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AssigneeThe Toronto-Dominion Bank
InventorsJesse Cole Cresswell, Brendan Leigh Ross, Anthony Lawrence Caterini, Gabriel Loaiza Ganem, Bradley Craig Anderson Brown
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A computer model is trained to account for data samples in a high-dimensional space as lying on different manifolds, rather than a single manifold to represent the data set, accounting for the data set as a whole as a union of manifolds. Different data samples that may be expected to belong to the same underlying manifold are determined by grouping the data. For generative models, a generative model may be trained that includes a sub-model for each group trained on that group's data samples, such that each sub-model can account for the manifold of that group. The overall generative model includes information describing the frequency to sample from each sub-model to correctly represent the data set as a whole in sampling. Multi-class classification models may also use the grouping to improve classification accuracy by weighing group data samples according to the estimated latent dimensionality of the group.
A system for generative modeling of data on disjoint manifolds, comprising:
A method for generative modeling of data on disjoint manifolds, comprising:
A non-transitory computer-readable medium for generative modeling of data on disjoint manifolds, the non-transitory computer-readable medium containing instructions for execution by one or more processors for:
Dependencies are inferred from the claim text.