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Computing with Residue Numbers in High-Dimensional Representation
We introduce Residue Hyperdimensional Computing, a computing framework that unifies residue number systems with an algebra defined over random, high-dimensional vectors.
Learning and generalization of compositional representations of visual scenes
In contrast to learning category labels, here we train deep neural networks to output the full compositional vector description of an input image.
Computing with Hypervectors for Efficient Speaker Identification
With only 1. 02k active parameters and a 128-minute pass through the training data we achieve Top-1 and Top-5 scores of 31% and 52% on the VoxCeleb1 dataset of 1, 251 speakers.
Vector Symbolic Architectures as a Computing Framework for Emerging Hardware
We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware.
High-dimensional distributed semantic spaces for utterances
High-dimensional distributed semantic spaces have proven useful and effective for aggregating and processing visual, auditory, and lexical information for many tasks related to human-generated data.
Language Recognition using Random Indexing
Random Indexing is a simple implementation of Random Projections with a wide range of applications.
