Research map / Architectures and efficiency / Attention and transformers
ConvT3: Structured State Kernels for Convolutional State Space 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
- Convolutional State Space Models for Long-Range Spatiotemporal Modeling (NeurIPS 2023 · similarity 0.92)
- From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics (ICLR 2025 · similarity 0.86)
- Spatial-Mamba: Effective Visual State Space Models via Structure-Aware State Fusion (ICLR 2025 · similarity 0.85)
- Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models (NeurIPS 2025 · similarity 0.85)
- Theoretical Foundations of Deep Selective State-Space Models (NeurIPS 2024 · similarity 0.85)
- Exploiting Temporal State Space Sharing for Video Semantic Segmentation (CVPR 2025 · 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.
