Learning internal representations by error propagation

@inproceedings{Rumelhart1986LearningIR,
  title={Learning internal representations by error propagation},
  author={David E. Rumelhart and Geoffrey E. Hinton and Ronald J. Williams},
  year={1986}
}
Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies
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This contribution presents an overview of the theoretical and practical aspects of the broad family of learning algorithms based on Stochastic Gradient Descent, including Perceptrons, Adalines,
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References

SHOWING 1-10 OF 66 REFERENCES
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
The fundamental principles, basic mechanisms, and formal analyses involved in the development of parallel distributed processing (PDP) systems are presented in individual chapters contributed by
Harmony Theory: Problem Solving, Parallel Cognitive Models, and Thermal Physics.
Abstract : The first paper describes a parallel model designed to solve a class of relatively simple problems from elementary physics, and discusses the implications for models of problem solving in
Harmony Theory: A Mathematical Framework for Stochastic Parallel Processing.
TLDR
As this temperature is lowered, the system appears to display a dramatic tendency to coherently interpret input, even if the evidence for any particular interpretation is very weak.
Inductive Information Retrieval Using Parallel Distributed Computation.
TLDR
The retrieval system described makes dynamic use of the internal structure of a database to infer relationships among items in the database, which can help overcome incompleteness and imprecision in requests for information, as well as in thedatabase itself.
Pattern-recognizing stochastic learning automata
  • A. Barto, P. Anandan
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics
  • 1985
A class of learning tasks is described that combines aspects of learning automation tasks and supervised learning pattern-classification tasks. These tasks are called associative reinforcement
A Learning Algorithm for Boltzmann Machines
TLDR
A general parallel search method is described, based on statistical mechanics, and it is shown how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way.
Analogical Processes in Learning
TLDR
The role of analogy and procedural representation in learning is examined from several domains, including turtle geometry, kinship terms, and the learning of a computer text editor.
Learning by statistical cooperation of self-interested neuron-like computing elements.
  • A. Barto
  • Computer Science
    Human neurobiology
  • 1985
TLDR
It is argued that some of the longstanding problems concerning adaptation and learning by networks might be solvable by this form of cooperativity, and computer simulation experiments are described that show how networks of self-interested components that are sufficiently robust can solve rather difficult learning problems.
Separating Figure from Ground with a Parallel Network
TLDR
The network model is too simplified to serve as a model of human performance, but it does demonstrate that one global property of outlines can be computed through local interactions in a parallel network.
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2
3
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