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Papers that changed AI, with a plain-language explainer for each.
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- 1989
Backpropagation Applied to Handwritten Zip Code Recognition
Neural Computation
In 1989 LeCun and colleagues at AT&T Bell Laboratories trained a convolutional network with backpropagation to read handwritten zip-code digits from U.S. mail, reaching 5.0% test error. The work showed that the convolutional design could be learned end to end on a real task.
- 1988
ALVINN: An Autonomous Land Vehicle in a Neural Network
ANIPS11
A 3-layer back-propagation network that takes camera and laser range finder images and outputs the direction to steer, trained on simulated road images and tested on Carnegie Mellon's autonomous navigation test vehicle.
- 1985
A Learning Algorithm for Boltzmann Machines
Cognitive Science 9
A 1985 Cognitive Science paper by Ackley, Hinton and Sejnowski that presents a general learning rule for connectionist networks and reports simple examples of efficient internal representations.
- 1958
The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
Psychological Review
In 1958 Frank Rosenblatt described the perceptron, a model whose connection strengths change with training so that it learns to classify stimuli by trial and error. It became the starting point for trainable neural networks.