Semi-supervised learning for landmark localization.

Semi-supervised learning for landmark localization

Номер патента: US20180365532A1
Опубликовано: 20-12-2018
Автор(ы): Jan Kautz, Pavlo Molchanov, Sina Honari, Stephen Walter Tyree
Принадлежит: Nvidia Corp
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Рисунок 1. Взаимосвязь патентов (ближайшие 20).

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Methods, systems, and computer readable media for utilizing machine learning for merging duplicate data records

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Automatic testing with feature tags trained by machine learning for updates in version control systems

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Machine learning for home understanding and notification

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Character-based representation learning for information extraction using artificial intelligence techniques

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The SoupSauce specification is an extension to the invention for block chain processing for consistent asynchronous, non-linear, localized, state-driven and/or condition-based procedurality in distributed systems (and enabling safer application of AI learning) for distributed information system processing for wide regulatory purposes related to digital technologies.

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The PrioSab specification is an extension to the invention for block chain processing for consistent asynchronous, non-linear, localized, state-driven and/or condition-based procedurality in distributed systems (and enabling safer application of AI learning) for distributed information system processing where priority processing schemes and reconfiguration or voting using redundancy and signal continuity can be designated and controlled on or within a device using a variety of different specified mechanisms.

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IntegPplz is an extension to block chain processing (ie for consistent asynchronous, non-linear, localized, state-driven and/or condition-based procedurality in distributed systems (and enabling safer application of AI learning)) for situations where information related to people, assets and activities with commercial /care implications that allows degrees of centralized management of collaborative arrangements including technical processes and contracts and/or info flows.

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EQoStaple device is a scope3 PARTIAL offset tag functionally complementing the CleanStaple zero sum Scope 3 supply tag. It is accompanied by an extension to the inventions for Emissions tracking and for block chain processing that supports the processing for such tags (consistent asynchronous, non-linear, localized, state-driven and/or condition-based procedurality in distributed systems and enabling safer application of AI learning) for data/use cases where there is no access or definition of a single global time signal or where time zone information is less effective due to privacy protectio

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The Algo specification is an extension to the invention for block chain processing for consistent asynchronous, non-linear, localized, state-driven and/or condition-based procedurality in distributed systems (and enabling safer application of AI learning) for distributed information system processing where algorithmic approaches should be produced, systemized and managed in a way to be shared that is as consistent as possible with other shared systemizing structures.

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ConfidOrder is an extension to block chain processing (ie for consistent asynchronous, non-linear, localized, state-driven and/or condition-based procedurality in distributed systems (and enabling safer application of AI learning)) for information system processing related to formalizing order by independent or regulated processes and publically accountable means according to regulation over individuals and organizations within one or more jurisdictions enabled by personal or other ‘constitutional’ devices and associated systems, including for governance checks in voting.

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ClarZ is a possible patent of addition extension to the invention for block chain processing for consistent asynchronous, non-linear, localized, state-driven and/or condition-based procedurality in distributed systems (and enabling safer application of AI learning) for data/use cases where there is no access or definition of a single global time signal or where time zone information becomes less effective due to privacy protections.

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Systems and Methods for Learning-based Network. The present invention relates generally to social network field and more specifically to a new and useful network to connect learners with their peers and educators. The platform creates a learning chain to link topics in a logical sequence so learners who struggle with a certain topic, can check backward learning chain for the prior topics to be learned and learners can check forward learning chain to figure out what to learn for their dream job. A learning management system is accessible along the chain that includes learning material.

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Semi-supervised learning for landmark localization

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