A survey of human-in-the-loop for machine learning
TL;DR: In this paper , the authors survey existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) improving model performance from data processing, (2) the work of improving model model performance through interventional model training, and (3) the design of the system independent human in the loop.
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About: This article is published in Future Generation Computer Systems. The article was published on 01 Oct 2022. and is currently open access. The article focuses on the topics: Computer science & Human-in-the-loop.
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Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges
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Information extraction meets the semantic web: a survey
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Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?
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Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering
TL;DR: The authors used adversarial evaluation to stress-test a model's understanding of natural language and found that the resulting adversarial examples are limited in complexity and are ineffective in the presence of superficial patterns.
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Challenges and opportunities: from big data to knowledge inAI 2.0
TL;DR: It is concluded that integrating data-driven machine learning with human knowledge can effectively lead to explainable, robust, and general AI.
Video Object Segmentation and Tracking: A Survey
TL;DR: This survey aims to provide a comprehensive review of the state-of-the-art VOST methods, classify these methods into different categories, and identify new trends.
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