公开(公告)号：US20220335257A1

公开(公告)日：2022-10-20

申请号：US17231015

申请日：2021-04-15

Applicant: salesforce.com, inc.

Inventor： Devansh Arpit ,  Huan Wang ,  Caiming Xiong

IPC: G06K9/62 ,  G06N3/08

Abstract: A system uses a neural network to detect anomalies in time series data. The system trains the neural network for a fixed number of iterations using data from a time window of the time series. The system uses the loss value at the end of the fixed number of iterations for identifying anomalies in the time series data. For a time window, the system initializes the neural network to random values and trains the neural network for a fixed number of iterations using the data of the time window. After the fixed number of iterations, the system compares the loss values for various data points to a threshold value. Data points having loss value exceeding a threshold are identified as anomalous data points.

公开(公告)号：US20220108183A1

公开(公告)日：2022-04-07

申请号：US17154401

申请日：2021-01-21

Applicant: Salesforce.com, inc.

Inventor： Devansh Arpit

IPC: G06N3/08 ,  G06N3/04

Abstract: The embodiments are directed to training a momentum contrastive autoencoder using a contrastive learning framework. The contrastive learning framework learns a latent space distribution by matching latent representations of the momentum contrastive autoencoder to a pre-specified distribution, such as a distribution over a unit hyper-sphere. Once the latent space distribution is learned, samples for a new data set may be obtained from the latent space distribution. This results in a simple and scalable algorithm that avoids many of the optimization challenges of existing generative models, while retaining the advantage of efficient sampling.