System, method, and platform for auto machine learning via optimal hybrid AI formulation from crowd
Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a...
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creator | Savage, Lauren Austin, Mark Dabholkar, Abhay Nagarajan, Vijayan Whitney, Joshua |
description | Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed. |
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Other embodiments are disclosed.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNi7EOgjAQQFkcjPoP544DEmRWo9EZncnZXqVJr9eUouHvxcQPcHlveW-eqWbsE3EOTKkTnQN6DcFhMhIZJgAOSYBRddYTOMLorX_CyyJISJbRQTc-otWwv357HqbZigcThUFFeetlNjPoelr9vMjW59PteNlQkJb6gIo8pfbeFEVV7eqyPmzLf5oPSVA-JA</recordid><startdate>20230117</startdate><enddate>20230117</enddate><creator>Savage, Lauren</creator><creator>Austin, Mark</creator><creator>Dabholkar, Abhay</creator><creator>Nagarajan, Vijayan</creator><creator>Whitney, Joshua</creator><scope>EVB</scope></search><sort><creationdate>20230117</creationdate><title>System, method, and platform for auto machine learning via optimal hybrid AI formulation from crowd</title><author>Savage, Lauren ; Austin, Mark ; Dabholkar, Abhay ; Nagarajan, Vijayan ; Whitney, Joshua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11556737B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Savage, Lauren</creatorcontrib><creatorcontrib>Austin, Mark</creatorcontrib><creatorcontrib>Dabholkar, Abhay</creatorcontrib><creatorcontrib>Nagarajan, Vijayan</creatorcontrib><creatorcontrib>Whitney, Joshua</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Savage, Lauren</au><au>Austin, Mark</au><au>Dabholkar, Abhay</au><au>Nagarajan, Vijayan</au><au>Whitney, Joshua</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>System, method, and platform for auto machine learning via optimal hybrid AI formulation from crowd</title><date>2023-01-17</date><risdate>2023</risdate><abstract>Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. 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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
title | System, method, and platform for auto machine learning via optimal hybrid AI formulation from crowd |
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