Research map / Architectures and efficiency / Attention and transformers
On the "Induction Bias" in Sequence Models
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
- When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective (NeurIPS 2025 · similarity 0.89)
- (How) Do Language Models Track State? (ICML 2025 · similarity 0.89)
- Understanding and Improving Length Generalization in Recurrent Models (ICML 2025 · similarity 0.88)
- Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors (ICLR 2024 · similarity 0.88)
- Separations in the Representational Capabilities of Transformers and Recurrent Architectures (NeurIPS 2024 · similarity 0.88)
- Beyond Attention: Breaking the Limits of Transformer Context Length with Recurrent Memory (AAAI 2024 · similarity 0.88)
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.
