arXiv (OAI Expanded)
open access
Approaching an unknown communication system by latent space exploration and causal inference
This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of individual latent variables to extreme values with methods inspired by causal inference into an approach we call causal dis
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
- Approaching an unknown communication system by latent space exploration and causal inference#785025
- Understanding And Enhancing Diversity In Generative Models#354230
- From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables#134564
- Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols#609804
- Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients#266171
Record · ID 783435
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