International Business Machines Corporation

Walmart Apollo, LLC

Sakib Abdul Mondal

Gargi Banerjee Dasgupta

Renuka Sindhgatta

Rodney B Wallace

Tuhin Bhattacharya

Abhijit Mondal

This application relates to apparatus and methods for training machine learning models using supervised, or semi-supervised, learning. In some examples, a computing device obtains training data that includes labelled, and unlabeled, data for training a machine learning model. The computing device applies the machine learning model to the training data to generate output data. The machine learning model executes with a plurality of coefficients applied to a plurality of hyperparameters. The computing device further applies a loss model to the training data and the output data to generate a loss value. Based on the loss values, the computing device determines updated values for the plurality of coefficients of the machine learning model. The computing device may continue to determine updated values for the plurality of coefficients until one or more conditions are satisfied. The computing device may then store the final coefficient values in a data repository.

Methods and apparatus for automatic attribute extraction for training machine learning models

Systems and methods for unified attribute extraction are disclosed. A set of product data including at least one text-based description of each of the products in the set of products is received and at least one closed list attribute is extracted from the at least one text-based description of each of the products. The at least one closed list attribute is extracted by an attribute extraction model configured to implement an extended conditional random field (XCRF) process. A set of attributes including each of the at least one closed list attributes extracted from the at least one text-based description of each of the products in the set of products is output.

Systems and methods for unified extraction of attributes

Optimizing allocation of multi-tasking servers is provided. A schedule that identifies a plurality of time periods is received. The schedule associates each server of a plurality of servers with at least one time period. Performance characteristics, including a multitasking threshold, of each server of the plurality of servers is determined. A simulation of the plurality of servers is performed to determine a service level of the simulation. A workload identifying a plurality of incidents is received. Each incident is allocated to a server. One or more performance metrics of each time period is determined. The service level of the simulation is determined based, at least in part, on the one or more performance metrics of each time period. The schedule is optimized based, at least in part, on at least one of the service level and a target service level.

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Bengaluru, India

Sakib Abdul Mondal