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Human-in-the-Loop Design Cycles – A Process Framework that Integrates Design Sprints, Agile Processes, and Machine Learning with Humans
Demands on more transparency of the backbox nature of machine learning m...
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A psychophysics approach for quantitative comparison of interpretable computer vision models
The field of transparent Machine Learning (ML) has contributed many nove...
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Sampling Humans for Optimizing Preferences in Coloring Artwork
Many circumstances of practical importance have performance or success m...
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Randomized Algorithms for the Loop Cutset Problem
We show how to find a minimum weight loop cutset in a Bayesian network w...
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Semantic Human Matting
Human matting, high quality extraction of humans from natural images, is...
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Human-Centric Data Cleaning [Vision]
Data Cleaning refers to the process of detecting and fixing errors in th...
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User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics
Topic models are widely used analysis techniques for clustering document...
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Formalizing Interruptible Algorithms for Human over-the-loop Analytics
Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. Algorithms that include humans "in-the-loop" have proved beneficial for accuracy by allowing a user to provide direction in these situations, but the slowness of human interactions causes execution times to increase exponentially. Thus, we seek to formalize frameworks that include humans "over-the-loop", giving the user an option to intervene when they deem it necessary while not having user feedback be an execution requirement. With this strategy, we hope to increase the accuracy of solutions with minimal losses in execution time. This paper describes our vision of this strategy and associated problems.
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