Research map / Architectures and efficiency / Attention and transformers
On Expressive Power of Floating-Point Transformers
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
- Transformers Can Do Arithmetic with the Right Embeddings (NeurIPS 2024 · similarity 0.85)
- Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions (ICML 2025 · similarity 0.85)
- Exact Expressive Power of Transformers with Padding (NeurIPS 2025 · similarity 0.84)
- Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure (NeurIPS 2024 · similarity 0.83)
- On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding (ICML 2025 · similarity 0.83)
- Fixed Universal Transformers (NeurIPS 2026 · 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.
