Traditional computer vision and machine learning algorithms have been largely studied in a centralized setting, where all the processing is performed at a single central location....
Object recognition has made great strides recently. However, the best methods, such as those based on kernelSVMs are highly computationally intensive. The problem of how to accele...
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Methods for cleaning up (or recognizing) states of a neural network are crucial for the functioning of many neural cognitive models. For example, Vector Symbolic Architectures pro...
Terrence C. Stewart, Yichuan Tang, Chris Eliasmith
Information Retrieval systems can be improved by exploiting context information such as user and document features. This article presents a model based on overlapping probabilistic...