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Found 9 papers, 6 papers with code
Polynomial-based Self-Attention for Table Representation learning
Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning.
Graph Convolutions Enrich the Self-Attention in Transformers!
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc.
CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis
With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios.
STaSy: Score-based Tabular data Synthesis
Our proposed training strategy includes a self-paced learning technique and a fine-tuning strategy, which further increases the sampling quality and diversity by stabilizing the denoising score matching training.
An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models
Tabular data typically contains private and important information; thus, precautions must be taken before they are shared with others.
SOS: Score-based Oversampling for Tabular Data
To our knowledge, we are the first presenting a score-based tabular data oversampling method.
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
The problem of processing very long time-series data (e. g., a length of more than 10, 000) is a long-standing research problem in machine learning.
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks.
OCT-GAN: Neural ODE-based Conditional Tabular GANs
Synthesizing tabular data is attracting much attention these days for various purposes.
