Invention Grant
- Patent Title: System and a method for training a neural network having autoencoder architecture to recover missing data
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Application No.: US17197248Application Date: 2021-03-10
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Publication No.: US11698946B2Publication Date: 2023-07-11
- Inventor: Emil Laftchiev , Qing Yan , Daniel Nikovski
- Applicant: Mitsubishi Electric Research Laboratories, Inc.
- Applicant Address: US MA Cambridge
- Assignee: Mitsubishi Electric Research Laboratories, Inc.
- Current Assignee: Mitsubishi Electric Research Laboratories, Inc.
- Current Assignee Address: US MA Cambridge
- Agent Gennadiy Vinokur; Hironori Tsukamoto
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06F18/214 ; G16Y40/35 ; H04L67/12 ; G16Y40/10

Abstract:
A computer-implemented method of training an autoencoder to recover missing data is provided. The autoencoder includes an encoder for encoding its inputs into a latent space and a decoder for decoding the encodings from the latent space. The method comprises creating a first training set including a valid data set of multiple dimensions, and training the encoder and the decoder in a first training stage using the first training set to reduce a difference between the valid data set provided to the encoder and a data set decoded by the decoder. The method further comprises creating a second training set comprising an invalid data set, and training the encoder in a second training stage using the second training set to reduce a difference between encodings of valid data instances and encodings of their corresponding invalid data instances.
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