arXiv (All)
open access
Motion Equivariant Convolutional LSTM: When Motion Becomes a Gauge Symmetry
Objects move, cameras pan, and weather drifts with the wind: much of what changes in sequential data is motion. Motion changes where things are, not what they are, and is therefore a symmetry of the data. Yet standard recurrent networks are not equivariant to it: their memory stays put while the inp
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- On Making $\mathrm{SE}(2)$-Invariant Networks Optimal#1342641
- Reducing Symmetry Increase in Equivariant Neural Networks#447949
- PE-CSNet: An equivariant network architecture with learnable patch-based sparse representation#609141
- VLA-Rolling: Fewer Denoising Steps, Better Control via Adaptive Action Reuse#1389989
- COMET: Decoupled Distillation, Routing, and Capacity Control for Task-Agnostic Continual Vision--Language Learning#1387196
Record · ID 1413012
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive