ArticleAdvances in neural information processing systems2017
Inferring Generative Model Structure with Static Analysis.
Article in Advances in neural information processing systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Snorkel: rapid training data creation with weak supervision.The VLDB journal : very large data bases : a publication of the VLDB Endowment · 2020Article
- Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences.Nature communications · 2019Article
- Snuba: Automating Weak Supervision to Label Training Data.Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases · 2018Article
- Exploring the Utility of Developer Exhaust.Proceedings of the Second Workshop on Data Management for End-to-End Machine Learning. Workshop on Data Management for End-to-End Machine Learning (2nd : 2018 : Houston, Tex.) · 2018Article
- Training Complex Models with Multi-Task Weak Supervision.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial IntelligenceArticle
- Scene Graph Prediction with Limited Labels.Proceedings. IEEE International Conference on Computer VisionArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects training label quality, but is difficult to learn without any ground truth labels. We instead rely on these weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus reducing the data required to learn structure significantly. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations found, improving over the standard sample complexity, which is exponential in
Identifiers
29391769PMC5789796What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.