We propose a framework for large scale learning and annotation of structured models. The system interleaves interactive labeling (where the current model is used to semiautomate t...
We look at distributed representation of structure with variable binding, that is natural for neural nets and allows traditional symbolic representation and processing. The repres...
Abstract. Activity recognition has attracted increasing attention in recent years due to its potential to enable a number of compelling contextaware applications. As most approache...
Due to its occurrence in engineering domains and implications for natural learning, the problem of utilizing unlabeled data is attracting increasing attention in machine learning....
We develop an object classification method that can learn a novel class from a single training example. In this method, experience with already learned classes is used to facilita...