公开(公告)号：US20240112013A1

公开(公告)日：2024-04-04

申请号：US17951889

申请日：2022-09-23

Applicant: Google LLC

Inventor： Hanjun Dai ,  Bo Dai ,  Mengjiao Yang ,  Yuan Xue ,  Dale Eric Schuurmans

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/0472

Abstract: The present disclosure is directed to generative models for datasets constrained by marginal constraints. One method includes receiving a request to generate a target dataset based on a marginal constraint for a source dataset. A first object occurs at a source frequency in the source dataset. The marginal constraint indicates a target frequency for the first object. The source dataset encodes a set of co-occurrence frequencies for a plurality of object pairs. A source generative model is accessed. The source generative model includes a first module and a second module that are trained on the source dataset. The second module is updated based on the marginal constraint. An adapted generative model is generated that includes the first module and the updated second module. The target dataset is generated based on the adapted generative model. The first object occurs at the target frequency in the target dataset. The target dataset encodes the set of co-occurrence frequencies for the plurality of object pairs.

公开(公告)号：US11947503B2

公开(公告)日：2024-04-02

申请号：US17351086

申请日：2021-06-17

Applicant: Google LLC

Inventor： Hanjun Dai ,  Azade Nazi ,  Yujia Li ,  Bo Dai ,  Dale Eric Schuurmans

IPC: G06F16/21 ,  G06F16/22 ,  G06N3/045

CPC classification number: G06F16/212 ,  G06F16/2237 ,  G06F16/2246 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating data defining a graph. In one aspect, a method comprises: sequentially generating a respective edge set for each node in the graph, wherein for each of a plurality of nodes after a first node, generating the edge set for the node comprises: receiving a context embedding for the node that summarizes a respective edge set for each node that precedes the node; generating, based on the context embedding for the node: (i) a respective edge set for the node, and (ii) a respective embedding of the edge set for the node; generating a context embedding for a next node in the ordering of the nodes using the embedding of the edge set for the node; and adding the set of edges defined by the edge set for the node to the graph.

公开(公告)号：US20230289626A1

公开(公告)日：2023-09-14

申请号：US18183410

申请日：2023-03-14

Applicant: Google LLC

Inventor： Hanjun Dai ,  Dale Eric Schuurmans ,  Xinyun Chen ,  Dengyong Zhou ,  Bo Dai ,  Hongyu Ren

IPC: G06N5/022 ,  G06F16/2453

CPC classification number: G06N5/022 ,  G06F16/2453

Abstract: Provided are computing systems, methods, and platforms for negative sampling in knowledge graphs with improved efficiency. A knowledge graph comprising entities and links between the entities can be obtained. A query computation graph comprising nodes and edges can be generated based on the knowledge graph. The nodes of the query computation graph can include anchor nodes, a root node, and intermediate nodes positioned in paths between the anchor nodes and the root node. A node cut of a query of the query computation graph can be determined and can include at least one node that cuts at least one path between each anchor node and the root node of the query computation graph. Negative samples can be identified by bidirectionally traversing the query computation graph in a first direction from the anchor nodes to the node cut and in a second direction from the root node to the node cut.