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Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management
Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management
Summary
Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management is a scholarly article[1].
Key Facts
Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management. Retrieved May 24, 2026, from https://4ort.xyz/entity/generating-synthetic-data-with-variational-autoencoder-to-address-class-imbalance-of-graph-attention-network-prediction-
MLA“Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/generating-synthetic-data-with-variational-autoencoder-to-address-class-imbalance-of-graph-attention-network-prediction-.
BibTeX@misc{4ortxyz_generating-synthetic-data-with-variational-autoencoder-to-address-class-imbalance-of-graph-attention-network-prediction-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management}}, year = {2026}, url = {https://4ort.xyz/entity/generating-synthetic-data-with-variational-autoencoder-to-address-class-imbalance-of-graph-attention-network-prediction-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management — https://4ort.xyz/entity/generating-synthetic-data-with-variational-autoencoder-to-address-class-imbalance-of-graph-attention-network-prediction- (retrieved 2026-05-24)
