PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
- URL: http://arxiv.org/abs/2106.09756v1
- Date: Thu, 17 Jun 2021 18:35:37 GMT
- Title: PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
- Authors: Haiping Lu, Xianyuan Liu, Robert Turner, Peizhen Bai, Raivo E Koot,
Shuo Zhou, Mustafa Chasmai, Lawrence Schobs
- Abstract summary: Pykale is a Python library for knowledge-aware machine learning on graphs, images, texts, and videos.
We formulate new green machine learning guidelines based on standard software engineering practices.
We build PyKale on PyTorch and leverage the rich PyTorch ecosystem.
- Score: 6.276936701568444
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Machine learning is a general-purpose technology holding promises for many
interdisciplinary research problems. However, significant barriers exist in
crossing disciplinary boundaries when most machine learning tools are developed
in different areas separately. We present Pykale - a Python library for
knowledge-aware machine learning on graphs, images, texts, and videos to enable
and accelerate interdisciplinary research. We formulate new green machine
learning guidelines based on standard software engineering practices and
propose a novel pipeline-based application programming interface (API). PyKale
focuses on leveraging knowledge from multiple sources for accurate and
interpretable prediction, thus supporting multimodal learning and transfer
learning (particularly domain adaptation) with latest deep learning and
dimensionality reduction models. We build PyKale on PyTorch and leverage the
rich PyTorch ecosystem. Our pipeline-based API design enforces standardization
and minimalism, embracing green machine learning concepts via reducing
repetitions and redundancy, reusing existing resources, and recycling learning
models across areas. We demonstrate its interdisciplinary nature via examples
in bioinformatics, knowledge graph, image/video recognition, and medical
imaging.
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