REVIEW
Generative Models for Learning from Crowds
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
classification
cs.AIcs.HCcs.LG
keywords
generativeinferencemethodsmodelsaggregationalgorithmconsistentlycrowds
read the original abstract
In this paper, we propose generative probabilistic models for label aggregation. We use Gibbs sampling and a novel variational inference algorithm to perform the posterior inference. Empirical results show that our methods consistently outperform state-of-the-art methods.
Discussion (0). Continue with ORCID to comment.