LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics

• URL: http://arxiv.org/abs/2412.13321v1
• Date: Tue, 17 Dec 2024 20:40:06 GMT
• Title: LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics
• Authors: Tiankai Xie, Jiaqing Chen, Yaoqing Yang, Caleb Geniesse, Ge Shi, Ajinkya Chaudhari, John Kevin Cava, Michael W. Mahoney, Talita Perciano, Gunther H. Weber, Ross Maciejewski,
• Abstract summary: LossLens is a visual analytics framework that explores loss landscapes at multiple scales. We demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.
• Score: 40.39489322471626
• License:
• Abstract: Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. We demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.

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