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Delving into Deep Learning

The latest neural networks learn to see and hear, and maybe even dream.

Brian Hayes

From Perceptrons to Connectionism

2014-05HayesFp187.jpgClick to Enlarge ImageArtificial neural networks have had a roller coaster history. In the 1950s Frank Rosenblatt described a class of devices he called perceptrons. To show how they work, he built an electromechanical contraption with 400 photocells as sensors and motor-driven potentiometers to adjust the weights. The stripe-detecting network described above is a particularly simple instance of a perceptron. (This specific model was introduced in 1984 by A. K. Dewdney.)

In 1969 Marvin Minsky and Seymour Papert published a critique of two-layer perceptrons, giving mathematical proofs of their limitations. For example, they showed that no network without hidden layers can distinguish connected geometric figures from those made up of two or more disconnected pieces. Beyond the proofs, Minsky and Papert offered a harsh assessment of the entire neural network field, remarking that much writing on the subject was “without scientific value.”

The Minsky-Papert impossibility proofs applied only to networks without hidden layers. Rosenblatt and others were already experimenting with multilayer devices, but they had trouble finding efficient learning rules. In the aftermath of these setbacks, neural network research languished for a decade. And there was a further misfortune: Rosenblatt died in a boating accident in 1971, on his 43rd birthday.

Interest in neural networks revived in the 1980s under the new brand name of connectionism. A key event was the discovery of an algorithm known as back-propagation, which allowed efficient training of neural networks with three layers: an input layer, an output layer, and a single hidden layer. The technique was first formulated by Paul J. Werbos and was popularized by David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, who demonstrated its successful application in 1986.

As the name suggests, back-propagation reverses the flow of information through the network. Error-correcting signals travel back from the output layer to the hidden layer, then continue on to the input layer. Within each layer, the corrective adjustment is determined by the principle of steepest descent: Incorrect weights are nudged in the direction that causes the greatest change in the output. The process is not guaranteed to find the best possible assignment of weights—it can get trapped in a local optimum—but experiments suggested that densely connected networks seldom succumb to this hazard.

In principle, back-propagation can be applied to networks of any depth, but with multiple hidden layers the procedure tends to bog down. There is also the risk of “overfitting,” where the network learns the training cases too well, responding to irrelevant details that are not present outside the training set.

The neural network roller coaster did not plunge steeply after climbing to the connectionist peak in the 1980s. Nevertheless, 20 years passed before the current frenzy over deep learning began.

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