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stat.ML2018
Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm
Elif Vural
Traditional machine learning algorithms assume that the training and test data have the same distribution, while this assumption does not necessarily hold in real applications. Dom…
stat.ML2018
Domain Adaptation on Graphs by Learning Aligned Graph Bases
Mehmet Pilanci, Elif Vural
A common assumption in semi-supervised learning with graph models is that the class label function varies smoothly on the data graph, resulting in the rather strict prior that the…
stat.ML2018
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification
Jeremy Aghaei Mazaheri, Elif Vural, Claude Labit +1
Sparse representations using overcomplete dictionaries have proved to be a powerful tool in many signal processing applications such as denoising, super-resolution, inpainting, com…