Patents by Inventor Markus Weimer
Markus Weimer has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11922315Abstract: Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training.Type: GrantFiled: August 26, 2019Date of Patent: March 5, 2024Assignee: Microsoft Technology Licensing, LLC.Inventors: Matteo Interlandi, Byung-Gon Chun, Markus Weimer, Gyeongin Yu, Saeed Amizadeh
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Publication number: 20230342359Abstract: Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.Type: ApplicationFiled: June 30, 2023Publication date: October 26, 2023Inventors: Irene Rogan SHAFFER, Remmelt Herbert Lieve AMMERLAAN, Gilbert ANTONIUS, Marc T. FRIEDMAN, Abhishek ROY, Lucas ROSENBLATT, Vijay Kumar RAMANI, Shi QIAO, Alekh JINDAL, Peter ORENBERG, H M Sajjad Hossain, Soundararajan Srinivasan, Hiren Shantilal PATEL, Markus WEIMER
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Patent number: 11748350Abstract: Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.Type: GrantFiled: April 3, 2020Date of Patent: September 5, 2023Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Irene Rogan Shaffer, Remmelt Herbert Lieve Ammerlaan, Gilbert Antonius, Marc T. Friedman, Abhishek Roy, Lucas Rosenblatt, Vijay Kumar Ramani, Shi Qiao, Alekh Jindal, Peter Orenberg, H M Sajjad Hossain, Soundararajan Srinivasan, Hiren Shantilal Patel, Markus Weimer
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Publication number: 20220051104Abstract: Methods, systems, and computer program products are provided for generating a neural network model. A ML pipeline parser is configured to identify a set of ML operators for a previously trained ML pipeline, and map the set of ML operators to a set of neural network operators. The ML pipeline parser generates a first neural network representation using the set of neural network operators. A neural network optimizer is configured to perform an optimization on the first neural network representation to generate a second neural network representation. A tensor set provider outputs a set of tensor operations based on the second neural network representation for execution on a neural network framework. In this manner, a traditional ML pipeline can be converted into a neural network pipeline that may be executed on an appropriate framework, such as one that utilizes specialized hardware accelerators.Type: ApplicationFiled: August 14, 2020Publication date: February 17, 2022Inventors: Matteo INTERLANDI, Markus WEIMER, Saeed AMIZADEH, Konstantinos KARANASOS, Supun Chathuranga NAKANDALA, Karla J. SAUR, Carlo Aldo CURINO, Gyeongin YU
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Publication number: 20210263932Abstract: Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.Type: ApplicationFiled: April 3, 2020Publication date: August 26, 2021Inventors: Irene Rogan Shaffer, Remmelt Herbert Lieve Ammerlaan, Gilbert Antonius, Marc T. Friedman, Abhishek Roy, Lucas Rosenblatt, Vijay Kumar Ramani, Shi Qiao, Alekh Jindal, Peter Orenberg, H M Sajjad Hossain, Soundararajan Srinivasan, Hiren Shantilal Patel, Markus Weimer
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Publication number: 20210124739Abstract: The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.Type: ApplicationFiled: August 11, 2020Publication date: April 29, 2021Applicant: Microsoft Technology Licensing, LLCInventors: Konstantinos KARANASOS, Matteo INTERLANDI, Fotios PSALLIDAS, Rathijit SEN, Kwanghyun PARK, Ivan POPIVANOV, Subramaniam VENKATRAMAN KRISHNAN, Markus WEIMER, Yuan YU, Raghunath RAMAKRISHNAN, Carlo Aldo CURINO, Doris Suiyi XIN, Karla Jean SAUR
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Publication number: 20210065007Abstract: Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training.Type: ApplicationFiled: August 26, 2019Publication date: March 4, 2021Inventors: Matteo INTERLANDI, Byung-Gon CHUN, Markus WEIMER, Gyeongin YU, Saeed AMIZADEH
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Patent number: 10298526Abstract: Embodiments are directed towards multi-level entity classification. An object associated with an entity is received. In one embodiment the object comprises and email and the entity comprises the IP address of a sending email server. If the entity has already been classified, as indicated by an entity classification cache, then a corresponding action is taken on the object. However, if the entity has not been classified, the entity is submitted to a fast classifier for classification. A feature collector concurrently fetches available features, including fast features and full features. The fast classifier classifies the entity based on the fast features, storing the result in the entity classification cache. Subsequent objects associated with the entity are processed based on the cached result of the fast classifier. Then, a full classifier classifies the entity based on at least the full features, storing the result in the entity classification cache.Type: GrantFiled: September 12, 2016Date of Patent: May 21, 2019Assignee: OATH INC.Inventors: Sharat Narayan, Vishwanath Tumkur Ramarao, Belle Tseng, Markus Weimer, Young Maeng, Jyh-Shin Shue
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Publication number: 20170012912Abstract: Embodiments are directed towards multi-level entity classification. An object associated with an entity is received. In one embodiment the object comprises and email and the entity comprises the IP address of a sending email server. If the entity has already been classified, as indicated by an entity classification cache, then a corresponding action is taken on the object. However, if the entity has not been classified, the entity is submitted to a fast classifier for classification. A feature collector concurrently fetches available features, including fast features and full features. The fast classifier classifies the entity based on the fast features, storing the result in the entity classification cache. Subsequent objects associated with the entity are processed based on the cached result of the fast classifier. Then, a full classifier classifies the entity based on at least the full features, storing the result in the entity classification cache.Type: ApplicationFiled: September 12, 2016Publication date: January 12, 2017Inventors: Sharat Narayan, Vishwanath Tumkur Ramarao, Belle Tseng, Markus Weimer, Young Maeng, Jyh-Shin Shue
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Patent number: 9442881Abstract: Embodiments are directed towards multi-level entity classification. An object associated with an entity is received. In one embodiment the object comprises and email and the entity comprises the IP address of a sending email server. If the entity has already been classified, as indicated by an entity classification cache, then a corresponding action is taken on the object. However, if the entity has not been classified, the entity is submitted to a fast classifier for classification. A feature collector concurrently fetches available features, including fast features and full features. The fast classifier classifies the entity based on the fast features, storing the result in the entity classification cache. Subsequent objects associated with the entity are processed based on the cached result of the fast classifier. Then, a full classifier classifies the entity based on at least the full features, storing the result in the entity classification cache.Type: GrantFiled: August 31, 2011Date of Patent: September 13, 2016Assignee: Yahoo! Inc.Inventors: Sharat Narayan, Vishwanath Tumkur Ramarao, Belle Tseng, Markus Weimer, Young Maeng, Jyh-Shin Shue