公开(公告)号：US20250068891A1

公开(公告)日：2025-02-27

申请号：US18724510

申请日：2022-02-18

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Chao LI ,  Yurong CHEN ,  Wenjian SHAO

IPC: G06N3/0464

Abstract: Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement dynamic triplet convolution for convolutional neural networks are disclosed. An example apparatus disclosed herein for a convolutional neural network is to calculate one or more scalar kernels based on an input feature map applied to a layer of the convolutional neural network, ones of the one or more scalar kernels corresponding to respective dimensions of a static multidimensional convolutional filter associated with the layer of the convolutional neural network. The disclosed example apparatus is also to scale elements of the static multidimensional convolutional filter along a first one of the dimensions based on a first one of the one or more scalar kernels corresponding to the first one of the dimensions to determine a dynamic multidimensional convolutional filter associated with the layer of the convolutional neural network.

公开(公告)号：US20240005628A1

公开(公告)日：2024-01-04

申请号：US18031064

申请日：2020-11-19

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Yikai WANG ,  Ming LU ,  Yurong CHEN

IPC: G06V10/44 ,  G06V10/80 ,  G06V10/82

CPC classification number: G06V10/454 ,  G06V10/82 ,  G06V10/811 ,  G06V10/806

Abstract: Techniques related to bidirectional compact deep fusion networks for multimodal image inputs are discussed. Such techniques include applying a shared convolutional layer and independent batch normalization layers to input volumes for each modality and fusing features from the resultant output volumes in both directions across the modalities.

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：US20240312196A1

公开(公告)日：2024-09-19

申请号：US18565967

申请日：2021-11-30

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Yurong CHEN ,  Chao LI

IPC: G06V10/82 ,  G06N3/0464 ,  G06V20/40

CPC classification number: G06V10/82 ,  G06N3/0464 ,  G06V20/42

Abstract: An apparatus, method, device and medium for dynamic quadruple convolution in a 3-dimensional (3D) convolutional neural network (CNN) are provided. The method includes: a multi-dimensional attention block configured to: receive an input feature map of a video data sample; and dynamically generate convolutional kernel scalars along four dimensions of a 3-dimensional convolution kernel space based on the input feature map, the four dimensions comprising an output channel number, an input channel number, a temporal size and a spatial size; and a convolution block configured to sequentially multiply the generated convolutional kernel scalars with a static 3D convolution kernel in a matrix-vector product way to obtain a dynamic kernel of dynamic quadruple convolution.

公开(公告)号：US20240013047A1

公开(公告)日：2024-01-11

申请号：US18252231

申请日：2020-12-24

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Yurong CHEN ,  Xiaolong LIU

IPC: G06N3/08

CPC classification number: G06N3/08 ,  G06V10/7715

Abstract: Dynamic conditional pooling for neural network processing is disclosed. An example of a storage medium includes instructions for receiving an input at a convolutional layer of a convolutional neural network (CNN); receiving an input sample at a pooling stage of the convolutional layer; generating a plurality of soft weights based on the input sample; performing conditional aggregation on the input sample utilizing the plurality of soft weights to generate an aggregated value; and performing conditional normalization on the aggregated value to generate an output for the convolutional layer.

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.