Mitigating damage to multi-layer networks

A system for mitigating damage to multi-layer networks, the system comprising:

• one or more processors;
• and
• a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• providing a multi-layer network comprising a plurality of nodes arranged into a plurality of layers;
• receiving a plurality of training cases for training the multi-layer network;
• and
• for each training case of the plurality of training cases:
• determining a set of dropout nodes within the multi-layer network, based at least on a damage model having a probability of a node being selected for dropout that is based at least on a target operating environment of the multi-layer network, wherein the probability of a node being selected for dropout is spatially correlated;
• and
• training the multi-layer network with the determined set of dropout nodes disabled, wherein a different set of dropout nodes is determined for different training cases.

A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method for mitigating damage to multi-layer networks, the method comprising:

• providing a multi-layer network comprising a plurality of nodes arranged into a plurality of layers;
• receiving a plurality of training cases for training the multi-layer network;
• and
• for each training case of the plurality of training cases:
• determining a set of dropout nodes within the multi-layer network, based at least on a damage model having a probability of a node being selected for dropout that is based at least on a target operating environment of the multi-layer network, wherein the probability of a node being selected for dropout is spatially correlated;
• and
• training the multi-layer network with the determined set of dropout nodes disabled, wherein a different set of dropout nodes is determined for different training cases.