公开(公告)号：US20190220605A1

公开(公告)日：2019-07-18

申请号：US16361397

申请日：2019-03-22

Applicant: Intel Corporation

Inventor： Michael Kounavis ,  Antonios Papadimitriou ,  Anindya Paul ,  Micah Sheller ,  Li Chen ,  Cory Cornelius ,  Brandon Edwards

IPC: G06F21/60 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06F21/60 ,  G06N3/0454 ,  G06N3/08

Abstract: In one example an apparatus comprises a memory and a processor to create, from a first deep neural network (DNN) model, a first plurality of DNN models, generate a first set of adversarial examples that are misclassified by the first plurality of deep neural network (DNN) models, determine a first set of activation path differentials between the first plurality of adversarial examples, generate, from the first set of activation path differentials, at least one composite adversarial example which incorporates at least one intersecting critical path that is shared between at least two adversarial examples in the first set of adversarial examples, and use the at least one composite adversarial example to generate a set of inputs for a subsequent training iteration of the DNN model. Other examples may be described.

公开(公告)号：US20190042937A1

公开(公告)日：2019-02-07

申请号：US15892138

申请日：2018-02-08

Applicant: Intel Corporation

Inventor： Micah Sheller ,  Cory Cornelius ,  Jason Martin ,  Yonghong Huang ,  Shih-Han Wang

IPC: G06N3/08 ,  G06F21/55

Abstract: Methods, apparatus, systems and articles of manufacture for federated training of a neural network using trusted edge devices are disclosed. An example system includes an aggregator device to aggregate model updates provided by one or more edge devices. The one or more edge devices to implement respective neural networks, and provide the model updates to the aggregator device. At least one of the edge devices to implement the neural network within a trusted execution environment.