26 citations · 84 across the 7 of their papers we have counts for
4 papers · 1 filter
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization
Ozsel Kilinc, Ismail Uysal
In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specif…
GAR: An efficient and scalable Graph-based Activity Regularization for semi-supervised learning
Ozsel Kilinc, Ismail Uysal
In this paper, we propose a novel graph-based approach for semi-supervised learning problems, which considers an adaptive adjacency of the examples throughout the unsupervised port…
Clustering-based Source-aware Assessment of True Robustness for Learning Models
Ozsel Kilinc, Ismail Uysal
We introduce a novel validation framework to measure the true robustness of learning models for real-world applications by creating source-inclusive and source-exclusive partitions…
Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks
Ozsel Kilinc, Ismail Uysal
In this paper, we discuss a different type of semi-supervised setting: a coarse level of labeling is available for all observations but the model has to learn a fine level of laten…