3 papers
cs.LG2026
LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks
Shiyi Liu, Jiaqing Chen, Nicholas Hadler +6
Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and the outcomes of their react…
cs.LG2026
Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis
Jiaqing Chen, Nicholas Hadler, Tiankai Xie +8
Loss landscapes are a powerful tool for understanding neural network optimization and generalization, yet traditional low-dimensional analyses often miss complex topological featur…
cs.LG2024
LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics
Tiankai Xie, Jiaqing Chen, Yaoqing Yang +8
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…