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In this section, we design a supervised machine learning framework to aggregate information from various ranking techniques for link prediction in social networks.. In a nutshell,

Proposed a weakly supervised framework for organ segmentation in CT images Examined multiple loss functions with balanced cross entropy resulting in best performance.

In this paper, we propose (1) a novel theoretical framework that can be applied to a wide family of structured prediction settings able to capture potential local structure in the

In this section, we design a supervised machine learning framework to aggregate information from various ranking techniques for link prediction in social networks.. In a nutshell,

The algorithm performs edge prediction through a machine learning framework that accommodates the breadth and depth of information contained in Hetionet v1.0 ( Himmelstein

Motivated by applications to machine learning and imaging science, we study a class of online and stochastic optimization problems with loss functions that are not Lipschitz

2 Weakly Supervised Learning & Negative Evidence Models Pooling for Weakly Supervised Learning with ConvNets Weakly Supervised Learning for Deep Structured Prediction..

Navab, Weakly-supervised structured output learning with flexible and latent graphs using high-order loss functions, 2015 IEEE International Conference on Computer Vision (ICCV),