Machine-Learned State Space Model for Joint Forecasting

    公开(公告)号:US20210065066A1

    公开(公告)日:2021-03-04

    申请号:US17008338

    申请日:2020-08-31

    Applicant: Google LLC

    Abstract: A deep state space generative model is augmented with intervention prediction. The state space model provides a principled way to capture the interactions among observations, interventions, critical event occurrences, true states, and associated uncertainty. The state space model can include a discrete-time hazard rate model that provides flexible fitting of general survival time distributions. The state space model can output a joint prediction of event risk, observation and intervention trajectories based on patterns in temporal progressions, and correlations between past measurements and interventions.

    Content placement criteria expansion

    公开(公告)号:US10346492B2

    公开(公告)日:2019-07-09

    申请号:US15298324

    申请日:2016-10-20

    Applicant: Google LLC

    Abstract: Systems and methods of providing information via a computer network are provided. A data processing system can identify a cluster that includes a plurality of online content items having a semantic or user similarity. The data processing system determines a plurality of cluster placement criteria of the cluster, and receives content configured for display with a web page. The content can be associated with the cluster based on the semantic or user similarity. A cluster placement criterion of the plurality of cluster placement criteria can be selected based on a quality metric of the selected cluster placement criterion, and the selected cluster placement criterion can be provided as a supplemental criterion used to select the content for display with the web page.

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