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DOE OSTI · 2514366

LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics

Xie, Tiankai [Arizona State Univ., Tempe, AZ (United States)] (ORCID:0009000888320063)·Chen, Jiaqing [Arizona State Univ., Tempe, AZ (United States)]·Yang, Yaoqing [Dartmouth College, Hanover, NH (United States)]·Geniesse, Caleb [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000278978338)·Shi, Ge [Univ. of California, Davis, CA (United States)] (ORCID:0000000269446874)·Chaudhari, Ajinkya [Univ. of California, Davis, CA (United States)]·Cava, John Kevin [Arizona State Univ., Tempe, AZ (United States)]·Mahoney, Michael W. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)]·Perciano, Talita [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000223881803)·Weber, Gunther H. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000217941398)·Maciejewski, Ross [Arizona State Univ., Tempe, AZ (United States)] (ORCID:0000000188036355)
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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. Here 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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Xie, Tiankai [Arizona State Univ., Tempe, AZ (United States)] (ORCID:0009000888320063), Chen, Jiaqing [Arizona State Univ., Tempe, AZ (United States)], Yang, Yaoqing [Dartmouth College, Hanover, NH (United States)], Geniesse, Caleb [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000278978338), Shi, Ge [Univ. of California, Davis, CA (United States)] (ORCID:0000000269446874), Chaudhari, Ajinkya [Univ. of California, Davis, CA (United States)], Cava, John Kevin [Arizona State Univ., Tempe, AZ (United States)], Mahoney, Michael W. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)], Perciano, Talita [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000223881803), Weber, Gunther H. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000217941398), Maciejewski, Ross [Arizona State Univ., Tempe, AZ (United States)] (ORCID:0000000188036355). 2024-12-16. LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics. https://doi.org/10.1109/mcg.2024.3509374

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