Memory for Small Cues (SDMSCue), a new variant of Sparse Distributed Memory (SDM) that is capable of handling small cues. SDM is a content-addressable memory technique that relies on similar memory items tending to be clustered together in the same region or subspace of the semantic space. SDM has been used before as associative memory or control structure for software agents. In this context, small cues refer to input cues that are presented to SDM for reading associations; but have many missing parts or fields from them. The original SDM failed to handle such a problem. Hence, our work with SDMSCue comes to overcome this pitfall. The main idea in our work; is the projection of the semantic space on a smaller subspace; that is selected bas...
This paper presents detailed simulation results on a modified Sparse Distributed Memory (SDM) system...
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is pre...
For a number of years, artificial neural networks have been used for a variety of applications to au...
In this work, I present Sparse Distributed Memory for Small Cues (SDMSCue), a new variant of Sparse ...
In this work, I present a review on Sparse Distributed Memory for Small Cues (SDMSCue), a variant of...
The Sparse Distributed Memory (SDM)[1] was originally developed to tackle the problem of storing lar...
Abstract. Sparse distributed memory is an auto-associative memory system that stores high dimensiona...
Sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean ...
We describe the use of genetic algorithms to initialize a set of hard locations that constitutes the...
Challenging AI applications, such as cognitive architectures, natural language understanding, and vi...
Abstract—The Sparse Distributed Memory (SDM) proposed by Kanerva provides a simple model for human l...
This paper presents detailed simulation results on a modified Sparse Distributed Memory (SDM) system...
It has been suggested that in certain situations it would make sense to use different activation pro...
The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massivel...
Abstract- This paper presents a Modified Sparse Distributed Memory architecture for use in software ...
This paper presents detailed simulation results on a modified Sparse Distributed Memory (SDM) system...
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is pre...
For a number of years, artificial neural networks have been used for a variety of applications to au...
In this work, I present Sparse Distributed Memory for Small Cues (SDMSCue), a new variant of Sparse ...
In this work, I present a review on Sparse Distributed Memory for Small Cues (SDMSCue), a variant of...
The Sparse Distributed Memory (SDM)[1] was originally developed to tackle the problem of storing lar...
Abstract. Sparse distributed memory is an auto-associative memory system that stores high dimensiona...
Sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean ...
We describe the use of genetic algorithms to initialize a set of hard locations that constitutes the...
Challenging AI applications, such as cognitive architectures, natural language understanding, and vi...
Abstract—The Sparse Distributed Memory (SDM) proposed by Kanerva provides a simple model for human l...
This paper presents detailed simulation results on a modified Sparse Distributed Memory (SDM) system...
It has been suggested that in certain situations it would make sense to use different activation pro...
The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massivel...
Abstract- This paper presents a Modified Sparse Distributed Memory architecture for use in software ...
This paper presents detailed simulation results on a modified Sparse Distributed Memory (SDM) system...
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is pre...
For a number of years, artificial neural networks have been used for a variety of applications to au...