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4 papers
Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking
James Tompkin, Kwang In Kim, Hanspeter Pfister +1
Large databases are often organized by hand-labeled metadata, or criteria, which are expensive to collect. We can use unsupervised learning to model database variation, but these m…
Context-guided diffusion for label propagation on graphs
Kwang In Kim, James Tompkin, Hanspeter Pfister +1
Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isotropic diffusion, which is induced by the commonly-used graph Laplacian regulariz…
Semi-supervised Learning with Explicit Relationship Regularization
Kwang In Kim, James Tompkin, Hanspeter Pfister +1
In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points. Ex…
Local High-order Regularization on Data Manifolds
Kwang In Kim, James Tompkin, Hanspeter Pfister +1
The common graph Laplacian regularizer is well-established in semi-supervised learning and spectral dimensionality reduction. However, as a first-order regularizer, it can lead to…