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pro vyhledávání: '"Cacciarelli, Davide"'
We present a scalable framework designed to craft efficient lightweight models for video object detection utilizing self-training and knowledge distillation techniques. We scrutinize methodologies for the ideal selection of training images from video
Externí odkaz:
http://arxiv.org/abs/2404.10411
Autor:
Cacciarelli, Davide, Kulahci, Murat
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attentio
Externí odkaz:
http://arxiv.org/abs/2302.08893
In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial
Externí odkaz:
http://arxiv.org/abs/2302.00422
Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors often requir
Externí odkaz:
http://arxiv.org/abs/2212.13067
The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is
Externí odkaz:
http://arxiv.org/abs/2207.09874
Akademický článek
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Autor:
Cacciarelli, Davide, Kulahci, Murat
Publikováno v:
In Computers and Chemical Engineering July 2022 163
Publikováno v:
International Journal of Semantic Computing; Sep2024, Vol. 18 Issue 3, p417-435, 19p
Autor:
Cacciarelli, Davide1,2 (AUTHOR), Kulahci, Murat1,3 (AUTHOR) muku@dtu.dk
Publikováno v:
Quality Engineering. 2023, Vol. 35 Issue 4, p741-750. 10p.
Akademický článek
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