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…
Apple Intelligence Foundation Language Models: Tech Report 2025
Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang +395
We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model opti…
Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors
Michelle S. Lam, Fred Hohman, Dominik Moritz +3
AI policy sets boundaries on acceptable behavior for AI models, but this is challenging in the context of large language models (LLMs): how do you ensure coverage over a vast behav…
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…
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…
Exploring Empty Spaces: Human-in-the-Loop Data Augmentation
Catherine Yeh, Donghao Ren, Yannick Assogba +2
Data augmentation is crucial to make machine learning models more robust and safe. However, augmenting data can be challenging as it requires generating diverse data points to rigo…