{
  "$type": "site.standard.document",
  "description": "A method of training a predictor to predict a location of a computing device in an indoor environment incudes: receiving training data including strength of signals received from wireless access points at positions of an indoor environment, where the training data includes: a subset of labeled data…",
  "path": "/patents/1287213",
  "publishedAt": "2021-04-01T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G06N3/08",
    "NAVER CORPORATION"
  ],
  "textContent": "A method of training a predictor to predict a location of a computing device in an indoor environment incudes: receiving training data including strength of signals received from wireless access points at positions of an indoor environment, where the training data includes: a subset of labeled data including signal strength values and location labels; and a subset of unlabeled data including signal strength values and not including labels indicative of locations; training a variational autoencoder to minimize a reconstruction loss of the signal strength values of the training data, where the variational autoencoder includes encoder neural networks and decoder neural networks; and training a classification neural network to minimize a prediction loss on the labeled data, where the classification neural network generates a predicted location based on the latent variable, and where the encoder neural networks and the classification neural network form the predictor.",
  "title": "Semi-Supervised Variational Autoencoder for Indoor Localization"
}