An event happening in the world is often made of different activities and actions that can unfold simultaneously or sequentially within a few seconds.
In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.
In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing.
Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.
Quantitative results support the visually consistency of our data and we demonstrate a tissue type-based visual attention aid as a sample tool that could be developed from our database.