Patent ID: 11941523
Assignee: CAPITAL ONE SERVICES, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A system comprising:
a database configured to store a training set that comprises a plurality of examples, wherein each example, of the plurality of examples, comprises an input, a ground truth output, and is associated with a conditional probability of the ground truth output given the input;
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
initialize a deep neural network model to include an input layer, an output layer, a plurality of hidden layers, and a plurality of model parameters;
train, based on the training set, the deep neural network model by
performing a plurality of first iterations that modifies the plurality of model parameters based on a first loss function wherein the first loss function comprises a negative log-likelihood of the plurality of model parameters, wherein each respective first iteration of the plurality of first iterations is associated with a respective first mini-batch from the plurality of examples, and wherein each respective first iteration of the plurality of second iterations is performed based on minimizing an average gradient of the first loss function for each example in the respective first mini-batch,
after performing the plurality of first iterations, performing a plurality of second iterations that adjusts the plurality of model parameters based on a second loss function that applies different weighting to examples based on whether the deep neural network classifies an example correctly or incorrectly wherein the second loss function comprises a weighted negative log-likelihood of the plurality of model parameters, wherein each respective second iteration of the plurality of second iterations is associated with a respective second mini-batch from the plurality of examples, wherein each respective second iteration of the plurality of second iterations is performed based on minimizing an average gradient of the second loss function for each example in the respective second mini-batch and by giving zero weight to any example in the respective second mini-batch that is correctly classified by the deep neural network model, and
wherein, for at least one second iteration of the plurality of second iterations, at least one example of the second mini-batch for the at least one second iteration is correctly classified by the deep neural network model, the at least one example is given zero weight based on being correctly classified, and the average gradient of the second loss function is determined based on a size of the mini-batch that includes the at least one example, and

determining that the deep neural network model, after performing the plurality of first iterations and the plurality of second iterations, satisfies one or more stopping criteria; and

after the deep neural network model has been trained, generate, based on an input data set, one or more predictions using the deep neural network model.