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公开(公告)号:US20230211692A1
公开(公告)日:2023-07-06
申请号:US17257298
申请日:2020-09-03
Applicant: GOOGLE LLC
Inventor: Alex Donaldson , David X. Wang , Kostas Kollias , Xin Wei Chow , Navin Gunatillaka , Jesse Head , Michael Graham Woodward , Ingrid Trollope , Anddrew Foster , Ivan Kuznetsov , Sreenivas Gollapudi
CPC classification number: B60L53/65 , G01C21/3469 , B60L2240/62 , B60L2240/70
Abstract: To navigate an electric vehicle from a starting location to a destination, a system identifies multiple charging stations between the starting location and the destination and determining a navigation route that requires a least amount of time for the electric vehicle to travel from the starting location to the destination via one or more of the charging stations, including determining a non-linear relationship between an amount of time and an amount of charge the EV receives during the amount of time.
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公开(公告)号:US20230281430A1
公开(公告)日:2023-09-07
申请号:US18118061
申请日:2023-03-06
Applicant: Google LLC
Inventor: Ali Kemal Sinop , Sreenivas Gollapudi , Petar Velickovic , Sofia Ira Ktena , Ameya Avinash Velingker
Abstract: Methods and systems for conditioning graph neural networks on affinity features. One of the methods includes obtaining graph data representing an input graph that comprises a set of nodes and a set of edges that each connect a respective pair of nodes, the graph data comprising respective node features for each of the nodes, edge features for each of the edges, and a respective weight for each of the edges; generating one or more affinity features, each affinity feature representing a property of one or more random walks through the graph guided by the respective weights for the edges; and processing the graph data using a graph neural network that is conditioned on the one or more affinity features to generate a task prediction for a machine learning task for the input graph.
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公开(公告)号:US20230388224A1
公开(公告)日:2023-11-30
申请号:US17886764
申请日:2022-08-12
Applicant: Google LLC
Inventor: Ali Kemal Sinop , Sreenivas Gollapudi , Konstantinos Kollias
Abstract: Example aspects of the present disclosure provide for an example computer-implemented method for generating alternative network paths, the example method including obtaining a network graph; determining flows respectively for edges of the network graph by: resolving a linear system of weights associated with the edges, the linear system resolved over a reduced network graph, and propagating a solution of the linear system into a respective partition of a plurality of partitions of the network graph to determine at least one of the flows within the respective partition; and determining a plurality of alternative paths across the network graph.
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