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Found 8 papers, 3 papers with code
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences.
Mosaic: An Architecture for Scalable & Interoperable Data Views
Mosaic is an architecture for greater scalability, extensibility, and interoperability of interactive data views.
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.
Designing Data: Proactive Data Collection and Iteration for Machine Learning
Lack of diversity in data collection has caused significant failures in machine learning (ML) applications.
Network Report: A Structured Description for Network Datasets
Challenges arise as networks are often used across different domains (e. g., network science, physics, etc) and have complex structures.
Symphony: Composing Interactive Interfaces for Machine Learning
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems.
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels
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 labels over all data instances.
