341 citations · 937 across the 30 of their papers we have counts for
5 papers · 1 filter
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…