arXiv (All)
open access
ibUMAP: Coherent and Scalable Field Evaluation for UMAP Optimization
UMAP achieves scalable layout optimization through stochastic negative sampling. However, this stochasticity can lead to unstable embeddings across reruns and downstream reuse, as the estimated repulsive forces depend on the ordering of sampling events. We present ibUMAP, a coherent field-based alte
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
- Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events#974452
- Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning#433626
- FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting#609091
- A Data Fusion Framework for Grounding Aerospace Surrogate Model via Experimental Wind-Tunnel Observations#927565
- Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps#1143687
Record · ID 1156717
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