Invention Grant
- Patent Title: Predicting likelihoods of conditions being satisfied using recurrent neural networks
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Application No.: US15588535Application Date: 2017-05-05
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Publication No.: US10726327B2Publication Date: 2020-07-28
- Inventor: Gregory Sean Corrado , Ilya Sutskever , Jeffrey Adgate Dean
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G16H50/20 ; G06N3/063 ; G06N3/02

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.
Public/Granted literature
- US20170308787A1 PREDICTING LIKELIHOODS OF CONDITIONS BEING SATISFIED USING RECURRENT NEURAL NETWORKS Public/Granted day:2017-10-26
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