14 citations · 26 across the 6 of their papers we have counts for
6 papers
Orthogonal Matrices for MBAT Vector Symbolic Architectures, and a "Soft" VSA Representation for JSON
Stephen I. Gallant
Vector Symbolic Architectures (VSAs) give a way to represent a complex object as a single fixed-length vector, so that similar objects have similar vector representations. These ve…
DataWords: Getting Contrarian with Text, Structured Data and Explanations
Stephen I. Gallant, Mirza Nasir Hossain
Our goal is to build classification models using a combination of free-text and structured data. To do this, we represent structured data by text sentences, DataWords, so that simi…
Predicting Severe Sepsis Using Text from the Electronic Health Record
Phil Culliton, Michael Levinson, Alice Ehresman +3
Employing a machine learning approach we predict, up to 24 hours prior, a diagnosis of severe sepsis. Strongly predictive models are possible that use only text reports from the El…
Representing Objects, Relations, and Sequences
Stephen I. Gallant, T. Wendy Okaywe
Vector Symbolic Architectures (VSAs) are high-dimensional vector representations of objects (eg., words, image parts), relations (eg., sentence structures), and sequences for use w…
Automated Generation of Connectionist Expert Systems for Problems Involving Noise and Redundancy
Stephen I. Gallant
When creating an expert system, the most difficult and expensive task is constructing a knowledge base. This is particularly true if the problem involves noisy data and redundant m…
Bayesian Assessment of a Connectionist Model for Fault Detection
Stephen I. Gallant
A previous paper [2] showed how to generate a linear discriminant network (LDN) that computes likely faults for a noisy fault detection problem by using a modification of the perce…