Fully automated machine learning system which generates and optimizes solutions given a dataset and a desired outcome
First Claim
1. An automated system for optimizing a model, the system comprising:
- one or more memory units for storing instructions; and
one or more processors configured to execute the instructions to perform operations comprising;
receiving a data input comprising a desired outcome and an input dataset identifier, the desired outcome comprising at least one of a data classification, a data regression, or a data synthesis;
retrieving an input dataset based on the input dataset identifier;
receiving an input model based on the desired outcome;
creating, by using a data synthesis model, a synthetic dataset based on the input dataset and a similarity metric;
debugging the input model using the synthetic dataset to create a debugged model;
selecting an actual dataset based on the input dataset and the desired outcome;
optimizing the debugged model using the actual dataset; and
storing the optimized model.
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Accused Products
Abstract
Automated systems and methods for optimizing a model are disclosed. For example, in an embodiment, a method for optimizing a model may comprise receiving a data input that includes a desired outcome and an input dataset identifier. The method may include retrieving an input dataset based on the identifier and receiving an input model based on the desired outcome. The method may also comprise using a data synthesis model to create a synthetic dataset based on the input dataset and a similarity metric. The method may also comprise debugging the input model using synthetic dataset to create a debugged model. The method may also comprise selecting an actual dataset based on the input dataset and the desired outcome. In some aspects, the method may comprise optimizing the debugged model using the actual dataset and storing the optimized model.
148 Citations
19 Claims
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1. An automated system for optimizing a model, the system comprising:
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one or more memory units for storing instructions; and one or more processors configured to execute the instructions to perform operations comprising; receiving a data input comprising a desired outcome and an input dataset identifier, the desired outcome comprising at least one of a data classification, a data regression, or a data synthesis; retrieving an input dataset based on the input dataset identifier; receiving an input model based on the desired outcome; creating, by using a data synthesis model, a synthetic dataset based on the input dataset and a similarity metric; debugging the input model using the synthetic dataset to create a debugged model; selecting an actual dataset based on the input dataset and the desired outcome; optimizing the debugged model using the actual dataset; and storing the optimized model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A method for automated model optimization, the method comprising:
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receiving data inputs comprising a desired outcome and an input dataset identifier, the desired outcome comprising at least one of a data classification, a data regression, or a data synthesis; retrieving an input dataset based on the input dataset identifier; receiving an input model based on the desired outcome; creating, by using a data synthesis model, a synthetic dataset based on the input dataset and a similarity metric; debugging the input model using the synthetic dataset to create a debugged model; selecting an actual dataset based on the input dataset and the desired outcome; optimizing the debugged model using the actual dataset; and storing the optimized model.
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19. A system for debugging a model, the system comprising:
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one or more memory units for storing instructions; and one or more processors configured to execute the instructions to perform operations comprising; receiving, via an interface, a model, a dataset, and a desired outcome comprising at least one of a data classification, a data regression, or a data synthesis; receiving, via the interface, a command to debug the model; adjusting, based on the desired outcome, the model to create an adjusted model, wherein adjusting the model comprises at least one of adjusting a hyperparameter, altering a model characteristic, editing a model parameter, or editing model code; training the adjusted model based on the command; receiving model output; and performing, based on the model output, one of terminating debugging, transmitting model output to the interface, or adjusting the adjusted model.
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Specification