4 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…
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…
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
Michał Dereziński, Michael W. Mahoney
Large matrices arise in many machine learning and data analysis applications, including as representations of datasets, graphs, model weights, and first and second-order derivative…
Extensions to the SENSEI In situ Framework for Heterogeneous Architectures
Burlen Loring, E. Wes Bethel, Gunther H. Weber +1
The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and pr…