公开(公告)号：US20240012875A1

公开(公告)日：2024-01-11

申请号：US18365568

申请日：2023-08-04

Applicant: Soumyasundar PAL ,  Yingxue ZHANG ,  Mark COATES

Inventor： Soumyasundar PAL ,  Yingxue ZHANG ,  Mark COATES

IPC: G06F17/17 ,  G08G1/08

CPC classification number: G06F17/17 ,  G08G1/08

Abstract: Probabilistic spatiotemporal forecasting comprising acquiring a time series of observed states from a real-world system, each observed state corresponding to a respective time-step in the time series and including a set of data observations of the real-world system for the respective time-step. For each of a plurality of the time steps in the time series of observed states, a hidden state is generated for the time-step based on an observed state for a prior time-step and an approximated posterior distribution generated for a hidden state for the prior time-step. The use of an approximated posterior distribution can enable improved forecasting in complex, high dimensional settings.

公开(公告)号：US11531886B2

公开(公告)日：2022-12-20

申请号：US16697124

申请日：2019-11-26

Applicant: Yingxue Zhang ,  Soumyasundar Pal ,  Mark Coates ,  Deniz Ustebay

Inventor： Yingxue Zhang ,  Soumyasundar Pal ,  Mark Coates ,  Deniz Ustebay

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

Abstract: Method and system for predicting labels for nodes in an observed graph, including deriving a plurality of random graph realizations of the observed graph; learning a predictive function using the random graph realizations; predicting label probabilities for nodes of the random graph realizations using the learned predictive function; and averaging the predicted label probabilities to predict labels for the nodes of the observed graph.

公开(公告)号：US20210158149A1

公开(公告)日：2021-05-27

申请号：US16697124

申请日：2019-11-26

Applicant: Yingxue ZHANG ,  Soumyasundar PAL ,  Mark COATES ,  Deniz USTEBAY

Inventor： Yingxue ZHANG ,  Soumyasundar PAL ,  Mark COATES ,  Deniz USTEBAY

IPC: G06N3/08 ,  G06N3/04

Abstract: Method and system for predicting labels for nodes in an observed graph, including deriving a plurality of random graph realizations of the observed graph; learning a predictive function using the random graph realizations; predicting label probabilities for nodes of the random graph realizations using the learned predictive function; and averaging the predicted label probabilities to predict labels for the nodes of the observed graph.