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Exploring Probabilistic Models for Semi-supervised Learning

arXiv 2404.04199 ↗First posted 5 Apr 2024cs.LGcited by 0

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Abstract (arXiv)

This thesis studies advanced probabilistic models, including both their theoretical foundations and practical applications, for different semi-supervised learning (SSL) tasks. The proposed probabilistic methods are able to improve the safety of AI systems in real applications by providing reliable uncertainty estimates quickly, and at the same time, achieve competitive performance compared to their deterministic counterparts. The experimental results indicate that the methods proposed in the thesis have great value in safety-critical areas, such as the autonomous driving or medical imaging analysis domain, and pave the way for the future discovery of highly effective and efficient probabilistic approaches in the SSL sector.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 41 of its 158 references that are in the map. First posted on arXiv 5 Apr 2024.

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References · 41

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29 more
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