公开(公告)号：US09508040B2

公开(公告)日：2016-11-29

申请号：US13915870

申请日：2013-06-12

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ahmad Bilal ,  Mehmet Iyigun ,  Milos Kralj ,  Christopher Kleynhans ,  Hari Pulapaka ,  Arun Kishan ,  Asela Gunawardana ,  Paul Koch ,  Christopher Meek ,  Eric Horvitz ,  Rich Caruana ,  Michael Fortin

IPC: G06F9/445 ,  G06F9/48 ,  G06N5/02

CPC classification number: G06N5/02 ,  G06F9/445 ,  G06F9/44578 ,  G06F9/485

Abstract: Systems and methods of pre-launching applications in a computer system, said applications being likely to be activated by a user from a terminated and/or suspended process state, are disclosed. The pre-launching of an application may be based on the assessed probability of the application being activated—as well as the level of availability of system resources to affect such pre-launching. Applications may be pre-launched based on these and other conditions/considerations, designed to improve the user's experience of a quick launch of applications in the background. Several prediction models are presented to provide a good estimate of the likelihood of an application being activated by a user. Such prediction models may comprise an adaptive predictor (based on past application usage situations) and/or a switch rate predictor (based on historic data of an application being switched and, possibly, having a decay rate applied to such switch rate measure).

Abstract translation: 公开了在计算机系统中预先启动应用程序的系统和方法，所述应用程序可能被用户从终止的和/或暂停的进程状态激活。 预先启动应用程序可能是基于评估的应用程序被激活的概率以及影响这种预发射的系统资源的可用性级别。 应用程序可能会基于这些和其他条件/注意事项预先启动，旨在提高用户在后台快速启动应用程序的体验。 呈现了几个预测模型，以提供用户激活应用程序的可能性的良好估计。 这样的预测模型可以包括自适应预测器（基于过去的应用使用情况）和/或开关速率预测器（基于被切换的应用的历史数据，并且可能具有应用于这种开关速率测量的衰减速率）。

公开(公告)号：US20170132528A1

公开(公告)日：2017-05-11

申请号：US15195894

申请日：2016-06-28

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ozlem Aslan ,  Rich Caruana ,  Matthew R. Richardson ,  Abdelrahman Mohamed ,  Matthai Philipose ,  Krzysztof Geras ,  Gregor Urban ,  Shengjie Wang

IPC: G06N99/00 ,  G06N7/00

CPC classification number: G06N20/00

Abstract: Multiple machine learning models can be jointly trained in parallel. An example process for jointly training multiple machine learning models includes providing a set of machine learning models that are to learn a respective task, the set of machine learning models including a first machine learning model and a second machine learning model. The process can initiate training of the first machine learning model to learn a task using training data. During the training of the first machine learning model, information can be passed between the first machine learning model and the second machine learning model. Such passing of information (or “transfer of knowledge”) between the machine learning models can be accomplished via the formulation, and optimization, of an objective function that comprises model parameters that are based on the multiple machine learning models in the set.