Background: Recently, Almeida and Vinga offered a new approach for the representation of arbitrary discrete sequences, referred to as Universal Sequence Maps (USM), and discussed ...
Pseudo-likelihood and contrastive divergence are two well-known examples of contrastive methods. These algorithms trade off the probability of the correct label with the probabili...
This work presents a general rank-learning framework for passage ranking within Question Answering (QA) systems using linguistic and semantic features. The framework enables query...
Matthew W. Bilotti, Jonathan L. Elsas, Jaime G. Ca...
In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set ...
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...