David E. Rumelhart

发表

The neurobiological significance of the new learning models

David Zipser, David E. Rumelhart, D. Rumelhart, 1993 .

On learning the past-tenses of English verbs: implicit rules or parallel distributed processing

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1986 .

A simulation-based tutorial system for exploring parallel distributed processing

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1988 .

Facial Expression Recognition Using a Neural Network

Christine L. Lisetti, David E. Rumelhart, D. Rumelhart, 1998, FLAIRS.

Forecasting Chaotic Computational Ecosystems [1]

David E. Rumelhart, Andreas S. Weigend, Bernardo A. Huberman, 2018, 1990 Lectures in Complex Systems.

A Connectionist Model of the Owl's Sound Localization System

Eric I. Knudsen, David E. Rumelhart, Daniel J. Rosen, 1993, NIPS.

Explorations in Parallel Distributed Processing - Macintosh version: A Handbook of Models, Programs, and Exercises

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1989 .

Open Questions About Computation in Cerebral Cortex

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

Back-Propagation: Theory, Architecture, and Applications

Yves Chauvin, David E. Rumelhart, Y. Chauvin, 1995 .

A computer system for automatically constructing stimulus material

James A. Levin, David E. Rumelhart, Albert L. Stevens, 1977 .

An Introduction to Linear Algebra in Parallel Distributed Processing

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

Integrated Segmentation and Recognition of Hand-Printed Numerals

James D. Keeler, Wee Kheng Leow, David E. Rumelhart, 1990, NIPS.

Analogical Processes in Learning

Donald A. Norman, David E. Rumelhart, D. Rumelhart, 1980 .

Active Semantic Networks as a Model of Human Memory

Donald A. Norman, David E. Rumelhart, D. Rumelhart, 1973, IJCAI.

An interactive activation model of context effects in letter perception: part 1.: an account of basic findings

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1988 .

Implementing neural networks

David E. Rumelhart, Walter J. Savitch, Max Stanford Tomlinson, 1988 .

An Interactive Activation Model of the Effect of Context in Perception. Part 2

David E. Rumelhart, James L McCelland, D. Rumelhart, 1980 .

A Self-Organizing Integrated Segmentation and Recognition Neural Net

James D. Keeler, David E. Rumelhart, D. Rumelhart, 1991, NIPS.

Context-Dependent Multiple Distribution Phonetic Modeling with MLPs

Horacio Franco, Nelson Morgan, Michael Cohen, 1992, NIPS.

Ensembles for supervised classification learning

David E. Rumelhart, Ofer Matan, D. Rumelhart, 1996 .

Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks

David E. Rumelhart, Richard Durbin, D. Rumelhart, 1989, Neural Computation.

Accretion, tuning and restructuring: Three modes of learning

Donald A. Norman, David E. Rumelhart, D. Rumelhart, 1976 .

Analogical Processes in Learning. Technical Report, April 1979-August 1980.

Donald A. Norman, David E. Rumelhart, D. Rumelhart, 1980 .

P3: A Parallel Network Simulating System

James L. McClelland, David E. Rumelhart, 1987 .

Forward Models: Supervised Learning with a Distal Teacher

Michael I. Jordan, David E. Rumelhart, D. Rumelhart, 1992, Cogn. Sci..

Predicting the Future: a Connectionist Approach

David E. Rumelhart, Andreas S. Weigend, Bernardo A. Huberman, 1990, Int. J. Neural Syst..

The PDP Perspective

James L. McClelland, David E. Rumelhart, 1987 .

Strategies for developing effective neural network applications

David E. Rumelhart, 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

On Evaluating Story Grammars

David E. Rumelhart, D. Rumelhart, 1980, Cogn. Sci..

Brain style computation: learning and generalization

David E. Rumelhart, D. Rumelhart, 1990 .

Reflections on Cognition and Parallel Distributed Processing

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

Neural and Conceptual Interpretation of PDP Models

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

Neuroscience and connectionist theory

David E. Rumelhart, Mark A. Gluck, D. Rumelhart, 2013 .

Learning representations by back-propagating errors

Geoffrey E. Hinton, Ronald J. Williams, David E. Rumelhart, 1986, Nature.

Parallel distributed processing, 9th Edition

David E. Rumelhart, D. Rumelhart, 1989 .

A process model for long-term memory.

Donald A. Norman, David E. Rumelhart, Peter H. Lindsay, 1972 .

Internal World Models and Supervised Learning

Michael I. Jordan, David E. Rumelhart, D. Rumelhart, 1991, ML.

Backpropagation: the basic theory

Yves Chauvin, David E. Rumelhart, Richard M. Golden, 1995 .

The Representation of Knowledge in Memory 1

David E. Rumelhart, Andrew Ortony, D. Rumelhart, 2017 .

Backpropagation: theory, architectures, and applications

Yves Chauvin, David E. Rumelhart, Y. Chauvin, 1995 .

Toward an interactive model of reading.

David E. Rumelhart, D. Rumelhart, 1994 .

A multicomponent theory of the perception of briefly exposed visual displays

David E. Rumelhart, D. Rumelhart, 1970 .

