36 citations · 112 across the 14 of their papers we have counts for
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cs.LG2017★ 26 cited
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…
cs.LG2017
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…
cs.LG2017★ 4 cited
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…