CONNECTIONIST LEARNING PROCEDURES11This chapter appeared in Volume 40 of Artificial Intelligence in 1989, reprinted with permission of North-Holland Publishing. It is a revised version of Technical Report CMU-CS-87-115, which has the same title and was prepared in June 1987 while the author was at Carnegie Mellon University. The research was supported by contract N00014-86-K-00167 from the Office of Naval Research and by grant IST-8520359 from the National Science Foundation.

Maximum Likelihood from Incomplete Data Via the EM Algorithm

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Optimization by Simulated Annealing

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Learning representations by back-propagating errors

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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

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Adaptive Signal Processing

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Untitled

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A Learning Algorithm for Boltzmann Machines*

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A theory of the learnable

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Beyond regression : new fools for prediction and analysis in the behavioral sciences

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Principles of Neurodynamics.

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Genetic Algorithms and Simulated Annealing

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Adaptive signal processing

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Parallel networks that learn to pronounce English text

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Self-organization of orientation sensitive cells in the striate cortex

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Connectionist Models and Their Properties

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A Maximum Likelihood Approach to Continuous Speech Recognition

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How Does a Brain Build a Cognitive Code?

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The Correlation Theory of Brain Function

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Distributed representations

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Learning and Relearning in Boltzmann Machines

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Feature Discovery by Competitive Learning*

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Parallel models of associative memory

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Inductive Inference: Theory and Methods

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1983 • 895 citations

The function of dream sleep

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How Learning Can Guide Evolution.

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An analogue approach to the travelling salesman problem using an elastic net method

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Learning distributed representations of concepts.

1989 • 792 citations

Temporal credit assignment in reinforcement learning

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A Theory of Adaptive Pattern Classifiers

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OPTIMAL PERCEPTUAL INFERENCE

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A mean field theory learning algorithm for neural networks

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‘Unlearning’ has a stabilizing effect in collective memories

1983 • 425 citations

Associative memory. A system-theoretical approach

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From basic network principles to neural architecture: emergence of spatial-opponent cells.

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Parallel visual computation

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Neural computation by concentrating information in time.

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Connectionist models and their properties

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Learning internal representations by back-propagating errors

1986 • 345 citations

Modular learning in neural networks

1987 • 328 citations

Pattern-recognizing stochastic learning automata

1985 • 309 citations

Cortical connections and parallel processing: Structure and function

1986 • 298 citations

Phoneme Recognition Using Time-Delay Neural Networks

1990 • 284 citations

A marker induction mechanism for the establishment of ordered neural mappings: its application to the retinotectal problem

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From basic network principles to neural architecture: emergence of orientation columns.

1986 • 258 citations

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1986 • 250 citations

Dynamic connections in neural networks

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Using fast weights to deblur old memories

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Optimization strategies gleaned from biological evolution

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Dynamic connections in neural networks

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Learning translation invariant recognition in a massively parallel networks

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Probabilistic Solution of Inverse Problems

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Field theory of self-organizing neural nets

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Associative memory: A system-theoretical approach

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Learning Representations by Recirculation

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Theory of neural-analog reinforcement systems and its application to the brain-model problem

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Learning sets of filters using back-propagation

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A Learning Algorithm for Boltzmann Machines**The research reported here was supported by grants from the System Development Foundation. We thank Peter Brown, Francis Crick, Mark Derthick, Scott Fahlman, Jerry Feldman, Stuart Geman, Gail Gong, John Hopfield, Jay McClelland, Barak Pearlmutter, Harry Printz, Dave Rumelhart, Tim Shallice, Paul Smolensky, Rick Szeliski, and Venkataraman Venkatasubramanian for helpful discussions.Reprint requests should be addressed to David Ackley, Computer Science …

1987 • 108 citations

Selective networks capable of representative transformations, limited generalizations, and associative memory.

1982 • 108 citations

Neural Representation of Conceptual Knowledge.

1986 • 106 citations

Scaling laws for the attractors of Hopfield networks

1985 • 105 citations

Multiresolution computation of visible-surface representations

1984 • 91 citations

Cortical Connections and Parallel Processing: Structure and Function

1987 • 90 citations

Boltzmann machines for speech recognition

1986 • 82 citations

A unified framework for connectionist systems

1988 • 76 citations

Learning Optimal Discriminant Functions through a Cooperative Game of Automata

1987 • 69 citations

Plain talk about neurodevelopmental epistemology

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Complexity of Connectionist Learning with Various Node Functions

1987 • 43 citations

Understanding biological computation: reliable learning and recognition.

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Scaling relationships in back-propagation learning: dependence on training set size

1987 • 33 citations

SELF-ORGANIZING MULTILAYERED NEURAL NETWORK.

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Disjunctive models of Boolean category learning

1987 • 29 citations

Abstraction and representation of continuous variables in connectionist networks

1986 • 24 citations

G-maximization: an unsupervised learning procedure for discovering regularities

1986 • 23 citations

Development of feature-analyzing cells and their columnar organization in a layered self-adaptive network

1988 • 23 citations

Computer Simulation in Brain Science

1988 • 22 citations

G-maximization: An unsupervised learning procedure for discovering regularities

1987 • 18 citations

A vestibulo-ocular reflex with no head movement

1986 • 17 citations

Connectionistic models of boolean category representation

1986 • 15 citations

The evolution of data processing abilities in competing automata

1988 • 5 citations