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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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A Generalized Explanation Framework for Visualization of Deep Learning Model Predictions.

Pei Wang, Nuno Vasconcelos

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    Summary
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    A new framework called GALORE unifies multiple explanation types for AI, improving fine-grained classification. This approach helps understand AI decisions and why alternatives are rejected, enhancing AI interpretability.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Attribution-based explanations are common in computer vision but insufficient for fine-grained classification.
    • Expert domains require understanding not only 'what' but also 'why' a class is chosen and 'why not' an alternative.

    Purpose of the Study:

    • To introduce a unified framework (GALORE) for advanced AI explanations.
    • To address limitations of current methods in fine-grained classification tasks.
    • To provide deeper insights into AI decision-making processes.

    Main Methods:

    • Developed the GenerAlized expLanatiOn fRamEwork (GALORE) unifying attributive, deliberative, and counterfactual explanations.
    • Defined explanations as combinations of attribution maps and confidence scores.
    • Proposed an evaluation protocol using CUB200 and ADE20K datasets with part and attribute annotations.

    Main Results:

    • Confidence scores improve explanation accuracy.
    • Deliberative explanations reveal network insecurities and correlate with human deliberation.
    • Efficiently computed counterfactual explanations enhance human learning in machine teaching.

    Conclusions:

    • GALORE offers a comprehensive approach to AI explanations, particularly for fine-grained classification.
    • The framework enhances AI interpretability by providing multi-faceted insights.
    • GALORE has practical implications for improving AI understanding and human-AI interaction.