5 papers
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…
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…
Quantum Transfer Learning to Boost Dementia Detection
Sounak Bhowmik, Talita Perciano, Himanshu Thapliyal
Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely in…
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…
Visualizing Loss Functions as Topological Landscape Profiles
Caleb Geniesse, Jiaqing Chen, Tiankai Xie +7
In machine learning, a loss function measures the difference between model predictions and ground-truth (or target) values. For neural network models, visualizing how this loss cha…