arXiv (All)
open access
Minkowski Attractor Networks: Closed-Form Hyperbolic Flows for Visual Representations
Geometric representation learning predominantly scaffolds representations onto flat Euclidean subspaces or compact product tori ($\mathbb{T}^K$). However, flat manifolds possess vanishing curvature and polynomial volume growth, inherently suffering from metric distortion when embedding multi-scale,
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
- MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception#1118877
- RW-Flow: One-Step Generation on Compact Manifolds via Wasserstein Gradient Flows#1144238
- Early Learning Shapes Later Directions Of Representation Change In Continual Learning#1130923
- LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference.#301659
- ViscoReg: Neural Signed Distance Functions via Viscosity Solutions#1130158
Record · ID 1142125
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