May 30, 2024 · Scaling up crowd-sourcing to very large datasets: a case for active learning. Proceedings of the VLDB Endowment, Vol. 8, 2 (2014), 125--136. Digital Library.

Jan 12, 2024 · Scaling the size of these datasets requires anno- tating a massive set of instructions with detailed responses, which can be done either manually or.

Feb 2, 2024 · Large-Scale Linked Data Integration Using Probabilistic Reasoning and Crowdsourcing. In: VLDB Journal, Volume 22, Issue 5 (2013), Page 665-687, Special ...

Apr 26, 2024 · As a result it opens up new practical use cases, including scaling up to high-dimensional input spaces and large pools of unlabelled data. Given its simplicity ...

Jan 15, 2024 · Combining human and machine intelligence in large-scale crowdsourcing. ... Scaling up crowd-sourcing to very large datasets: A case for active learning.

Aug 2, 2023 · Data labeling costs. Creating labeled data for large-scale training is quite expensive and time-consuming. The pricing details of Data Labelling by Google Cloud ...

Sep 5, 2023 · These include improving existing dataset, crowd sourcing and active learning based methods ... The challenge of scaling up crowdsourced labeling is significant.

Jun 24, 2024 · This method can effectively handle outlier detection in large datasets with more than five dimensions but has high time complexity. Another family of methods is ...

Oct 6, 2023 · Crowdsourcing is an efficient source of social values data, if reliable. •. Flickr and PPGIS crowdsourced datasets sample different aspects of social values ...

Dec 12, 2023 · One major advantage of crowdsourcing is scalability—by distributing tasks to a large, global pool of contributors on digital platforms, projects can process ...