arXiv (All)
open access
Bayesian Optimization in Sequence-to-Architecture Latent Space for Zero-Shot NAS
Zero-shot Neural Architecture Search removes the prohibitive cost of traditional NAS, but its search process is typically based on the evolutionary algorithm (EA); lacking an explicit model of the objective, it often resorts to a near-random search through mutation. Bayesian Optimization offers a pr
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
- Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation#1341873
- Manifold-Constrained Initial Noise Optimization for Efficient Generative Model Alignment#1154988
- Generating from Discrete Distributions Using Diffusions: Insights from Random Constraint Satisfaction Problems#974359
- Sample-Efficient Optimization over Generative Priors via Coarse Learnability#1116085
- An adaptive parameter optimization method for astronomical image alignment using Bayesian optimization. I. A hierarchical search strategy for FWHM and SNR#975090
Record · ID 1202586
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