Product Units with Trainable Exponents and Multi-Layer Networks

David E. Rumelhart, Richard Durbin, D. Rumelhart, 1989, NATO Neurocomputing.

The architecture of mind: a connectionist approach

David E. Rumelhart, D. Rumelhart, 1989 .

Toward a microstructural account of human reasoning

David E. Rumelhart, D. Rumelhart, 1989 .

Feature Discovery by Competive Learning

David Zipser, David E. Rumelhart, D. Rumelhart, 1986, Cogn. Sci..

Introduction to human information processing

David E. Rumelhart, D. Rumelhart, 1977 .

The basic ideas in neural networks

Bernard Widrow, David E. Rumelhart, Michael A. Lehr, 1994, CACM.

BACK-PROPAGATION, WEIGHT-ELIMINATION AND TIME SERIES PREDICTION

David E. Rumelhart, Andreas S. Weigend, Bernardo A. Huberman, 1991 .

Learning and connectionist representations

Peter M. Todd, David E. Rumelhart, D. Rumelhart, 1993 .

An Analysis of the Delta Rule and the Learning of Statistical Associations

James L. McClelland, David E. Rumelhart, 1987 .

s Multiple-State Context-Dependent Phonetic Modeling with MLP

Horacio Franco, Nelson Morgan, David E. Rumelhart, 1992 .

Feature Discovery by Competive Learning

David Zipser, David E. Rumelhart, D. Rumelhart, 1985, Cogn. Sci..

Context-dependent connectionist probability estimation in a hybrid hidden Markov model-neural net speech recognition system

Horacio Franco, Nelson Morgan, Michael Cohen, 1994, Comput. Speech Lang..

A Glossary of Terms Including a Classification of Typing Errors

Jonathan Grudin, Donald A. Norman, David E. Rumelhart, 1983 .

Representation in Memory.

Donald A. Norman, David E. Rumelhart, D. Rumelhart, 1983 .

The Logic of Activation Functions

James L. McClelland, David E. Rumelhart, 1987 .

Context-Dependent Connectionist Probability Estimation in a Hybrid HMM-Neural Net Speech Recognition

Michael Cohen, Horacio Franco, Nelson Morgan, 1994 .

Biologically Plausible Models of Place Recognition and Goal Location

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

Combining Neural Networks And Hidden Markov Models For Continuous Speech Recognition

Yochai Konig, Horacio Franco, Nelson Morgan, 1992 .

Neural networks: applications in industry, business and science

Bernard Widrow, David E. Rumelhart, Michael A. Lehr, 1994, CACM.

Learning internal representations by error propagation

Geoffrey E. Hinton, Ronald J. Williams, David E. Rumelhart, 1986 .

Schemata and Sequential Thought Processes in PDP Models

Geoffrey E. Hinton, James L. McClelland, David E. Rumelhart, 1986 .

Self-organizing integrated segmentation and recognition neural network

David E. Rumelhart, James David Keeler, D. Rumelhart, 1992, Defense, Security, and Sensing.

Hybrid neural network/hidden Markov model continuous-speech recognition

Horacio Franco, Nelson Morgan, Michael Cohen, 1992, ICSLP.

The appeal of parallel distributed processing

Geoffrey E. Hinton, James L. McClelland, David E. Rumelhart, 1986 .

Parallel Distributed Processing: Explorations in the Microstructure of Cognition : Psychological and Biological Models

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1986 .

Acoustic Classification with Neural Networks

David E. Rumelhart, Charles S. Weaver, John S. Ostrem, 1990 .

Generalization by Weight-Elimination with Application to Forecasting

David E. Rumelhart, Andreas S. Weigend, Bernardo A. Huberman, 1990, NIPS.

A distributed model of human learning and memory

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1986 .

Weight elimination and effective network size

David E. Rumelhart, Andreas S. Weigend, D. Rumelhart, 1994, COLT 1994.

Simulating a Skilled Typist: A Study of Skilled Cognitive-Motor Performance

Donald A. Norman, David E. Rumelhart, D. Rumelhart, 1982, Cogn. Sci..

Inductive Principles of Statistics and Learning Theory: V. N. Vapnik

David E. Rumelhart, 2013 .

Certain Aspects of the Anatomy and Physiology of the Cerebral Cortex

James L. McClelland, David E. Rumelhart, 1987 .

A Neural Network That Classifies Mass Spectra

David E. Rumelhart, D. Rumelhart, Bo Curry, 2001 .

An Interactive Activation Model of the Effect of Context in Perception, Part I. Technical Report, April, 1979 through March, 1980. Report No. 8002.

James L. McClelland, David E. Rumelhart, 1980 .

The LNR approach to human information processing

Donald A. Norman, David E. Rumelhart, D. Rumelhart, 1981, Cognition.

Information Processing in Dynamical Systems: Foundations of Harmony Theory

James L. McClelland, David E. Rumelhart, 1987 .

State-Dependent Factors Influencing Neural Plasticity: A Partial Account of the Critical Period

James L. McClelland, David E. Rumelhart, 1987 .

Understanding Understanding

David E. Rumelhart, .

MSnet: A Neural Network which Classifies Mass Spectra

David E. Rumelhart, D. Rumelhart, Bo U Curry, 1990 .