341 citations · 937 across the 30 of their papers we have counts for
31 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…
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…
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…
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…
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments
Angie Boggust, Venkatesh Sivaraman, Yannick Assogba +3
To deploy machine learning models on-device, practitioners use compression algorithms to shrink and speed up models while maintaining their high-quality output. A critical aspect o…
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference
Fred Hohman, Chaoqun Wang, Jinmook Lee +7
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models on…
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…
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings
Zijie J. Wang, Fred Hohman, Duen Horng Chau
Machine learning models often learn latent embedding representations that capture the domain semantics of their training data. These embedding representations are valuable for inte…
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements
Samantha Robertson, Zijie J. Wang, Dominik Moritz +2
Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal. With finite time and resources, ML practitioners are forced to pri…
Collaborative Machine Learning Model Building with Families Using Co-ML
Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman +4
Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to build a model. However, solo…
Designing Data: Proactive Data Collection and Iteration for Machine Learning
Aspen Hopkins, Fred Hohman, Luca Zappella +2
Lack of diversity in data collection has caused significant failures in machine learning (ML) applications. While ML developers perform post-collection interventions, these are tim…
Symphony: Composing Interactive Interfaces for Machine Learning
Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman +5
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, r…
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…
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
Haekyu Park, Nilaksh Das, Rahul Duggal +4
Existing research on making sense of deep neural networks often focuses on neuron-level interpretation, which may not adequately capture the bigger picture of how concepts are coll…
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…
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks
Nilaksh Das, Haekyu Park, Zijie J. Wang +4
Deep neural networks (DNNs) are now commonly used in many domains. However, they are vulnerable to adversarial attacks: carefully crafted perturbations on data inputs that can fool…
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
Zijie J. Wang, Robert Turko, Omar Shaikh +5
Deep learning's great success motivates many practitioners and students to learn about this exciting technology. However, it is often challenging for beginners to take their first…
CNN 101: Interactive Visual Learning for Convolutional Neural Networks
Zijie J. Wang, Robert Turko, Omar Shaikh +5
The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challengi…
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning
Nilaksh Das, Haekyu Park, Zijie J. Wang +4
Deep neural networks (DNNs) are increasingly powering high-stakes applications such as autonomous cars and healthcare; however, DNNs are often treated as "black boxes" in such appl…
ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features
Xiangyun Lei, Fred Hohman, Duen Horng Chau +1
In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory. These techniques often require resear…
NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions
Haekyu Park, Fred Hohman, Duen Horng Chau
As deep neural networks are increasingly used in solving high-stake problems, there is a pressing need to understand their internal decision mechanisms. Visualization has helped ad…
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
Ángel Alexander Cabrera, Will Epperson, Fred Hohman +3
The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems m…