PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
/ Authors
/ Abstract
PyKale is a Python library for Knowledge-aware machine learning from multiple sources of data to enable/accelerate interdisciplinary research. It embodies green machine learning principles to reduce repetitions/redundancy, reuse existing resources, and recycle learning models across areas. We propose a pipeline-based application programming interface (API) so all machine learning workflows follow a standardized six-step pipeline. PyKale focuses on leveraging knowledge from multiple sources for accurate and interpretable prediction, particularly multimodal learning and transfer learning. To be more accessible, it separates code and configurations to enable non-programmers to configure systems without coding. PyKale is officially part of the PyTorch ecosystem and includes interdisciplinary examples in bioinformatics, knowledge graph, image/video recognition, and medical imaging: https://pykale.github.io/.
Journal: Proceedings of the 31st ACM International Conference on Information & Knowledge Management