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WaveletML: A Scalable and Extensible Wavelet Neural Network Framework

WaveletML is an open-source Python framework designed for building, training, and evaluating Wavelet Neural Networks (WNNs) tailored for supervised learning tasks such as regression and classification. Leveraging the power of PyTorch and the modularity of scikit-learn, WaveletML provides a unified, extensible, and scalable platform for researchers and practitioners to explore wavelet-based neural architectures.Key features include:A flexible architecture supporting various WNN configurations.Built-in support for both gradient-based and metaheuristic optimization methods.Seamless integration with the scikit-learn API for model interoperability

By Nguyen, Van ThieuToolReference information

Resource information

DOI
10.6084/m9.figshare.29095376
Credit basis
Creators in DataCite DOI metadata
Publisher
figshare
Upstream last modified
Mar 29, 2026
Access
Files remain with the original publisher
Last checked
Nov 5, 2026
License
GPL 3.0+
Last record update
Nov 5, 2026

About this resource

WaveletML is an open-source Python framework designed for building, training, and evaluating Wavelet Neural Networks (WNNs) tailored for supervised learning tasks such as regression and classification. Leveraging the power of PyTorch and the modularity of scikit-learn, WaveletML provides a unified, extensible, and scalable platform for researchers and practitioners to explore wavelet-based neural architectures.Key features include:A flexible architecture supporting various WNN configurations.Built-in support for both gradient-based and metaheuristic optimization methods.Seamless integration with the scikit-learn API for model interoperability and pipeline construction.Designed for scalability, enabling deployment on CPU or GPU environments.Easily extendable to accommodate custom wavelet functions, and training algorithms.Whether you're prototyping WNN-based models or conducting advanced experimental research, WaveletML aims to bridge the gap between theory and practical implementation in wavelet-based learning systems.