In this paper we describe a probabilistic approach for supervised learning when we have multiple annotators providing (possibly noisy) labels but no absolute ...
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Abstract. For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels.
In this paper we describe a probabilistic approach for supervised learning when we have multiple annotators providing (possibly noisy) labels but no absolute ...
With the advent of crowdsourcing services it has become quite cheap and reasonably effective to get a data set labeled by multiple annotators in a short amount ...
Dec 24, 2020 · We provide a new perspective to decompose annotation noise into common noise and individual noise and differentiate the source of confusion.
Jul 18, 2023 · In this work, we investigate the reliability of annotations to improve learning from crowds. Specifically, we first project annotator and data ...
Sep 24, 2023 · Ustalov et al., (2024). Learning from Crowds with Crowd-Kit. Journal of Open Source Software, 9(96), 6227, https://doi.org/10.21105/joss.
Sep 6, 2017 · In this paper, we address the problem of learning deep neural networks from crowds. We begin by describing an EM algorithm for jointly learning ...
In this paper, we employ a probabilistic model for learning from multiple annotators that can also learn the annotator ex- pertise even when their expertise may ...
Abstract. Crowdsourcing services are often used to collect a large amount of labeled data for machine learning. Although they provide us an easy way to get ...