System and method for addressing overfitting in a neural network

A computer-implemented method comprising:

• obtaining a plurality of training cases;
• and
• training a neural network having a plurality of layers on the plurality of training cases, each of the layers including one or more feature detectors, and each of the feature detectors having a corresponding set of weights, wherein training the neural network on the plurality of training cases comprises, for each of the training cases respectively:
• determining one or more feature detectors to disable during processing of the training case,
• disabling the one or more feature detectors in accordance with the determining, and
• processing the training case using the neural network with the one or more feature detectors disabled to generate a predicted output for the training case.

A system comprising one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:

• obtaining a plurality of training cases;
• and
• training a neural network having a plurality of layers on the plurality of training cases, each of the layers including one or more feature detectors, and each of the feature detectors having a corresponding set of weights, wherein training the neural network on the plurality of training cases comprises, for each of the training cases respectively:
• determining one or more feature detectors to disable during processing of the training case,
• disabling the one or more feature detectors in accordance with the determining, and
• processing the training case using the neural network with the one or more feature detectors disabled to generate a predicted output for the training case.

A computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• obtaining a plurality of training cases;
• and
• training a neural network having a plurality of layers on the plurality of training cases, each of the layers including one or more feature detectors, and each of the feature detectors having a corresponding set of weights, wherein training the neural network on the plurality of training cases comprises, for each of the training cases respectively:
• determining one or more feature detectors to disable during processing of the training case,
• disabling the one or more feature detectors in accordance with the determining, and
• processing the training case using the neural network with the one or more feature detectors disabled to generate a predicted output for the training case.