Systems and methods for training generative models using summary statistics and other constraints
AssigneeUnlearn.AI, Inc.
InventorsAaron M. Smith, Anton D. Loukianov, Charles K. Fisher, Jonathan R. Walsh
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Abstract
Systems and methods for training and utilizing constrained generative models in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a constrained generative model. The method includes steps for receiving a set of data samples from a first distribution, identifying a set of constraints from a second distribution, and training a generative model based on the set of data samples and the set of constraints.
Claims20
A method for training a constrained generative model, the method comprising:
- receiving a set of data samples from a first distribution;
- identifying a set of constraints from a second distribution;
- and
- training a generative model based on the set of data samples and the set of constraints, wherein:
- the generative model is a neural network comprising hidden units;
- and
- training the generative model comprises:
- training the generative model on the set of data samples;
- updating the generative model to add a set of new hidden units and connections to the generative model;
- and
- training the generative model based on the set of constraints by modifying weights for the set of new hidden units.
A non-transitory machine-readable medium containing processor instructions for training a constrained generative model, where execution of the instructions by a processor causes the processor to perform a process that comprises:
- receiving a set of data samples from a first distribution;
- identifying a set of constraints from a second distribution;
- and
- training a generative model based on the set of data samples and the set of constraints, wherein:
- the generative model is a neural network comprising hidden units;
- and
- training the generative model comprises:
- training the generative model on the set of data samples;
- updating the generative model to add a set of new hidden units and connections to the generative model;
- and
- training the generative model based on the set of constraints by modifying weights for the set of new hidden units.
Dependencies are inferred from the claim text.