{
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  "description": "Methods of training predictors for the location of a computing device in an indoor environment are provided. The methods comprise receiving training data comprising labelled data and unlabelled data. A method of training a predictor comprises training a variational autoencoder, wherein the…",
  "path": "/patents/1424443",
  "publishedAt": "2025-10-29T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G01C21/206",
    "NAVER LABS CORP [KR]"
  ],
  "textContent": "Methods of training predictors for the location of a computing device in an indoor environment are provided. The methods comprise receiving training data comprising labelled data and unlabelled data. A method of training a predictor comprises training a variational autoencoder, wherein the variational autoencoder comprises encoder neural networks, which encode signal strength values in a latent variable, and decoder neural networks, which decode the latent variable to reconstructed signal strength values, and training a classification neural network that employs the latent variable to generate a predicted location. Another method of training a predictor comprises training a classification neural network together with a variational autoencoder, wherein the classification neural network receives signal strength values of the training data as input and outputs a predicted location to decoder neural networks of the variational autoencoder.",
  "title": "USING SEMI-SUPERVISED VARIATIONAL AUTOENCODER FOR WI-FI-BASED INDOOR LOCALIZATION"
}