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Tabular Data Generation
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Generation of the tabular data using generative models
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
The Prompt is Mightier than the Example
Graph Conditional Flow Matching for Relational Data Generation
A Note on Statistically Accurate Tabular Data Generation Using Large Language Models
A Comprehensive Survey of Synthetic Tabular Data Generation
Diffusion Transformers for Tabular Data Time Series Generation
TabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion Models
Assessing Generative Models for Structured Data
GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction
A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond
LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship Preservation
Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints
TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generation
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer
Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation
Understanding and Mitigating Memorization in Diffusion Models for Tabular Data
Tabular data generation with tensor contraction layers and transformers
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators
Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
Generating Realistic Tabular Data with Large Language Models
TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation
Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
Preserving logical and functional dependencies in synthetic tabular data
Tabular Data Generation using Binary Diffusion
Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation
Scaling Up Diffusion and Flow-based XGBoost Models
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
High-Quality Tabular Data Generation using Post-Selected VAE
Unmasking Trees for Tabular Data
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
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