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Found 14 papers, 6 papers with code
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming
Weak Supervision (WS) techniques allow users to efficiently create large training datasets by programmatically labeling data with heuristic sources of supervision.
A Survey on Programmatic Weak Supervision
Labeling training data has become one of the major roadblocks to using machine learning.
Creating Training Sets via Weak Indirect Supervision
Creating labeled training sets has become one of the major roadblocks in machine learning.
WRENCH: A Comprehensive Benchmark for Weak Supervision
To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Labeling training datasets has become a key barrier to building medical machine learning models.
Improving Sample Complexity with Observational Supervision
Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.
Learning Dependency Structures for Weak Supervision Models
Labeling training data is a key bottleneck in the modern machine learning pipeline.
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications.
Training Complex Models with Multi-Task Weak Supervision
Snorkel MeTaL: A framework for training models with multi-task weak supervision
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Snorkel: Rapid Training Data Creation with Weak Supervision
In a user study, subject matter experts build models 2. 8x faster and increase predictive performance an average 45. 5% versus seven hours of hand labeling.
Learning the Structure of Generative Models without Labeled Data
Curating labeled training data has become the primary bottleneck in machine learning.
Data Programming: Creating Large Training Sets, Quickly
Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.
