Sap Se

Juniper Networks, Inc.

Sap Ag

Srivatsan Santhanam

Atreya Biswas

Jackson Mathai

Denny Jee King Gee

Raghavendra Deshpande

Tapan Kumar Chauhan

Aurobinda Pradhan

Nakul P Shenoy

Aniruddha Bhosekar

Ankit Anand

Prabha Parthasarathy

Saurabh Srivastava

Nirmal Baven

Anmol Bhat

Muthuraman M

Methods, systems, and computer-readable storage media for receiving, by a ML application executing within a cloud platform, a first inference request, the first inference request including first inference data, transmitting, by the ML application, the first inference data to the UAT system within the cloud platform, retrieving, by the UAT system, a first ML model in response to the inference request, the first ML model being in an inactive state, providing, by the UAT system, a first inference based on the first inference data using the first ML model, providing a first accuracy evaluation at least partially based on the first inference, and transitioning the first ML model from the inactive state to an active state, the first ML model being used for production in the active state.

User acceptance test system for machine learning systems

A framework provides a detailed explanation regarding specific aspects of a (complex) calculation produced by an application (e.g., an analytical application). An explainability engine receives a request for explanation of the calculation. The explainability engine traverses homogenous data clusters according to the request, in order to produce a final path. The final path is used to select and then populate a template comprising explanation note(s). The outcome (comprising the final path and the template) is processed with a ruleset according to a covariance (COV) function in order to provide a first intermediate outcome. The first intermediate result is then processed with a second input according to a correlation (COR) function to provide a second intermediate outcome. The second intermediate result is processed according to a challenge function to provide a challenged outcome, and feedback (e.g., reward or penalization) to the ruleset. The challenged outcome provides detailed explanation to the user.

Explanation of computation result using challenge function

Methods, systems, and computer-readable storage media for receiving historical data, the historical data including variable vectors, each variable vector being assigned to a class, processing the historical data through encoders to provide feature vectors, each feature vector corresponding to a respective variable vector and being assigned to the class of the respective variable vector, generating a set of decision trees based on the feature vectors, each decision tree corresponding to a class in the set of classes, transforming each decision tree into a set of rules to provide sets of rules, each rule in a set of rules defining conditions to assign at least a portion of an electronic document to a respective class in the set of classes, and providing the sets of rules for execution in an enterprise system, the enterprise system classifying electronic documents to classes in the set of classes based on the sets of rules.

Automated rule generation framework using machine learning for classification problems

A device may receive, from a set of elements of a set of network devices, information associated with a set of parameters. The device may determine an event based on the set of parameters. The device may determine a rule set based on the event. The device may determine other events that are associated with the event based on the rule set. The device may determine statuses of the other events based on the information associated with the set of parameters. The device may evaluate a rule, of the rule set, based on the event and the statuses of the other events. The device may determine an action to be performed based on evaluating the rule. The device may cause the action to be performed in association with the element of the set of elements.

Network device management

According to some embodiments, a method and system provides receiving a list of a plurality of conversations, each of the conversations associated with a common set of characteristics and each of the conversations having associated conversation details; presenting at least a portion of the list of the plurality of conversations in a user interface, each of the conversations included in the presented portion of the list being separately represented; receiving a selection of a plurality of the conversations included in the presented portion of the list; and presenting, simultaneously and grouped together in the user interface, conversation details associated with each of the selected plurality of the conversations, separate and apart from the presented portion of the list.

Conversation explorer with split navigation user interface

A method of providing user input values for input controls includes receiving, in a graphical user interface wherein input controls are associated with predefined alternative input values, a first input identifying user selection of at least some of the input controls. The method includes receiving a second input identifying user selection of at least one of the predefined alternative input values that is associated with each of the selected input controls. The second input is made using one of the selected input controls. The method includes entering the selected predefined alternative input value in each of the selected input controls. The value may be automatically entered upon receiving the second input. A third input, for example made with a context menu, may be required to enter the value in the selected controls.

Providing user input values in input controls

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Bengaluru, India

Srivatsan Santhanam