Method for topological optimization of graph-based models

A system comprising:

• non-transitory computer readable media that include instructions thereon that, in response to execution by one or more processors, control performance of operations comprising:
• receive a graph-based model in a first format, including a topology of the graph-based model in which the topology of the graph-based model is static;
• encode the graph-based model from the first format into a neural network topology optimizer (NNTO) readable format such that the topology of the encoded graph-based model is configured to be altered;
• create a first plurality of entities based on at least a same portion of the encoded graph-based model, each entity of the first plurality of entities including a different topological mutation to at least the same portion of the encoded graph-based model;
• perform a learning operation by tuning parameters of the first plurality of entities to produce an optimization score for each entity of the first plurality of entities, the optimization score based on selectable features configured to be optimized;
• perform a validation operation which validates each entity of the first plurality of entities using a common validation data set;
• determine that an improvement in validation performance for at least one entity of the first plurality of entities is within a threshold amount of improvement;
• select a solution entity of the first plurality of entities with a highest optimization score and that produced at least the threshold amount of improvement in the validation performance;
• and
• add the selected solution entity into the graph-based model in place of the same portion.

A system, comprising:

• one or more processors;
• non-transitory computer readable media that include instructions thereon that, in response to execution by the one or more processors, control performance of operations comprising:
• receive a graph-based model in a first format, including a topology of the graph-based model in which the topology of the graph-based model is static;
• encode the graph-based model from the first format into a neural network topology optimizer (NNTO) readable format such that the topology of the encoded graph-based model is configured to be altered;
• create a first plurality of entities based on at least a same portion of the encoded graph-based model, each entity of the first plurality of entities including a different topological mutation to at least the same portion of the encoded graph-based model;
• receive an optimization score for each entity of the first plurality of entities, the optimization score based on selectable features configured to be optimized;
• receive data relating to a validation operation which validates each entity of the first plurality of entities;
• determine that an improvement in validation performance for at least one entity of the first plurality of entities is within a threshold amount of improvement;
• select a solution entity of the first plurality of entities with a highest optimization score and that produced at least the threshold amount of improvement in the validation performance;
• and
• add the selected solution entity into the graph-based model in place of the same portion.