We consider a general family of problems of which the output space admits vector-valued structure, covering a broad family of important domains, e. g. multi-label learning and multi-class classification.
Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data.
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.
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.
Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.
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.