SYSTEMS AND METHODS FOR AN ACCELERATED AND ENHANCED TUNING OF A MODEL BASED ON PRIOR MODEL TUNING DATA
Disclosed examples including generating a joint model based on first and second subject models, the first and second subject models selected based on a relationship between the first and second subject models; selecting the joint model from a plurality of joint models after a determination that entropy data points of the joint model satisfy a threshold, the entropy data points based on multiple tuning trials of the joint model; and providing tuning data associated with the joint model to a tuning session of a target model.
1 . An apparatus comprising:
interface circuitry;
instructions, and
programmable circuitry to be programmed by the instructions to:
generate a joint model based on first and second subject models, the first and second subject models selected based on a relationship between the first and second subject models;
select the joint model from a plurality of joint models after a determination that entropy data points of the joint model satisfy a threshold, the entropy data points based on multiple tuning trials of the joint model, and
provide tuning data associated with the joint model to a tuning session of a target model.
2 . The apparatus of claim 1 , wherein the programmable circuitry is to:
access the first and second subject models in a pool of historical subject models; and
select the first and second subject models as a pair of subject models to generate the joint model after a relatedness metric value corresponding to a relatedness between the first and second subject models satisfies a relatedness threshold.
3 . The apparatus of claim 2 , wherein the programmable circuitry is to:
determine a second relatedness metric value for a third subject model and a fourth subject model from the pool of historical subject models; and
after a determination that the second relatedness metric value does not satisfy the relatedness threshold, not select the third and fourth subject models as a pair of subject models to generate the joint model.
4 . The apparatus of claim 1 , wherein the programmable circuitry is to:
compare a behavior of the first subject model to a behavior of the second subject model; and
select the first and second subject models based on the comparison.
5 . The apparatus of claim 1 , wherein the tuning data associated with the joint model is historical tuning data associated with the first and second subject models.
6 . The apparatus of claim 1 , wherein the programmable circuitry is to:
generate predictions based on the multiple tuning trials of the joint model; and
generate the entropy data points based on at least some of the predictions.
7 . The apparatus of claim 1 , wherein the entropy data points of the joint model are maximum entropy data points.
8 . A storage device or storage disk comprising computer-readable instructions to cause programmable circuitry to at least:
generate a joint model based on first and second subject models, the first and second subject models selected based on a relationship between the first and second subject models;
select the joint model from a plurality of joint models after a determination that entropy data points of the joint model satisfy a threshold, the entropy data points based on multiple tuning trials of the joint model; and
provide tuning data associated with the joint model to a tuning session of a target model.
9 . The storage device or storage disk of claim 8 , wherein the computer-readable instructions are to cause the programmable circuitry to:
access the first and second subject models in a pool of historical subject models; and
select the first and second subject models as a pair of subject models to generate the joint model after a relatedness metric value corresponding to a relatedness between the first and second subject models satisfies a relatedness threshold.
10 . The storage device or storage disk of claim 9 , wherein the computer-readable instructions are to cause the programmable circuitry to
determine a second relatedness metric value for a third subject model and a fourth subject model from the pool of historical subject models; and
after a determination that the second relatedness metric value does not satisfy the relatedness threshold, not select the third and fourth subject models as a pair of subject models to generate the joint model.
11 . The storage device or storage disk of claim 8 , wherein the computer-readable instructions are to cause the programmable circuitry to:
compare a behavior of the first subject model to a behavior of the second subject model; and
select the first and second subject models based on the comparison.
12 . The storage device or storage disk of claim 8 , wherein the tuning data associated with the joint model is historical tuning data associated with the first and second subject models.
13 . The storage device or storage disk of claim 8 , wherein the computer-readable instructions are to cause the programmable circuitry to:
generate predictions based on the multiple tuning trials of the joint model; and
generate the entropy data points based on at least some of the predictions.
14 . The storage device or storage disk of claim 8 , wherein the entropy data points of the joint model are maximum entropy data points.
15 . A method comprising:
generating a joint model based on first and second subject models, the first and second subject models selected based on a relationship between the first and second subject models,
selecting, by executing an instruction with programmable circuitry, the joint model from a plurality of joint models after a determination that entropy data points of the joint model satisfy a threshold, the entropy data points based on multiple tuning trials of the joint model, and
providing tuning data associated with the joint model to a tuning session of a target model.
16 . The method of claim 15 , including:
accessing the first and second subject models in a pool of historical subject models; and
selecting the first and second subject models as a pair of subject models to generate the joint model after a relatedness metric value corresponding to a relatedness between the first and second subject models satisfies a relatedness threshold.
17 . The method of claim 16 , including:
determining a second relatedness metric value for a third subject model and a fourth subject model from the pool of historical subject models, and
after a determination that the second relatedness metric value does not satisfy the relatedness threshold, not selecting the third and fourth subject models as a pair of subject models to generate the joint model.
18 . The method of claim 15 , including:
comparing a behavior of the first subject model to a behavior of the second subject model, and
selecting the first and second subject models based on the comparison.
19 . The method of claim 15 , wherein the tuning data associated with the joint model is historical tuning data associated with the first and second subject models.
20 . The method of claim 15 , including:
generating predictions based on the multiple tuning trials of the joint model; and
generating the entropy data points based on at least some of the predictions.