Work

TINTOlib Benchmark v0.0.1

Year: 2024

Type: article

Abstract: This zip contains the resources and code for the study: "Improving the Use of Deep Neural Networks with Tabular Data by Exploiting Synthetic Images." It benchmarks eight tabular-to-image transformation techniques and evaluates their effectiveness in combination with hybrid architectures (CNN+MLP and ViT+MLP) across diverse datasets and machine learning tasks. Github repository: https://github.com/DCY1117/TINTOlib-benchmark

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14283269

Authors: Liu, Jiayun, Castillo-Cara, Manuel, García Castro, Raúl

Institutions: Universidad Politécnica de Madrid, Universidad Nacional de Educación a Distancia

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6968093936

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