Cover Image

Exploring Variational Graph Autoencoders for Distribution Grid Data Generation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Abbas, Syed Zain, Okoyomon, Ehimare
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
by: Okoyomon, Ehimare, et al.
Published: (2025)

BOOST-RPF: Boosted Sequential Trees for Radial Power Flow
by: Okoyomon, Ehimare, et al.
Published: (2026)

Rademacher Meets Colors: More Expressivity, but at What Cost ?
by: Carrasco, Martin, et al.
Published: (2025)

Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders
by: Sundsgaard, Konrad, et al.
Published: (2025)

Exploring Transformer Placement in Variational Autoencoders for Tabular Data Generation
by: Silva, Aníbal, et al.
Published: (2026)

Generalized Graph Transformer Variational Autoencoder
by: Karki, Siddhant
Published: (2025)

A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network
by: Rico, Julian Carvajal, et al.
Published: (2024)

IVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencoder
by: Zhou, Youran, et al.
Published: (2025)

Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders
by: Mancisidor, Rogelio A, et al.
Published: (2025)

Data Augmentation with Variational Autoencoder for Imbalanced Dataset
by: Stocksieker, Samuel, et al.
Published: (2024)

Investigating GNN Convergence on Large Randomly Generated Graphs with Realistic Node Feature Correlations
by: Ahmed, Mohammed Zain Ali
Published: (2026)

Urdu News Article Recommendation Model using Natural Language Processing Techniques
by: Abbas, Syed Zain, et al.
Published: (2022)

Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational Autoencoder
by: Zeng, Zezhen, et al.
Published: (2023)

Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data
by: Bouchattaoui, Mouad El, et al.
Published: (2023)

Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders
by: Dang, Hien, et al.
Published: (2023)

Synthetic Electrogram Generation with Variational Autoencoders for ECGI
by: Gutiérrez-Fernández, Miriam, et al.
Published: (2025)

A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs
by: Yang, Sikun, et al.
Published: (2023)

Analysis of Variational Sparse Autoencoders
by: Baker, Zachary, et al.
Published: (2025)

Variational Rank Reduction Autoencoders
by: Mounayer, Jad, et al.
Published: (2025)

Ensemble Visualization With Variational Autoencoder
by: Wu, Cenyang, et al.
Published: (2025)

An Introduction to Discrete Variational Autoencoders
by: Jeffares, Alan, et al.
Published: (2025)

Likelihood-Free Variational Autoencoders
by: Xu, Chen, et al.
Published: (2025)

On Kernel-based Variational Autoencoder
by: Qin, Tian, et al.
Published: (2024)

Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study
by: Rais, Khadija, et al.
Published: (2024)

Exploring Graph-Transformer Out-of-Distribution Generalization Abilities
by: Niv, Itay, et al.
Published: (2025)

CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data
by: Carlin, Lee, et al.
Published: (2025)

Riemannian Geometry-Preserving Variational Autoencoder for MI-BCI Data Augmentation
by: Poļaka, Viktorija, et al.
Published: (2026)

Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data
by: Simchoni, Giora, et al.
Published: (2024)

Graph Generative Models Evaluation with Masked Autoencoder
by: Wang, Chengen, et al.
Published: (2025)

Bidirectional Variational Autoencoders
by: Kosko, Bart, et al.
Published: (2025)

Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders
by: Rouzoumka, Y A, et al.
Published: (2025)

To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders
by: Salha-Galvan, Guillaume, et al.
Published: (2025)

Variational Rank Reduction Autoencoders for Generative Thermal Design
by: Tierz, Alicia, et al.
Published: (2025)

GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
by: Bresson, Roman, et al.
Published: (2026)

Attention Based Molecule Generation via Hierarchical Variational Autoencoder
by: Sivanesan, Divahar
Published: (2024)

Dynamic Model Switching for Improved Accuracy in Machine Learning
by: Hasani, Syed Tahir Abbas
Published: (2024)

Zero-Variance Gradients for Variational Autoencoders
by: Shao, Zilei, et al.
Published: (2025)

Discovering Influential Factors in Variational Autoencoders
by: Liu, Shiqi, et al.
Published: (2018)

Theoretical Convergence Guarantees for Variational Autoencoders
by: Surendran, Sobihan, et al.
Published: (2024)

A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data & Overparameterization?
by: Xiao, Tim Z., et al.
Published: (2023)