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Found 3 papers, 1 papers with code
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
On-device machine learning (ML) promises to improve the privacy, responsiveness, and proliferation of new, intelligent user experiences by moving ML computation onto everyday personal devices.
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements
Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal.
Collaborative Machine Learning Model Building with Families Using Co-ML
We share the Co-ML system design and contribute a discussion of how using Co-ML in a collaborative activity enabled beginners to collectively engage with dataset design considerations underrepresented in prior work such as data diversity, class imbalance, and data quality.
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