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Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
In real-world machine learning applications, data subsets correspond to ...
09/13/2019 ∙ by Vincent S. Chen, et al. ∙
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
03/29/2019 ∙ by Alexander Ratner, et al. ∙
0
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Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Labeling training datasets has become a key barrier to building medical ...
03/26/2019 ∙ by Jared Dunnmon, et al. ∙
12
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Learning Dependency Structures for Weak Supervision Models
Labeling training data is a key bottleneck in the modern machine learnin...
03/14/2019 ∙ by Paroma Varma, et al. ∙
8
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Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Labeling training data is one of the most costly bottlenecks in developi...
12/02/2018 ∙ by Stephen H. Bach, et al. ∙
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Training Complex Models with Multi-Task Weak Supervision
As machine learning models continue to increase in complexity, collectin...
10/05/2018 ∙ by Alexander Ratner, et al. ∙
14
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Snorkel: Rapid Training Data Creation with Weak Supervision
Labeling training data is increasingly the largest bottleneck in deployi...
11/28/2017 ∙ by Alexander Ratner, et al. ∙
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Learning the Structure of Generative Models without Labeled Data
Curating labeled training data has become the primary bottleneck in mach...
03/02/2017 ∙ by Stephen H. Bach, et al. ∙
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Data Programming: Creating Large Training Sets, Quickly
Large labeled training sets are the critical building blocks of supervis...
05/25/2016 ∙ by Alexander Ratner, et al. ∙
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