6 papers
Gluing Neural Networks Symbolically Through Hyperdimensional Computing
Peter Sutor, Dehao Yuan, Douglas Summers-Stay +2
Hyperdimensional Computing affords simple, yet powerful operations to create long Hyperdimensional Vectors (hypervectors) that can efficiently encode information, be used for learn…
Representing Sets as Summed Semantic Vectors
Douglas Summers-Stay, Peter Sutor, Dandan Li
Representing meaning in the form of high dimensional vectors is a common and powerful tool in biologically inspired architectures. While the meaning of a set of concepts can be sum…
A Computational Theory for Life-Long Learning of Semantics
Peter Sutor, Douglas Summers-Stay, Yiannis Aloimonos
Semantic vectors are learned from data to express semantic relationships between elements of information, for the purpose of solving and informing downstream tasks. Other models ex…
Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph
Douglas Summers-Stay
Representing knowledge as high-dimensional vectors in a continuous semantic vector space can help overcome the brittleness and incompleteness of traditional knowledge bases. We pre…
Graphcut Texture Synthesis for Single-Image Superresolution
Douglas Summers-Stay
Texture synthesis has proven successful at imitating a wide variety of textures. Adding additional constraints (in the form of a low-resolution version of the texture to be synthes…
Using a Distributional Semantic Vector Space with a Knowledge Base for Reasoning in Uncertain Conditions
Douglas Summers-Stay, Clare Voss, Taylor Cassidy
The inherent inflexibility and incompleteness of commonsense knowledge bases (KB) has limited their usefulness. We describe a system called Displacer for performing KB queries exte…