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Updated: Jun 14, 2025

An R-Based Landscape Validation of a Competing Risk Model
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LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics.

Tiankai Xie, Jiaqing Chen, Yaoqing Yang

    IEEE Computer Graphics and Applications
    |March 3, 2025
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    Summary
    This summary is machine-generated.

    LossLens visualizes complex neural network loss landscapes, offering new insights into model architecture and training. This framework aids in understanding both local and global solution spaces for improved machine learning diagnostics.

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    Area of Science:

    • Machine Learning
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Neural network training optimizes parameters via loss functions.
    • Analyzing loss landscapes offers insights into network architecture and learning.
    • Global loss landscape visualization is challenging.

    Purpose of the Study:

    • Introduce LossLens, a visual analytics framework for exploring neural network loss landscapes.
    • Enable multi-scale analysis of loss landscapes.
    • Enhance model diagnostics through integrated global and local metrics.

    Main Methods:

    • Developed LossLens, a visual analytics framework.
    • Integrated global and local scale metrics.
    • Applied framework to case studies involving ResNet-20 and physics-informed neural networks.

    Main Results:

    • LossLens provides a comprehensive visual representation of loss landscapes.
    • Framework facilitates understanding of architectural influences (e.g., residual connections).
    • Demonstrated utility in visualizing physics-informed neural network parameter effects.

    Conclusions:

    • LossLens effectively addresses the challenge of conceptualizing and visualizing global loss landscapes.
    • The framework enhances model diagnostics by integrating multi-scale loss landscape information.
    • LossLens shows promise for analyzing diverse neural network architectures and applications.