arXiv (All)
open access
Inferring Concepts from Noisy Examples in Hopfield-like Neural Networks
We study a variant of the pseudo-inverse learning rule for Hopfield-like Neural Networks, which allows the network to infer archetypal concepts on the basis of a limited number of examples. The key novelty is that the new learning rule compensates for correlations between examples at the population
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
- Few-Step Generation via Data-Space Iteration#1249526
- Interpretable AI with Local Distillation#482112
- All You Need Is Correlation#110974
- sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flows#793262
- sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flows#789258
Record · ID 1354118
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