69 citations · 159 across the 6 of their papers we have counts for
6 papers
Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph
Duen Horng Chau, Donghao Ren, Fred Hohman +1
While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) gra…
A Scalable Approach to Clustering Embedding Projections
Donghao Ren, Fred Hohman, Dominik Moritz
Interactive visualization of embedding projections is a useful technique for understanding data and evaluating machine learning models. Labeling data within these visualizations is…
Embedding Atlas: Low-Friction, Interactive Embedding Visualization
Donghao Ren, Fred Hohman, Halden Lin +1
Embedding projections are popular for visualizing large datasets and models. However, people often encounter "friction" when using embedding visualization tools: (1) barriers to ad…
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
Fred Hohman, Mary Beth Kery, Donghao Ren +1
On-device machine learning (ML) promises to improve the privacy, responsiveness, and proliferation of new, intelligent user experiences by moving ML computation onto everyday perso…
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels
Jochen Görtler, Fred Hohman, Dominik Moritz +5
The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class…
mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks
Mary Beth Kery, Donghao Ren, Fred Hohman +3
We aim to increase the flexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user i…