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Paper

High-Dimensional Analog Circuit Sizing via Bayesian Optimization in the Variational Autoencoder Enhanced Latent Space

by Independent / Community 000c7d20256f1d14a417ecb3b5a2b1180d73f142

Free2AITools Nexus Index

61.0

S: Semantic 50

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A: Authority 64

P: Popularity 40

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

High-dimensional analog circuit sizing with machine learning-based surrogate models suffers from the high sampling cost of evaluating expensive black-box objective functions in huge design spaces. This work addresses the sampling efficiency challenge by elaborately reducing the dimensionality of the input spaces, enabling efficient optimization for automated analog circuit sizing. We propose a latent space optimization method that includes an iteratively updated generative model based on a va...

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Paper Information Summary
Entity Passport
Registry ID	000c7d20256f1d14a417ecb3b5a2b1180d73f142
License	ArXiv
Provider	semantic_scholar

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Academic & Research Attribution

BibTeX

@misc{000c7d20256f1d14a417ecb3b5a2b1180d73f142,
  author = {Unknown},
  title = {High-Dimensional Analog Circuit Sizing via Bayesian Optimization in the Variational Autoencoder Enhanced Latent Space Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/000c7d20256f1d14a417ecb3b5a2b1180d73f142}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). High-Dimensional Analog Circuit Sizing via Bayesian Optimization in the Variational Autoencoder Enhanced Latent Space [Paper]. Free2AITools. https://api.semanticscholar.org/000c7d20256f1d14a417ecb3b5a2b1180d73f142

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Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 64

Popularity (P) 40

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for High-Dimensional Analog Circuit Sizing via Bayesian Optimization in the Variational Autoencoder Enhanced Latent Space: Authority (A:64), Popularity (P:40), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"High-dimensional analog circuit sizing with machine learning-based surrogate models suffers from the high sampling cost of evaluating expensive black-box objective functions in huge design spaces. This work addresses the sampling efficiency challenge by elaborately reducing the dimensionality of the input spaces, enabling efficient optimization for automated analog circuit sizing. We propose a latent space optimization method that includes an iteratively updated generative model based on a va..."

❝ Cite Node

@article{Unknown2026High-Dimensional,
  title={High-Dimensional Analog Circuit Sizing via Bayesian Optimization in the Variational Autoencoder Enhanced Latent Space},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈2CitationsSemantic Scholar

🏛️64AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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📦Data Source: semantic_scholar

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source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

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architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 2

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