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This invention aims to allow mobile applications based on Machine Learning to run in

different devices with different configurations, by optimizing the tradeoff between accuracy,

response time and storage use specifically to each device. It consists in allowing the developer to

train multiple models to solve the same task. A subset of trained models, with acceptable

accuracy, will then be made available for the application. Whenever the application is installed

on the device, it will pre-load each of the available models, initialize it with random parameters,

and run some inference steps on the device, in order to measure the computation time. With this

information, together with the full size and accuracy of the model , the application can select the

most accurate model with acceptable response time (according to the application), or even allow

the user to select a model.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.