REMOTE USAGE OF MACHINE LEARNED LAYERS BY A SECOND MACHINE LEARNING CONSTRUCT
Techniques are disclosed for remote usage of machine learned layers by a second machine learning construct. Layers determined within a first machine learning construct are sent to the second construct. A first data group is obtained in a first locality. The first data group is applied to a first localized machine learning construct. A first set of convolutional layers is determined within the first localized machine learning construct based on the first data group, where the first set of convolutional layers comprises a first data flow graph machine. Similarity is adjudicated between the first localized machine learning construct and a second localized machine learning construct. The first set of convolutional layers is sent to the second localized machine learning construct, based on the similarity that was adjudicated meeting a threshold. A second data group is analyzed by the second localized machine learning construct using the first set of convolutional layers.
1 . A computer-implemented method for data analysis comprising:
obtaining a first data group in a first locality;
applying the first data group to a first localized machine learning construct;
determining a first set of convolutional layers within the first localized machine learning construct based on the first data group wherein the first set of convolutional layers comprises a first data flow graph machine;
adjudicating similarity between the first localized machine learning construct and a second localized machine learning construct;
sending the first set of convolutional layers to the second localized machine learning construct, based on the similarity that was adjudicated meeting a threshold; and
analyzing a second data group by the second localized machine learning construct using the first set of convolutional layers.
2 . The method of claim 1 wherein the similarity is adjudicated based on machine learning construct context for the first localized machine learning construct and the second localized machine learning construct.
3 . The method of claim 1 wherein the threshold is updated based on the analyzing a second group of data by the second localized machine learning construct.
4 . The method of claim 1 wherein the first localized machine learning construct comprises a first retail establishment.
5 . The method of claim 4 wherein the second localized machine learning construct comprises a second retail establishment.
6 . The method of claim 1 wherein the analyzing comprises determining a sales recommendation for a retail establishment associated with the second localized machine learning construct.
7 - 8 . (canceled)
9 . The method of claim 1 wherein the first localized machine learning construct comprises a first vehicle.
10 . The method of claim 9 wherein the second localized machine learning construct comprises a second vehicle.
11 . The method of claim 10 further comprising transferring descriptors for the first set of convolutional layers using a mesh network comprising the first vehicle and the second vehicle.
12 . The method of claim 1 wherein the second localized machine learning construct comprises a second data flow graph machine.
13 . The method of claim 12 further comprising augmenting learning from the first localized machine learning construct by the second localized machine learning construct.
14 . The method of claim 13 wherein the augmenting learning is accomplished using a second group of data obtained within the second localized machine learning construct.
15 . The method of claim 13 further comprising sending results of the augmenting learning to a third machine learning construct.
16 . The method of claim 15 further comprising analyzing a third data group by the third machine learning construct using the results of the augmenting learning.
17 . The method of claim 12 wherein the first localized machine learning construct comprises a convolutional neural net.
18 . The method of claim 1 wherein the determining the first set of convolutional layers comprises machine learning.
19 . The method of claim 1 wherein the determining further comprises determining a first set of max pooling layers.
20 . The method of claim 1 wherein the determining further comprises determining a first set of hidden layers.
21 . The method of claim 1 wherein the determining further comprises determining a first set of weights.
22 . The method of claim 21 wherein the determining the first set of weights is accomplished using forward propagation and backward propagation.
23 - 24 . (canceled)
25 . The method of claim 1 further comprising applying a fourth data group to the second localized machine learning construct.
26 . The method of claim 25 further comprising determining a second set of convolutional layers on the second localized machine learning construct using the fourth data group.
27 . A computer program product embodied in a non-transitory computer readable medium for data analysis, the computer program product comprising code which causes one or more processors to perform operations of:
obtaining a first data group in a first locality;
applying the first data group to a first localized machine learning construct;
determining a first set of convolutional layers within the first localized machine learning construct based on the first data group wherein the first set of convolutional layers comprises a first data flow graph machine;
adjudicating similarity between the first localized machine learning construct and a second localized machine learning construct;
sending the first set of convolutional layers to the second localized machine learning construct, based on the similarity that was adjudicated meeting a threshold; and
analyzing a second data group by the second localized machine learning construct using the first set of convolutional layers.
28 . A computer system for data analysis comprising:
a memory which stores instructions;
one or more processors attached to the memory wherein the one or more processors, when executing the instructions which are stored, are configured to:
obtain a first data group in a first locality;
apply the first data group to a first localized machine learning construct;
determine a first set of convolutional layers within the first localized machine learning construct based on the first data group wherein the first set of convolutional layers comprises a first data flow graph machine;
adjudicate similarity between the first localized machine learning construct and a second localized machine learning construct;
send the first set of convolutional layers to the second localized machine learning construct, based on the similarity that was adjudicated meeting a threshold; and
analyze a second data group by the second localized machine learning construct using the first set of convolutional layers.