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Wang, Z. (author)
Machine learning aims to solve a task with a certain algorithm or statistical model that is trained on data, with or without labels. As a subcategory of machine learning, deep learning achieves good performance with its flexibility on end-to-end representation learning and architecture design. Despite the successes of deep learning, the output...
doctoral thesis 2024
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Raman, C.A. (author)
Over the last three decades, the social roots of human intelligence have come to influence the development of artificial intelligence (AI). Researchers in AI have moved beyond agents operating in isolation towards developing socially situated agents that can operate in the real world. Meanwhile, researchers in the social sciences have been...
doctoral thesis 2023
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Viering, T.J. (author)
This dissertation focuses on safety in machine learning. Our adopted safety notion is related to robustness of learning algorithms. Related to this concept, we touch upon three topics: explainability, active learning and learning curves.<br/><br/>Complex models can often achieve better performance compared to simpler ones. Such larger models are...
doctoral thesis 2023
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Mourragui, S.M.C. (author)
Extensive efforts in cancer research over the past decades have markedly improved diagnosis and treatments, leading to better outcomes for cancer patients. Paradoxically, however, these discoveries have begun to shed light on a level of complexity that rules out the emergence of a universal cancer treatment. As any tumor is now known to be...
doctoral thesis 2023
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Mey, A. (author)
The goal of this thesis is to investigate theoretical results in the field of semi-supervised learning, while also linking them to problems in related subjects as class probability estimation.<br/>
doctoral thesis 2020
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Yang, Y. (author)
In recent decades, the availability of a large amount of data has propelled the field of machine learning enormously. Machine learning, however, relies heavily on the availability of annotated data, typically labels indicating to which class a data instance belongs. With the huge amounts of data, this raises the question of how to efficiently...
doctoral thesis 2018
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Cheplygina, V. (author)
Multiple instance learning (MIL) is an extension of supervised learning where the objects are represented by sets (bags) of feature vectors (instances) rather than individual feature vectors. For example, an image can be represented by a bag of instances, where each instance is a patch in that image. Only bag labels are given, however, the...
doctoral thesis 2015
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