公开(公告)号：US20190042917A1

公开(公告)日：2019-02-07

申请号：US16014495

申请日：2018-06-21

Applicant: INTEL CORPORATION

Inventor： Yaniv Gurwicz ,  Raanan Yonatan Yehezkel Rohekar ,  Shami Nisimov ,  Guy Koren ,  Gal Novik

IPC: G06N3/04 ,  G06N7/00 ,  G06K9/62 ,  G06F17/30

Abstract: Various embodiments are generally directed to techniques for determining artificial neural network topologies, such as by utilizing probabilistic graphical models, for instance. Some embodiments are particularly related to determining neural network topologies by bootstrapping a graph, such as a probabilistic graphical model, into a multi-graphical model, or graphical model tree. Various embodiments may include logic to determine a collection of sample sets from a dataset. In various such embodiments, each sample set may be drawn randomly for the dataset with replacement between drawings. In some embodiments, logic may partition a graph into multiple subgraph sets based on each of the sample sets. In several embodiments, the multiple subgraph sets may be scored, such as with Bayesian statistics, and selected amongst as part of determining a topology for a neural network.

公开(公告)号：US11698930B2

公开(公告)日：2023-07-11

申请号：US16014495

申请日：2018-06-21

Applicant: INTEL CORPORATION

Inventor： Yaniv Gurwicz ,  Raanan Yonatan Yehezkel Rohekar ,  Shami Nisimov ,  Guy Koren ,  Gal Novik

IPC: G06F16/901 ,  G06N3/082 ,  G06F18/2137 ,  G06F18/21 ,  G06N3/045 ,  G06N5/01 ,  G06N7/01

CPC classification number: G06F16/9024 ,  G06F16/9027 ,  G06F18/2137 ,  G06F18/2163 ,  G06N3/045 ,  G06N3/082 ,  G06N5/01 ,  G06N7/01

Abstract: Various embodiments are generally directed to techniques for determining artificial neural network topologies, such as by utilizing probabilistic graphical models, for instance. Some embodiments are particularly related to determining neural network topologies by bootstrapping a graph, such as a probabilistic graphical model, into a multi-graphical model, or graphical model tree. Various embodiments may include logic to determine a collection of sample sets from a dataset. In various such embodiments, each sample set may be drawn randomly for the dataset with replacement between drawings. In some embodiments, logic may partition a graph into multiple subgraph sets based on each of the sample sets. In several embodiments, the multiple subgraph sets may be scored, such as with Bayesian statistics, and selected amongst as part of determining a topology for a neural network.