Unsupervised Learning
Unsupervised learning aims to extract meaningful patterns and structures from unlabeled data, addressing the limitations of supervised methods that require extensive labeled datasets. Current research focuses on developing robust algorithms and model architectures, such as autoencoders, generative models (like Gaussian Mixture Models), and contrastive learning approaches, to improve clustering, anomaly detection, and representation learning. These advancements are impacting diverse fields, including medical image analysis, financial market prediction, and signal processing, by enabling efficient analysis of large, unlabeled datasets and reducing reliance on expensive manual labeling.
Papers
Unsupervised learning of spatially varying regularization for diffeomorphic image registration
Junyu Chen, Shuwen Wei, Yihao Liu, Zhangxing Bian, Yufan He, Aaron Carass, Harrison Bai, Yong Du
Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs
Shiyuan Qu, Fenglian Dong, Zhiwei Wei, Chao Shang
Anomaly Detection and RFI Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches
Ben Jacobson-Bell, Steve Croft, Carmen Choza, Alex Andersson, Daniel Bautista, Vishal Gajjar, Matthew Lebofsky, David H. E. MacMahon, Caleb Painter, Andrew P. V. Siemion
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data
Jiin Im, Yongho Son, Je Hyeong Hong