Cover Image

Exploring Transformer Placement in Variational Autoencoders for Tabular Data Generation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Silva, Aníbal, Santos, Moisés, Restivo, André, Soares, Carlos
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Tabular data generation with tensor contraction layers and transformers
by: Silva, Aníbal, et al.
Published: (2024)

Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation
by: Santos, Moisés, et al.
Published: (2023)

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

To Predict or Not To Predict? Proportionally Masked Autoencoders for Tabular Data Imputation
by: Kim, Jungkyu, et al.
Published: (2024)

Diffusion Transformers for Tabular Data Time Series Generation
by: Garuti, Fabrizio, et al.
Published: (2025)

Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings
by: Naor, Erel, et al.
Published: (2025)

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

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

ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder
by: Sauter, Andreas, et al.
Published: (2025)

Hellinger Multimodal Variational Autoencoders
by: Vo, Huyen, et al.
Published: (2026)

Measuring LLM Sensitivity in Transformer-based Tabular Data Synthesis
by: R, Maria F. Davila, et al.
Published: (2025)

Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
by: Wang, Dongjie, et al.
Published: (2025)

Tabular Data Generation using Binary Diffusion
by: Kinakh, Vitaliy, et al.
Published: (2024)

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders
by: Stevens, Alexander, et al.
Published: (2024)

Multi-Layer Attention-Based Explainability via Transformers for Tabular Data
by: Gavito, Andrea Treviño, et al.
Published: (2023)

TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer
by: Gao, Weihao, et al.
Published: (2024)

GEM-T: Generative Tabular Data via Fitting Moments
by: Li, Miao, et al.
Published: (2025)

Generating Realistic Tabular Data with Large Language Models
by: Nguyen, Dang, et al.
Published: (2024)

TabularQGAN: A Quantum Generative Model for Tabular Data
by: Bhardwaj, Pallavi, et al.
Published: (2025)

Phase-Type Variational Autoencoders for Heavy-Tailed Data
by: Ziani, Abdelhakim, et al.
Published: (2026)

Uncertainty-Aware Generative Oversampling Using an Entropy-Guided Conditional Variational Autoencoder
by: Zare, Amirhossein, et al.
Published: (2025)

Variational Autoencoders for Efficient Simulation-Based Inference
by: Nautiyal, Mayank, et al.
Published: (2024)

Adaptive Compression of the Latent Space in Variational Autoencoders
by: Sejnova, Gabriela, et al.
Published: (2023)

TAGAL: Tabular Data Generation using Agentic LLM Methods
by: Ronval, Benoît, et al.
Published: (2025)

Poisson Variational Autoencoder
by: Vafaii, Hadi, et al.
Published: (2024)

Universal Embeddings of Tabular Data
by: Franz, Astrid, et al.
Published: (2025)

A Sobering Look at Tabular Data Generation via Probabilistic Circuits
by: Scassola, Davide, et al.
Published: (2026)

Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series
by: Shukla, Satya Narayan, et al.
Published: (2021)

ED-VAE: Entropy Decomposition of ELBO in Variational Autoencoders
by: Lygerakis, Fotios, et al.
Published: (2024)

Synthesizing Tabular Data Using Selectivity Enhanced Generative Adversarial Networks
by: Zhou, Youran, et al.
Published: (2025)

FASTGEN: Fast and Cost-Effective Synthetic Tabular Data Generation with LLMs
by: Nguyen, Anh, et al.
Published: (2025)

One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data
by: Luetto, Simone, et al.
Published: (2023)

TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting
by: Margeloiu, Andrei, et al.
Published: (2024)

Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data
by: Xie, Boyu, et al.
Published: (2025)

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
by: Jiang, Mingxuan, et al.
Published: (2025)

Deep Feature Embedding for Tabular Data
by: Wu, Yuqian, et al.
Published: (2024)

Automated Model Selection for Tabular Data
by: Amballa, Avinash, et al.
Published: (2024)

Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
by: Dobhal, Umang, et al.
Published: (2026)

CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis
by: Alshantti, Abdallah, et al.
Published: (2023)

Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning
by: Nam, Jaehyun, et al.
Published: (2024)