arXiv (All)
open access
Approximation in Contrastive Representation Learning
Contrastive representation learning (CRL) is a fundamental paradigm for learning transferable representations. Recent work identifies its population target as the pointwise mutual information (PMI) $\log\frac{p(x,y)}{p_X(x)p_Y(y)}$ between two modalities $X,Y$, where $p$ is the joint density
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
- Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback#1144423
- The Trade-off Between Covariate Dependence and Latent Structure in Representation Learning#620651
- The Trade-off Between Covariate Dependence and Latent Structure in Representation Learning#618972
- Similarity search generalisation in contrastive learning with InfoNCE loss#361453
- ITO: Multi-View Alignment and Training-Time Fusion for Image-Text Pretraining#1143698
Record · ID 1326638
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