公开(公告)号：US20160267395A1

公开(公告)日：2016-09-15

申请号：US14856481

申请日：2015-09-16

Applicant: QUALCOMM Incorporated

Inventor： David Jonathan JULIAN ,  Anthony SARAH

IPC: G06N99/00 ,  G06N5/04

CPC classification number: G06N99/005 ,  G06K9/00107 ,  G06K9/6262 ,  G06K9/6269 ,  G06N3/02 ,  G06N5/046

Abstract: A method of online training of a classifier includes determining a distance from one or more feature vectors of an object to a first predetermined decision boundary established during off-line training for the classifier. The method also includes updating a decision rule as a function of the distance. The method further includes classifying a future example based on the updated decision rule.

Abstract translation: 分类器的在线训练的方法包括确定从物体的一个或多个特征向量到在分类器的离线训练期间建立的第一预定判定边界的距离。 该方法还包括根据距离更新决策规则。 该方法还包括基于更新的决策规则对未来示例进行分类。

公开(公告)号：US20150324688A1

公开(公告)日：2015-11-12

申请号：US14483054

申请日：2014-09-10

Applicant: QUALCOMM Incorporated

Inventor： Casimir Matthew WIERZYNSKI ,  David Jonathan JULIAN ,  Anthony SARAH

IPC: G06N3/08

CPC classification number: G06N3/049 ,  G06N3/0454 ,  G06N3/08

Abstract: A method of generating a classifier model includes distributing a common feature model to two or more users. Multiple classifiers are trained on top of the common feature model. The method further includes distributing a first classifier of the multiple classifiers to a first user and a second classifier of the multiple classifiers to a second user.

Abstract translation: 生成分类器模型的方法包括将公共特征模型分发给两个或更多个用户。 在通用特征模型之上训练多个分类器。 该方法还包括将多个分类器的第一分类器分配给第一用户，并将多个分类器的第二分类器分配给第二用户。

公开(公告)号：US20150324686A1

公开(公告)日：2015-11-12

申请号：US14464558

申请日：2014-08-20

Applicant: QUALCOMM Incorporated

Inventor： David Jonathan JULIAN ,  Anthony SARAH ,  Casimir Matthew WIERZYNSKI

IPC: G06N3/08

CPC classification number: G06N20/00 ,  G06N3/08

Abstract: A method of learning a model includes receiving model updates from one or more users. The method also includes computing an updated model based on a previous model and the model updates. The method further includes transmitting data related to a subset of the updated model to the a user(s) based on the updated model.

Abstract translation: 学习模型的方法包括从一个或多个用户接收模型更新。 该方法还包括基于先前的模型和模型更新来计算更新的模型。 所述方法还包括基于所更新的模型向所述用户发送与所述更新的模型的子集有关的数据。

公开(公告)号：US20150278680A1

公开(公告)日：2015-10-01

申请号：US14659516

申请日：2015-03-16

Applicant: QUALCOMM Incorporated

Inventor： Venkata Sreekanta Reddy ANNAPUREDDY ,  David Jonathan JULIAN ,  Anthony SARAH

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/0454 ,  G06N3/049 ,  G06N3/088

Abstract: A method of distributed computation includes computing a first set of results in a first computational chain with a first population of processing nodes and passing the first set of results to a second population of processing nodes. The method also includes entering a first rest state with the first population of processing nodes after passing the first set of results and computing a second set of results in the first computational chain with the second population of processing nodes based on the first set of results. The method further includes passing the second set of results to the first population of processing nodes, entering a second rest state with the second population of processing nodes after passing the second set of results and orchestrating the first computational chain.

Abstract translation: 一种分布式计算的方法包括：在具有第一种处理节点的第一计算链中计算第一组结果，并将第一组结果传递给第二种处理节点。 该方法还包括在传递第一组结果之后，与处理节点的第一群体一起输入第一休息状态，并且基于第一组结果与第二种处理节点计算第一计算链中的第二组结果。 该方法还包括将第二组结果传递给第一处理节点群，在通过第二组结果并编排第一计算链之后，与第二处理节点群进入第二休息状态。

公开(公告)号：US20150248607A1

公开(公告)日：2015-09-03

申请号：US14267394

申请日：2014-05-01

Applicant: QUALCOMM Incorporated

Inventor： Anthony SARAH ,  Jeffrey Alexander LEVIN ,  Jeffrey Baginsky GEHLHAAR

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/04 ,  G06N3/0454 ,  G06N3/08 ,  G06N3/10

Abstract: Certain aspects of the present disclosure support efficient implementation of common neuron models. In an aspect, a first memory layout can be allocated for parameters and state variables of instances of a first neuron model, and a second memory layout different from the first memory layout can be allocated for parameters and state variables of instances of a second neuron model having a different complexity than the first neuron model.

Abstract translation: 本公开的某些方面支持有效实施普通神经元模型。 在一方面，可以为第一神经元模型的实例的参数和状态变量分配第一存储器布局，并且可以为不同于第一存储器布局的第二存储器布局分配用于第二神经元模型的实例的参数和状态变量 具有与第一神经元模型不同的复杂性。

公开(公告)号：US20150052093A1

公开(公告)日：2015-02-19

申请号：US14079181

申请日：2013-11-13

Applicant: QUALCOMM Incorporated

Inventor： Michael-David Nakayoshi CANOY ,  Yinyin LIU ,  Anthony SARAH ,  Adrienne MILNER

IPC: G06N3/08

CPC classification number: G06N3/08 ,  G06N3/049

Abstract: Methods and apparatus are provided for training a neural device having an artificial nervous system by modulating at least one training parameter during the training. One example method for training a neural device having an artificial nervous system generally includes observing the neural device in a training environment and modulating at least one training parameter based at least in part on the observing. For example, the training apparatus described herein may modify the neural device's internal learning mechanisms (e.g., spike rate, learning rate, neuromodulators, sensor sensitivity, etc.) and/or the training environment's stimuli (e.g., move a flame closer to the device, make the scene darker, etc.). In this manner, the speed with which the neural device is trained (i.e., the training rate) may be significantly increased compared to conventional neural device training systems.

Abstract translation: 提供了用于通过在训练期间调制至少一个训练参数来训练具有人造神经系统的神经装置的方法和装置。 用于训练具有人造神经系统的神经装置的一个示例性方法通常包括在训练环境中观察神经装置并且至少部分地基于观察来调制至少一个训练参数。 例如，本文描述的训练装置可以修改神经装置的内部学习机制（例如，尖峰率，学习速率，神经调节器，传感器灵敏度等）和/或训练环境的刺激（例如，将火焰移动到设备附近 ，使场景更暗等）。 以这种方式，与传统的神经元装置训练系统相比，神经装置训练的速度（即，训练速率）可以显着增加。

公开(公告)号：US20140310217A1

公开(公告)日：2014-10-16

申请号：US14047885

申请日：2013-10-07

Applicant: QUALCOMM Incorporated

Inventor： Anthony SARAH ,  Jeffrey Alexander LEVIN

IPC: G06N3/02

CPC classification number: G06N3/063 ,  G06N3/049

Abstract: A method for dynamically setting a neuron value processes a data structure including a set of parameters for a neuron model and determines a number of segments defined in the set of parameters. The method also includes determining a number of neuron types defined in the set of parameters and determining at least one boundary for a first segment.

Abstract translation: 用于动态设置神经元值的方法处理包括用于神经元模型的一组参数的数据结构并且确定在该组参数中定义的段的数量。 该方法还包括确定在该组参数中定义的神经元类型的数量，并确定第一段的至少一个边界。