公开(公告)号：US20240005649A1

公开(公告)日：2024-01-04

申请号：US18017050

申请日：2020-09-07

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Xiao Zhou ,  Guangli Zhang ,  Yu Zhang ,  Dian Gu

IPC: G06V10/82 ,  G06V10/77 ,  G06V10/44

CPC classification number: G06V10/82 ,  G06V10/7715 ,  G06V10/449

Abstract: Techniques related to poly-scale kernel-wise convolutional neural network layers are discussed. A poly-scale kernel-wise convolutional neural network layer is applied to an input volume to generate an output volume and include filters each having a number of filter kernels with the same sample rate and differing dilation rates optionally in a repeating pattern of dilation rate groups within each of filters with the pattern of dilation rate groups offset between the filters the poly-scale kernel-wise convolutional neural network layer.

公开(公告)号：US20220164669A1

公开(公告)日：2022-05-26

申请号：US17442111

申请日：2019-06-05

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Aojun Zhou ,  Dawei Sun ,  Dian Gu ,  Yurong Chen

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063

Abstract: Systems, methods, apparatuses, and computer program products to receive a plurality of binary weight values for a binary neural network sampled from a policy neural network comprising a posterior distribution conditioned on a theta value. An error of a forward propagation of the binary neural network may be determined based on a training data and the received plurality of binary weight values. A respective gradient value may be computed for the plurality of binary weight values based on a backward propagation of the binary neural network. The theta value for the posterior distribution may be updated using reward values computed based on the gradient values, the plurality of binary weight values, and a scaling factor.