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Machine-Learning Models Based on Non-local Neural Networks

机译:基于非局部神经网络的机器学习模型

摘要

In one embodiment, a method includes training a baseline machine-learning model based on a neural network comprising a plurality of stages, wherein each stage comprises a plurality of neural blocks, accessing a plurality of training samples comprising a plurality of content objects, respectively, determining one or more non-local operations, wherein each non-local operation is based on one or more pairwise functions and one or more unary functions, generating one or more non-local blocks based on the plurality of training samples and the one or more non-local operations, determining a stage from the plurality of stages of the neural network, and training a non-local machine-learning model by inserting each of the one or more non-local blocks in between at least two of the plurality of neural blocks in the determined stage of the neural network.
机译:在一个实施例中,一种方法包括基于包括多个阶段的神经网络训练基线机器学习模型,其中每个阶段包括多个神经块,分别访问包括多个内容对象的多个训练样本,确定一个或多个非本地操作,其中每个非本地操作基于一个或多个成对函数和一个或多个一元函数,并基于多个训练样本和一个或多个生成一个或多个非本地块非本地操作,从神经网络的多个阶段中确定一个阶段,以及通过在多个神经网络中的至少两个之间插入一个或多个非本地块中的每一个来训练非本地机器学习模型在神经网络的确定阶段阻止。

著录项

  • 公开/公告号US2019156210A1

    专利类型

  • 公开/公告日2019-05-23

    原文格式PDF

  • 申请/专利权人 FACEBOOK INC.;

    申请/专利号US201816192649

  • 发明设计人 KAIMING HE;ROSS GIRSHICK;XIAOLONG WANG;

    申请日2018-11-15

  • 分类号G06N3/08;G06N3/04;G06F17/15;G06F17/30;

  • 国家 US

  • 入库时间 2022-08-21 12:08:35

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