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Updated: Sep 3, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Deep Unsupervised Active Learning on Learnable Graphs.

Handong Ma, Changsheng Li, Xinchu Shi

    IEEE Transactions on Neural Networks and Learning Systems
    |July 27, 2022
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    Summary
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    This study introduces a novel deep unsupervised active learning model using learnable graphs (ALLGs) to improve sample representation and selection. ALLGs enhance deep learning for unsupervised active learning by incorporating graph structures and shortcut connections.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Science

    Background:

    • Deep learning shows promise in unsupervised active learning.
    • Existing methods often overlook sample relationships, limiting representation learning effectiveness.

    Purpose of the Study:

    • To propose a novel deep unsupervised active learning model leveraging learnable graphs.
    • To enhance sample representation and selection in unsupervised active learning.

    Main Methods:

    • Introduced a model named ALLGs (Active Learning via Learnable Graphs).
    • Learned optimal graph structures for better sample representation and selection.
    • Incorporated k-nearest neighbor graphs as a priori and relation propagation graph structures.
    • Utilized shortcut connections to mitigate the over-smoothing problem.

    Main Results:

    • Demonstrated the efficacy of ALLGs through extensive experiments on six datasets.
    • Showcased improved sample representation and selection capabilities.

    Conclusions:

    • ALLGs represent a novel approach to unsupervised active learning by integrating graph structure learning.
    • The method offers a more effective representation learning mechanism compared to existing auto-encoder based techniques.