Abstract: Techniques are disclosed relating to increasing the amount of training data available to machine learning algorithms. A computer system may access an initial set of training data that specifies a plurality of sequences, each of which may define a set of data values. The computer system may amplify the initial set of training data to create a revised set of training data. The amplifying may include identifying sub-sequences of data values in ones of the plurality of sequences in the initial set of training data and using an inheritance algorithm to create a set of additional sequences of data values, where each one of the set of additional sequences may include sub-sequences of data values from at least two different sequences in the initial set of training data. The computer system may process the set of additional sequences using the machine learning algorithm to train a machine learning model.

Abstract: Techniques are disclosed relating to increasing the amount of training data available to machine learning algorithms. A computer system may access an initial set of training data that specifies a plurality of sequences, each of which may define a set of data values. The computer system may amplify the initial set of training data to create a revised set of training data. The amplifying may include identifying sub-sequences of data values in ones of the plurality of sequences in the initial set of training data and using an inheritance algorithm to create a set of additional sequences of data values, where each one of the set of additional sequences may include sub-sequences of data values from at least two different sequences in the initial set of training data. The computer system may process the set of additional sequences using the machine learning algorithm to train a machine learning model.

Abstract: Techniques are disclosed relating to increasing the amount of training data available to machine learning algorithms. A computer system may access an initial set of training data that specifies a plurality of sequences, each of which may define a set of data values. The computer system may amplify the initial set of training data to create a revised set of training data. The amplifying may include identifying sub-sequences of data values in ones of the plurality of sequences in the initial set of training data and using an inheritance algorithm to create a set of additional sequences of data values, where each one of the set of additional sequences may include sub-sequences of data values from at least two different sequences in the initial set of training data. The computer system may process the set of additional sequences using the machine learning algorithm to train a machine learning model.

Abstract: Techniques are disclosed relating to increasing the amount of training data available to machine learning algorithms. A computer system may access an initial set of training data that specifies a plurality of sequences, each of which may define a set of data values. The computer system may amplify the initial set of training data to create a revised set of training data. The amplifying may include identifying sub-sequences of data values in ones of the plurality of sequences in the initial set of training data and using an inheritance algorithm to create a set of additional sequences of data values, where each one of the set of additional sequences may include sub-sequences of data values from at least two different sequences in the initial set of training data. The computer system may process the set of additional sequences using the machine learning algorithm to train a machine learning model.