公开(公告)号：US20210133911A1

公开(公告)日：2021-05-06

申请号：US16474540

申请日：2017-04-07

Applicant: INTEL CORPORATION

Inventor： Anbang YAO ,  Dongqi CAI ,  Libin WANG ,  Lin XU ,  Ping HU ,  Shandong WANG ,  Wehnua CHENG ,  Yiwen GUO ,  Liu YANG ,  Yuqing HOU ,  Zhou SU

IPC: G06T1/20 ,  G06N3/00 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00

Abstract: Described herein are advanced artificial intelligence agents for modeling physical interactions. An apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.

公开(公告)号：US20200242734A1

公开(公告)日：2020-07-30

申请号：US16474927

申请日：2017-04-07

Applicant: INTEL CORPORATION

Inventor： Shandong WANG ,  Yiwen GUO ,  Anbang YAO ,  Dongqi CAI ,  Libin WANG ,  Lin XU ,  Ping HU ,  Wenhua CHENG ,  Yurong CHEN

IPC: G06T3/40 ,  G06T1/20 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N20/10 ,  G06N20/20 ,  G06K9/62

Abstract: Methods and systems are disclosed using improved Convolutional Neural Networks (CNN) for image processing. In one example, an input image is down-sampled into smaller images with a smaller resolution than the input image. The down-sampled smaller images are processed by a CNN having a last layer with a reduced number of nodes than a last layer of a full CNN used to process the input image at a full resolution. A result is outputted based on the processed down-sampled smaller images by the CNN having a last layer with a reduced number of nodes. In another example, shallow CNN networks are built randomly. The randomly built shallow CNN networks are combined to imitate a trained deep neural network (DNN).

公开(公告)号：US20200027015A1

公开(公告)日：2020-01-23

申请号：US16474515

申请日：2017-04-07

Applicant: INTEL CORPORATION

Inventor： Angang YAO ,  Dongqi CAI ,  Libin WANG ,  Lin XU ,  Ping HU ,  Shandong WANG ,  Wenhua CHENG ,  Yiwen GUO ,  Liu YANG ,  Yurong CHEN ,  Yuqing HOU ,  Zhou SU

IPC: G06N7/00 ,  G06T1/20 ,  G06F9/48 ,  G06N20/00

Abstract: Described herein are systems and methods for providing deeply stacked automated program synthesis. In one embodiment, an apparatus to perform automated program synthesis includes a memory to store instructions for automated program synthesis and a compute cluster coupled to the memory. The compute cluster supports the instructions for performing the automated program synthesis including partitioning sketched data into partitions, training diverse sets of individual program synthesis units each having different capabilities with partitioned sketched data and for each partition applying respective transformations, and generating sketched baseline data for each individual program synthesis unit.

公开(公告)号：US20220230268A1

公开(公告)日：2022-07-21

申请号：US17517316

申请日：2021-11-02

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Dongqi CAI ,  Libin WANG ,  Lin XU ,  Ping HU ,  Shandong WANG ,  Wenhua CHENG ,  Yiwen GUO ,  Liu YANG ,  Yuqing HOU ,  Zhou SU

IPC: G06T1/20 ,  G06N20/00 ,  G06N3/00 ,  G06N3/04 ,  G06N3/08

Abstract: Described herein are advanced artificial intelligence agents for modeling physical interactions. In one embodiment, an apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.

公开(公告)号：US20200234411A1

公开(公告)日：2020-07-23

申请号：US16474848

申请日：2017-04-07

Applicant: INTEL CORPORATION

Inventor： Lin XU ,  Liu YANG ,  Anbang YAO ,  dongqi CAI ,  Libin WANG ,  Ping HU ,  Shaodong WANG ,  Wenhua CHENG ,  Yiwen GUO ,  Yurong CHEN

IPC: G06T5/00 ,  H04N9/64 ,  H04N9/07 ,  G06N3/08 ,  G06N5/04 ,  G06N20/10 ,  G06F9/50

Abstract: Methods and systems are disclosed using camera devices for deep channel and Convolutional Neural Network (CNN) images and formats. In one example, image values are captured by a color sensor array in an image capturing device or camera. The image values provide color channel data. The captured image values by the color sensor array are input to a CNN having at least one CNN layer. The CNN provides CNN channel data for each layer. The color channel data and CNN channel data is to form a deep channel image that stored in a memory. In another example, image values are captured by sensor array. The captured image values by the sensor array are input a CNN having a first CNN layer. An output is generated at the first CNN layer using the captured image values by the color sensor array. The output of the first CNN layer is stored as a feature map of the captured image.