公开(公告)号：US11531888B2

公开(公告)日：2022-12-20

申请号：US16757186

申请日：2018-10-15

Applicant: Robert Bosch GmbH

Inventor： Jan Achterhold ,  Jan Mathias Koehler ,  Tim Genewein

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for creating a deep neural network. The deep neural network includes a plurality of layers and connections having weights, and the weights in the created deep neural network are able to assume only predefinable discrete values from a predefinable list of discrete values. The method includes: providing at least one training input variable for the deep neural network; ascertaining a variable characterizing a cost function, which includes a first variable, which characterizes a deviation of an output variable of the deep neural network ascertained as a function of the provided training input variable relative to a predefinable setpoint output variable, and the variable characterizing the cost function further including at least one penalization variable, which characterizes a deviation of a value of one of the weights from at least one of at least two of the predefinable discrete values; training the deep neural network.

公开(公告)号：US11150657B2

公开(公告)日：2021-10-19

申请号：US16420440

申请日：2019-05-23

Applicant: Robert Bosch GmbH

Inventor： Marcello Carioni ,  Giorgio Patrini ,  Max Welling ,  Patrick Forré ,  Tim Genewein

IPC: G05D1/00 ,  G06N20/00 ,  B60W10/04 ,  B60W10/18 ,  B60W10/20 ,  B60W50/00 ,  B60W50/14

Abstract: A lossy data compressor for physical measurement data, comprising a parametrized mapping network hat, when applied to a measurement data point x in a space X, produces a point z in a lower-dimensional manifold Z, and configured to provide a point z on manifold Z as output in response to receiving a data point x as input, wherein the manifold Z is a continuous hypersurface that only admits fully continuous paths between any two points on the hypersurface; and the parameters θ of the mapping network are trainable or trained towards an objective that comprises minimizing, on the manifold Z, a distance between a given prior distribution PZ and a distribution PQ induced on manifold Z by mapping a given set PD of physical measurement data from X onto Z using the mapping network, according to a given distance measure.

公开(公告)号：US20200342315A1

公开(公告)日：2020-10-29

申请号：US16757186

申请日：2018-10-15

Applicant: Robert Bosch GmbH

Inventor： Jan Achterhold ,  Jan Mathias Koehler ,  Tim Genewein

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for creating a deep neural network. The deep neural network includes a plurality of layers and connections having weights, and the weights in the created deep neural network are able to assume only predefinable discrete values from a predefinable list of discrete values. The method includes: providing at least one training input variable for the deep neural network; ascertaining a variable characterizing a cost function, which includes a first variable, which characterizes a deviation of an output variable of the deep neural network ascertained as a function of the provided training input variable relative to a predefinable setpoint output variable, and the variable characterizing the cost function further including at least one penalization variable, which characterizes a deviation of a value of one of the weights from at least one of at least two of the predefinable discrete values; training the deep neural network.