Counting with the Crowd

By Adam Marcus, David R Karger, Samuel Madden, Rob Miller, Sewoong Oh. 2012

123 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “[123] solves the problem of selectivity estimation in a crowdsourced database.”
Crowdsourced Data Management: A Survey (2016, methodology): “Worker elimination, which eliminates low-quality workers or spammers based on worker models, is a very common strategy to improve quality [58], [78], [99].”
Query-Oriented Data Cleaning with Oracles (2015, methodology): “In addition, previous works propose different methods for evaluating crowd workers’ quality, e.g., to ﬁlter spam-mers and identify domain experts [42, 39, 36, 29, 37].”
Efficient Learning for Crowdsourced Regression (2017, methodology): “Crowdsourcing systems are widely used in practice for a variety of real-world tasks ranging from classification Sheng et al. (2008) and regression Marcus et al. (2012) to more complex tasks such as protein folding Peng et al. (2013), searching videos Bernstein et al. (2011), ranking Lee et al.…”
Crowdsourcing large scale wrapper inference (2014, unknown): “[30] engage 5 workers per task.”
Foundations of Crowd Data Sourcing (2015, background): “The error probability can be estimated based on preliminary tests with gold-standard data (where the ground truth is known) [6, 19] or by comparing the answers of different users to the same question [3, 14].”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “[130] study how to benefit from the crowd when the items being counted are challenging for computers to interpret (e.”
Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning (2014, background): “While query optimization techniques [17, 24, 28] can reduce the number of items that need labeling, any crowd-sourced database that relies solely on human-provided labels will eventually suer from scalability issues when facedwithweb-scale datasets and problems (e.”