Tabular data generation with tensor contraction layers and transformers
- URL: http://arxiv.org/abs/2412.05390v1
- Date: Fri, 06 Dec 2024 19:34:13 GMT
- Title: Tabular data generation with tensor contraction layers and transformers
- Authors: Aníbal Silva, André Restivo, Moisés Santos, Carlos Soares,
- Abstract summary: We investigate the potential of using embedding representations on data generation, utilizing tensor contraction layers and transformers.
Our empirical study, conducted across multiple datasets from the OpenML CC18 suite, compares models over density estimation and Machine Learning efficiency metrics.
The main takeaway from our results is that leveraging embedding representations with the help of tensor contraction layers improves density estimation metrics, albeit maintaining competitive performance in terms of machine learning efficiency.
- Score: 0.35998666903987897
- License:
- Abstract: Generative modeling for tabular data has recently gained significant attention in the Deep Learning domain. Its objective is to estimate the underlying distribution of the data. However, estimating the underlying distribution of tabular data has its unique challenges. Specifically, this data modality is composed of mixed types of features, making it a non-trivial task for a model to learn intra-relationships between them. One approach to address mixture is to embed each feature into a continuous matrix via tokenization, while a solution to capture intra-relationships between variables is via the transformer architecture. In this work, we empirically investigate the potential of using embedding representations on tabular data generation, utilizing tensor contraction layers and transformers to model the underlying distribution of tabular data within Variational Autoencoders. Specifically, we compare four architectural approaches: a baseline VAE model, two variants that focus on tensor contraction layers and transformers respectively, and a hybrid model that integrates both techniques. Our empirical study, conducted across multiple datasets from the OpenML CC18 suite, compares models over density estimation and Machine Learning efficiency metrics. The main takeaway from our results is that leveraging embedding representations with the help of tensor contraction layers improves density estimation metrics, albeit maintaining competitive performance in terms of machine learning efficiency.
Related papers
- Mixture of Attention Yields Accurate Results for Tabular Data [21.410818837489973]
We propose MAYA, an encoder-decoder transformer-based framework.
In the encoder, we design a Mixture of Attention (MOA) that constructs multiple parallel attention branches.
We employ collaborative learning with a dynamic consistency weight constraint to produce more robust representations.
arXiv Detail & Related papers (2025-02-18T03:43:42Z) - Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data [9.990687944474738]
LMMVAE is a novel model which separates the classic VAE latent model into fixed and random parts.
It is shown to improve squared reconstruction error and negative likelihood loss significantly on unseen data.
arXiv Detail & Related papers (2024-12-22T07:20:17Z) - TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation [91.50296404732902]
We introduce TabDiff, a joint diffusion framework that models all mixed-type distributions of tabular data in one model.
Our key innovation is the development of a joint continuous-time diffusion process for numerical and categorical data.
TabDiff achieves superior average performance over existing competitive baselines, with up to $22.5%$ improvement over the state-of-the-art model on pair-wise column correlation estimations.
arXiv Detail & Related papers (2024-10-27T22:58:47Z) - An improved tabular data generator with VAE-GMM integration [9.4491536689161]
We propose a novel Variational Autoencoder (VAE)-based model that addresses limitations of current approaches.
Inspired by the TVAE model, our approach incorporates a Bayesian Gaussian Mixture model (BGM) within the VAE architecture.
We thoroughly validate our model on three real-world datasets with mixed data types, including two medically relevant ones.
arXiv Detail & Related papers (2024-04-12T12:31:06Z) - Distribution-Aware Data Expansion with Diffusion Models [55.979857976023695]
We propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model.
DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data.
arXiv Detail & Related papers (2024-03-11T14:07:53Z) - Improving Out-of-Distribution Robustness of Classifiers via Generative
Interpolation [56.620403243640396]
Deep neural networks achieve superior performance for learning from independent and identically distributed (i.i.d.) data.
However, their performance deteriorates significantly when handling out-of-distribution (OoD) data.
We develop a simple yet effective method called Generative Interpolation to fuse generative models trained from multiple domains for synthesizing diverse OoD samples.
arXiv Detail & Related papers (2023-07-23T03:53:53Z) - VTAE: Variational Transformer Autoencoder with Manifolds Learning [144.0546653941249]
Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables.
The nonlinearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning.
We show that geodesics and accurate computation can substantially improve the performance of deep generative models.
arXiv Detail & Related papers (2023-04-03T13:13:19Z) - Dynamic Latent Separation for Deep Learning [67.62190501599176]
A core problem in machine learning is to learn expressive latent variables for model prediction on complex data.
Here, we develop an approach that improves expressiveness, provides partial interpretation, and is not restricted to specific applications.
arXiv Detail & Related papers (2022-10-07T17:56:53Z) - A Graphical Model for Fusing Diverse Microbiome Data [2.385985842958366]
We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data.
We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model.
arXiv Detail & Related papers (2022-08-21T17:54:39Z) - Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via
Generative Models [16.436293069942312]
We are interested in learning probabilistic generative models from high-dimensional heterogeneous data in an unsupervised fashion.
We propose a general framework that combines disparate data types through the exponential family of distributions.
The proposed algorithm is presented in detail for the commonly encountered heterogeneous datasets with real-valued (Gaussian) and categorical (multinomial) features.
arXiv Detail & Related papers (2021-08-27T18:10:31Z) - Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction.
We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data.
Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
arXiv Detail & Related papers (2020-03-10T03:10:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.