8 results about "Problem space" patented technology

Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.

A computer implemented method for certifying robustness of image classification in a neural network is provided. The method includes initializing a neural network model. The neural network model includes a problem space and a decision boundary. A processor receives a data set of images, image labels, and a perturbation schedule. Images are drawn from the data set in the problem space. A distance from the decision boundary is determined for the images in the problem space. A re-weighting value is applied to the images. A modified perturbation magnitude is applied to the images. A total loss function for the images in the problem space is determined using the re-weighting value. A confidence level of the classification of the images in the data set is evaluated for certifiable robustness.

The invention relates to a program testing method and device based on an SARSA algorithm, and the method comprises the steps: carrying out the environment modeling of a to-be-tested program, and obtaining a problem space; initializing SARSA algorithm parameters to obtain a test algorithm; executing the problem space through the test algorithm to obtain an execution result; and calculating a test index according to the execution result to obtain a test result. According to the method, the test coverage rate is increased, and the program test efficiency and accuracy are improved.

A numerical time series analysis and data simplification system and a software-implemented numerical time series analysis and data simplification process are disclosed. The numerical time series analysis and data simplification system and process provide several functions including channel math, signal filtering, and concatenation functions. The numerical time series analysis and data simplification system and process focus on a very specific engineering problem space and provide a generic solution to the likely variations of the problem space.

Systems and methods for selecting neural networks for machine learning problems are disclosed. A method includes accessing an input matrix. The method includes accessing a machine learning problem space associated with a machine learning problem and a plurality of untrained candidate neural networks for solving the machine learning problem. The method includes calculating, for each untrained candidate neural network, at least one expressivity metric that captures an expressivity of the candidate neural network with respect to a machine learning problem. The method includes calculating, for each untrained candidate neural network, at least one trainability metric that captures trainability of the candidate neural network with respect to a machine learning problem. The method includes selecting at least one candidate neural network for solving a machine learning problem based on at least one expressive metric and at least one trainability metric.

The present invention discloses a method for automatically generating audit suggestions in an audit report. First, the audit suggestion statements in previous audit reports are collected to construct an audit suggestion library; then, the audit problems pointed out in the audit report are encoded to obtain their embedding vectors in the problem space; then, a mapper is used to map the encoding of the audit problems in the problem space to the suggestion space; finally, the semantic model and the mapper are jointly trained to learn the parameters therein. When writing a new audit report, the problems found in the audit are input into the semantic model and the mapper to obtain the embedding vector of the problem in the suggestion space, and several suggestion statements closest to the embedding vector are selected from the audit suggestion library to generate the audit suggestions in the report. The present invention can make full use of the rigorous and standardized audit suggestion statements in the existing audit reports accumulated over a long period of time, and automatically generate accurate and professional audit suggestions based on the problems found in the audit.

Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.

Among a great deal of other disclosure and scope, systems and methods are enclosed that enable automated labelling of a subset of vectors in a given problem space. For example, in some of many cases, a first machine learning model pre-trained on a given problem space makes predictions regarding fresh, unseen data. In addition to this prediction, the model can output a confidence metric indicating its confidence regarding the prediction made. A subset of these vectors with the highest confidence may be selected. Relevant heuristics assessing each vector in the subset may be computed. These heuristics can be fed through a second machine learning model, which identifies if the given prediction made by the first model is correct. If so, the vector is automatically annotated with the correct predicted label, the vector is appended to the labeled set of data, and the first model is retrained with the new labeled set of data.