arXiv (All)
open access
Gromov-Wasserstein at Scale, Beyond Squared Norms
A fundamental challenge in data science is to match disparate point sets with each other. While optimal transport efficiently minimizes point displacements under a bijectivity constraint, it is inherently sensitive to rotations. Conversely, minimizing distortions via the Gromov-Wasserstein (GW) fram
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
- Cluster-Aware Matching via Laplacian Optimal Transport#381722
- Disentangling Marginal Capacity from Geometry in Optimal-Transport Label Transfer#1325749
- Towards optimal transport with global invariances#1346210
- Toward Calibrated Mixture-of-Experts Under Distribution Shift#290563
- Diverse via bounded Agreement: Geometric Regularization for Multimodal Fusion#792698
Record · ID 1201982
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