Systems and methods for applying semi-discrete calculus to meta machine learning

The method for implementing a meta-machine learning (meta-ML) optimization engine for a neural network (NN) or a machine learning (ML) connective model, the method comprising using a computer processor:

• generating training data and data by preparing raw training data and raw data;
• generating a modified backpropagation algorithm by replacing algorithmic traits of gradient descent in a backpropagation algorithm that uses gradient descent with the meta-ML optimization engine;
• training the NN or ML connective model using training data and the modified backpropagation algorithm;
• and
• applying the trained NN or ML connective model to data.

A system for building a meta-machine learning (meta-ML) optimization engine for a neural network (NN) or a machine learning (ML) connective model, the system comprising:

• a memory;
• and
• one or more processors configured to:
• compute local minima of a loss function for a data set;
• store a detachment of the loss function at a selected position of a parameter of the loss function;
• iteratively simulate a backpropagation algorithm by executing a sequence of optimization steps, wherein at each iteration an optimization step is configured to determine a position of a parameter of the loss function that is closer than the selected position of the parameter of the loss function or a position of the parameter of the loss function determined in a previous iteration to a local minimum of the loss function;
• compute at each iteration a detachment of the loss function and learning rate at the position of a determined parameter of the loss function;
• compute at each iteration one or more optimal learning rates, wherein an optimal learning rate comprises an absolute value of a difference between the position of a determined parameter of the loss function and the position of a local minimum of the loss function;
• store after each iteration the computed detachment, learning rate, and optimal learning rates;
• and
• train a ML connective model to model the optimal learning rates of the backpropagation algorithm.

A system for implementing a meta-machine learning (meta-ML) optimization engine for a neural network (NN) or a machine learning (ML) connective model, the system comprising:

• a memory;
• and
• one or more processors configured to:
• generate training data and data by preparing raw training data and raw data;
• generate a modified backpropagation algorithm by replacing algorithmic traits of gradient descent in a backpropagation algorithm that uses gradient descent with the meta-ML optimization engine;
• train the NN or ML connective model using the training data and the modified backpropagation algorithm;
• and
• apply the trained NN or ML connective model to the data.