MACHINE LEARNING COMMONS

Brand Owner Address Description
MLCOMMONS MLCOMMONS ASSOCIATION 649 Mission Street, 5th Floor San Fransisco CA 94105 MACHINE LEARNING COMMONS;Association services, namely, promoting innovation in the field of machine learning; Association services, namely, promoting public awareness of machine learning training accuracy, speed, performance, and efficiency; Association services, namely, promoting public awareness of machine learning inference accuracy, speed, performance, and efficiency; Association services, namely, promoting public awareness of mobile device and embedded device machine learning accuracy, speed, performance, and efficiency; Business services, namely, formulation of best practices for machine learning and machine learning interoperability and functionality to accelerate progress in the field of machine learning; Providing information, data sets, and best practices pertaining to machine learning and machine learning interoperability and functionality; Collection, analysis, and display of data for business purposes in the fields of machine learning and machine learning interoperability and functionality; Collection, analysis, and display of device and test data pertaining to machine learning, namely, compiling and analyzing statistics, device test data, and server test data regarding the machine learning interoperability and functionality of such devices and servers for business purposes; Organizing, arranging, and conducting presentations on the topic of machine learning; Organizing, arranging, and conducting business networking events in the field of machine learning;Design and development of technical specifications in the field of machine learning requirements, interoperability, and functionality; Design and development of technical specifications for devices and servers pertaining to machine learning requirements, interoperability, and functionality; Design and development of technical specifications for devices and servers pertaining to machine learning training and inference accuracy, speed, performance, and efficiency; Design and development of technical specifications for mobile device and embedded device machine learning accuracy, speed, performance, and efficiency; Design and development of software in the field of the machine learning; Design and development of software for testing devices and servers; Design and development of software for testing devices and servers for compliance with machine learning requirements and to measure machine learning interoperability and functionality; Providing benchmarking information and best practices in the field of machine learning for use to measure accuracy, speed, performance, efficiency, and interoperability;
 

Where the owner name is not linked, that owner no longer owns the brand

    
Technical Examples
  1. A system and method for processing machine learning techniques (such as neural networks) and other non-graphics applications using a graphics processing unit (GPU) to accelerate and optimize the processing. The system and method transfers an architecture that can be used for a wide variety of machine learning techniques from the CPU to the GPU. The transfer of processing to the GPU is accomplished using several novel techniques that overcome the limitations and work well within the framework of the GPU architecture. With these limitations overcome, machine learning techniques are particularly well suited for processing on the GPU because the GPU is typically much more powerful than the typical CPU. Moreover, similar to graphics processing, processing of machine learning techniques involves problems with solving non-trivial solutions and large amounts of data.