Exploring Probabilistic Models for Semi-supervised Learning

arXiv:2404.04199v1 Announce Type: new
Abstract: 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 …

abstract advanced ai systems applications arxiv cs.lg practical safety semi-supervised semi-supervised learning ssl studies supervised learning systems tasks thesis type uncertainty