arXiv (All)
open access
On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution
The interest in leveraging physics-based inductive bias in deep learning has resulted in recent development of hybrid deep generative models (hybrid-DGMs) that integrate known physics-based mathematical expressions in neural generative models. To identify these hybrid-DGMs requires inferring paramet
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
- On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution#1442369
- Interpretable Meta-Learning for Multi-Objective Chemical Search#1403143
- CosmoFlow: Using Deep Learning to Learn the Universe at Scale#620154
- DP18792: Experimental Evidence on the Learning Impact of Generative AI#357673
- Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows#1328974
Record · ID 1448264
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