I will describe my recent results on the automatic development of fixed-width recursive distributed representations of variable-sized hierarchal data structures. One implication of this wolk is that certain types of AI-style data-structures can now be represented in fixed-width analog vectors. Simple inferences can be perfonned using the type of pattern associations that neural networks excel at Another implication arises from noting that these representations become self-similar in the limit Once this door to chaos is opened. many interesting new questions about the representational basis of intelligence emerge, and can (and will) be discussed
Self-organization constitutes an,important paradigm in machine learning with successful applications...
Self-organization constitutes an,important paradigm in machine learning with successful applications...
The paper presents an experimental study of holistic computations over distributed representations (...
A long-standing difficulty for connectionist modeling has been how to represent variable-sized recur...
A long-standing difficulty for connectionist modeling has been how to represent variable-sized recur...
Representation poses important challenges to connectionism. The ability to structurally compose repr...
We present an approach to the structure unification problem using distributed representations of hie...
A naturally structured information is typical in symbolic processing. Nonetheless, learning in conne...
A structured organization of information is typically required by symbolic processing. On the other ...
We look at distributed representation of structure with variable binding, that is natural for neural...
This dissertation covers my attempts to confront the challenge and promise of multiplicative represe...
Self-organizing models constitute valuable tools for data visualization, clustering, and data mining...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
Self-organizing models constitute valuable tools for data visualization, clustering, and data mining...
We present an approach to the structure unification problem using distributed representations of hie...
Self-organization constitutes an,important paradigm in machine learning with successful applications...
Self-organization constitutes an,important paradigm in machine learning with successful applications...
The paper presents an experimental study of holistic computations over distributed representations (...
A long-standing difficulty for connectionist modeling has been how to represent variable-sized recur...
A long-standing difficulty for connectionist modeling has been how to represent variable-sized recur...
Representation poses important challenges to connectionism. The ability to structurally compose repr...
We present an approach to the structure unification problem using distributed representations of hie...
A naturally structured information is typical in symbolic processing. Nonetheless, learning in conne...
A structured organization of information is typically required by symbolic processing. On the other ...
We look at distributed representation of structure with variable binding, that is natural for neural...
This dissertation covers my attempts to confront the challenge and promise of multiplicative represe...
Self-organizing models constitute valuable tools for data visualization, clustering, and data mining...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
Self-organizing models constitute valuable tools for data visualization, clustering, and data mining...
We present an approach to the structure unification problem using distributed representations of hie...
Self-organization constitutes an,important paradigm in machine learning with successful applications...
Self-organization constitutes an,important paradigm in machine learning with successful applications...
The paper presents an experimental study of holistic computations over distributed representations (...