SYSTEMS AND METHODS FOR TRAINING AND USING MACHINE LEARNING MODELS AND ALGORITHMS
Systems and methods for training a machine learning model. The methods comprise, by a computing device: obtaining a training data set comprising a collection of training examples, each training example comprising data point(s); selecting a first subset of training examples from the collection of training examples based on at least one of a derivative vector of a loss function for each training examples in the collection of training examples and an importance of each training example relative to other training examples of the collection of training examples; and training the machine learning model using the first subset of training examples. A total number of training examples in the first subset of training examples is unequal to a total number of training examples in the collection of training examples.
1 . A method for training a machine learning model, comprising:
obtaining, by a computing device, a training data set comprising a collection of training examples, each training example comprising at least one data point;
selecting, by the computing device, a first subset of training examples from the collection of training examples based on at least one of a derivative vector of a loss function for each training example of the collection of training examples and an importance of each training examples relative to other training examples in the collection of training examples; and
training, by the computing device, the machine learning model using the first subset of training examples;
wherein a total number of training examples in the first subset is unequal to a total number of training examples in the training data set.
2 . The method according to claim 1 , further comprising using the machine learning model which has been trained to control operations of a mobile platform.
3 . The method according to claim 1 , wherein the at least one data point is obtained from an image generated by a camera.
4 . The method according to claim 1 , wherein the first subset of training examples is selected based on a norm of the derivative vector.
5 . The method according to claim 1 , wherein each said training example further comprises a true value for a property to be predicted by the machine learning model.
6 . The method according to claim 1 , further comprising selecting, by the computing device, a second subset of training examples based on at least one of a derivative vector of the loss function for each training example of the collection of training examples and an importance of each training examples relative to the other training examples in the collection of training examples.
7 . The method according to claim 6 , wherein the first subset of training examples is used in a first epoch in said training and the second subset of training examples is used in a second epoch of said training.
8 . The method according to claim 6 , wherein a total number of training examples in the second subset is different than the total number of training examples in the first subset.
9 . The method according to claim 1 , further comprising ranking the plurality of training examples in accordance with norms of the derivative vectors of the loss function.
10 . A system, comprising:
a processor;
a non-transitory computer-readable storage medium comprising programming instructions that are configured to cause the processor to implement a method for training a machine learning model, wherein the programming instructions comprise instructions to:
obtain a training data set comprising a collection of training examples, each training example comprising at least one data point;
select a first subset of training examples from the collection of training examples based on at least one of a derivative vector of a loss function for each training examples in the collection of training examples and an importance of each training examples of the collection of training examples relative to other training examples of the collection of training examples; and
train the machine learning model using the first subset of training examples;
wherein a total number of training examples in the first subset is unequal to a total number of training examples in the collection of training examples.
11 . The system according to claim 10 , wherein the programming instructions further comprise instructions to cause the machine learning model which has been trained to control operations of a mobile platform.
12 . The system according to claim 10 , wherein the at least one data point is obtained from an image generated by a camera.
13 . The system according to claim 10 , wherein the first subset of training examples is selected based on a norm of the derivative vector.
14 . The system according to claim 10 , wherein each said training example further comprises a true value for a property to be predicted by the machine learning model.
15 . The system according to claim 10 , wherein the programming instructions further comprise instructions to select a second subset of training examples based on at least one of a derivative vector of the loss function for each training example of the collection of training examples and an importance of each training examples relative to the other training examples of the collection of training examples.
16 . The system according to claim 15 , wherein the first subset of training examples is used in a first epoch in said training and the second subset of training examples is used in a second epoch of said training.
17 . The system according to claim 15 , wherein a total number of training examples in the second subset is different than the total number of training examples in the first subset.
18 . The system according to claim 10 , wherein the programming instructions further comprise instructions to rank the plurality of training examples in accordance with norms of the derivative vectors of the loss function.
19 . A computer program product comprising a memory and programming instructions that are configured to cause a processor to:
obtain a training data set comprising a collection of training examples, each training example comprising at least one data point;
select a first subset of training examples from the collection of training examples based on at least one of a derivative vector of a loss function for each training example in the collection of training examples and an importance of each training examples relative to other training examples of the collection of training examples; and
train the machine learning model using the first subset of training examples;
wherein a total number of training examples in the first subset is unequal to a total number of training examples in the collection of training examples.
20 . The computer program product according to claim 19 , wherein the programming instruction are further configured to cause the processor to use or instruct an external device to use the machine learning model which has been trained to control operations of a mobile platform.