Research map / Architectures and efficiency / Quantization
A Block Minifloat Representation for Training Deep Neural Networks
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
- Training DNNs with Hybrid Block Floating Point (NeurIPS 2018 · similarity 0.90)
- Training Deep Neural Networks with 8-bit Floating Point Numbers (NeurIPS 2018 · similarity 0.89)
- Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions (ICML 2025 · similarity 0.85)
- Scaling Laws for Floating–Point Quantization Training (ICML 2025 · similarity 0.84)
- Collage: Light-Weight Low-Precision Strategy for LLM Training (ICML 2024 · similarity 0.84)
- G-Net: A Provably Easy Construction of High-Accuracy Random Binary Neural Networks (NeurIPS 2025 · similarity 0.83)
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.
