US 2018/0293464 A1 · METHOD FOR UNDERSTANDING MACHINE-LEARNING DECISIONS BASED ON CAMERA DATA

Abstract

Described is a system for understanding machine-learning decisions. In an unsupervised learning phase, the system extracts, from input data, concepts represented by a machine-learning (ML) model in an unsupervised manner by clustering patterns of activity of latent variables of the concepts, where the latent variables are hidden variables of the ML model. The extracted concepts are organized into a concept network by learning functional semantics among the extracted concepts. In an operational phase, a subnetwork of the concept network is generated. Nodes of the subnetwork are displayed as a set of visual images that are annotated by weights and labels, and the ML model per the weights and labels.

Claims23

Dependent claims

A system for understanding machine-learning (ML) decisions, the system comprising:

one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of:
in an unsupervised learning phase, extracting from input data a plurality of concepts represented by an ML model in an unsupervised manner by clustering patterns of activity of latent variables of the concepts, wherein the latent variables are hidden variables of the ML model;
in the unsupervised learning phase, organizing the extracted concepts into a concept network by learning functional semantics among the extracted concepts;
in an operational phase, generating a subnetwork of the concept network;
and
displaying nodes of the subnetwork as a set of visual images that are annotated by weights and labels;
and
refining the ML model per the weights and labels.

5 more elements

A computer implemented method for understanding machine-learning (ML) decisions, the method comprising an act of:

causing one or more processers to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
in an unsupervised learning phase, extracting from input data, a plurality of concepts represented by an ML model in an unsupervised manner by clustering patterns of activity of latent variables of the concepts, wherein the latent variables are hidden variables of the ML model;
in the unsupervised learning phase, organizing the extracted concepts into a concept network by learning functional semantics among the extracted concepts;
in an operational phase, generating a subnetwork of the concept network;
displaying nodes of the subnetwork as a set of visual images that are annotated by weights and labels;
and
refining the ML model per the weights and labels.

4 more elements

A computer program product for understanding machine-learning (ML) decisions, the computer program product comprising:

computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of:
in an unsupervised learning phase, extracting from input data a plurality of concepts represented by an ML model in an unsupervised manner by clustering patterns of activity of latent variables of the concepts, wherein the latent variables are hidden variables of the ML model;
in the unsupervised learning phase, organizing the extracted concepts into a concept network by learning functional semantics among the extracted concepts;
in an operational phase, generating a subnetwork of the concept network;
displaying nodes of the subnetwork as a set of visual images that are annotated by weights and labels;
and
refining the ML model per the weights and labels.

4 more elements

Dependencies are inferred from the claim text.

Method for understanding machine-learning decisions based on camera data

Life of this patent

Method for understanding machine-learning decisions based on camera data

Life of this patent

Abstract

Claims23