2Q+: Improving Accumulator-Aware Weight Quantization (ICML 2024 · similarity 0.85)
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.