Techniques for determining artificial neural network topologies

At least one non-transitory computer-readable medium comprising a set of instructions that, in response to being executed by a processor circuit, cause the processor circuit to:

• determine a collection of sample sets from a dataset, each sample set in the collection of sample sets drawn randomly from the dataset with replacement between drawings;
• generate a graphical model tree based on the sample set collection and the dataset;
• partition the graphical model tree into a plurality of subgraphs, one subgraph associated with each sample set in the collection of sample sets;
• recursively partition each of the plurality of subgraphs into a plurality of levels of subgraphs;
• calculate a score of each subgraph at each level of recursion, the score representing a likelihood of explaining a sample set of data by the subgraph;
• at each recursive level of subgraphs, choose a subgraph with a highest score for inclusion at a next highest level of the graphical model tree and discard all other subgraphs at that level of subgraphs, until reaching a highest level of the graphical model tree;
• and
• determine a topology for a neural network for each sample set based on the subgraph associated with that sample set.

A computer-implemented method, comprising:

• determining a collection of sample sets from a dataset, each sample set in the collection of sample sets drawn randomly from the dataset with replacement between drawings;
• generating a graphical model tree based on the sample set collection and the dataset;
• partitioning the graphical model tree into a plurality of subgraphs, one subgraph associated with each sample set in the collection of sample sets;
• recursively partitioning each of the plurality of subgraphs into a plurality of levels of subgraphs;
• calculating a score of each subgraph at each level of recursion, the score representing a likelihood of explaining a sample set of data by the subgraph;
• at each recursive level of subgraphs, choosing a subgraph with a highest score for inclusion at a next highest level of the graphical model tree and discarding all other subgraphs at that level of subgraphs, until reaching a highest level of the graphical model tree;
• and
• determining a topology for a neural network for each sample set based on the subgraph associated with that sample set.