arXiv (All)
open access
Towards Diverse and Comprehensive Benchmarks for Mutual Information Estimation
Mutual information (MI) estimation is a central problem in machine learning and statistics; however, existing benchmarks typically evaluate estimators on simplified, low-dimensional distributions, leaving their performance on complex, realistic data largely unexplored. We address this gap with a com
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
- Decoding cell signaling via optimal transport and information theory#1488304
- Model-agnostic information transfer and fusion for classification with label noise#1413655
- Uncertainty Is Not Enough: Value-of-Information Routing for Mixtures of LoRA Experts#423962
- Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts#307040
- Training-Free Uncertainty Estimation for Embedding Models#1118355
Record · ID 1490569
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