Vmware, Inc.

Walmart Apollo, LLC

Nikash Walia

Margaret Angeline Petrus

Sunitha Krishna

Karen Hayrapetyan

Abon Chaudhuri

Theban Stanley

Yanxin Pan

Nihar Vanjara

Systems and methods are described for recommending security groups using graph-based learning models. A server can create a network graph that illustrates network flows between devices in a network and security groups that the devices belong to. The network graph can include nodes that represent the devices and security groups. The server can apply a graph-based learning model to learn embeddings of the nodes and create vectors using the embeddings. Using vectors of two nodes, the server can calculate a vector that represents an edge between the two nodes. The server can apply a binary classifier determine whether the edge should exist. A 'true' classification between two nodes can indicate that they should be able to communicate, and vice versa. A “true” classification between a device node and a security group node can indicate that the device should be assigned to the security group, and vice versa.

Modifying network relationships using a heterogenous network flows graph

A systems including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: receiving a query image comprising at least one target object and at least one candidate image; generating a plurality of proposals based on the at least one candidate image; selecting a portion of the plurality of proposals to produce a reduced proposal set; generating a query embedding corresponding to the query image; generating candidate embeddings corresponding to the portion of the plurality of proposals included in the reduced proposal set; computing similarity scores for the portion of the plurality of proposals; and comparing the similarity scores to a threshold to determine if the at least one candidate image comprises the at least one target object. Other embodiments are disclosed herein.

Systems and methods for executing attention-based object searches on images using neural network architectures

Systems and methods are described for recommending security groups using graph-based learning models. A server can create a network graph that illustrates network flows between devices in a network and security groups that the devices belong to. The network graph can include nodes that represent the devices and security groups. The server can apply a graph-based learning model to learn embeddings of the nodes and create vectors using the embeddings. Using vectors of two nodes, the server can calculate a vector that represents an edge between the two nodes. The server can apply a binary classifier determine whether the edge should exist. A 'true' classification between two nodes can indicate that they should be able to communicate, and vice versa. A 'true' classification between a device node and a security group node can indicate that the device should be assigned to the security group, and vice versa.

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San Jose, CA, United States of America

Nikash Walia