WO2023141847 - APPARATUS AND METHODS FOR RELIABILITY ADAPTATION FOR ARTIFICIAL INTELLIGENCE TRAINING
National phase entry:
Publication Number
WO/2023/141847
Publication Date
03.08.2023
International Application No.
PCT/CN2022/074159
International Filing Date
27.01.2022
Title **
[English]
APPARATUS AND METHODS FOR RELIABILITY ADAPTATION FOR ARTIFICIAL INTELLIGENCE TRAINING
[French]
APPAREIL ET PROCÉDÉS D'ADAPTATION DE FIABILITÉ POUR ENTRAÎNEMENT D'INTELLIGENCE ARTIFICIELLE
Applicants **
HUAWEI TECHNOLOGIES CO., LTD.
Inventors
TANG, Hao
ZHANG, Liqing
MA, Jianglei
GE, Yiqun
ZHU, Peiying
TONG, Wen
Application details
| Total Number of Claims/PCT | * |
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International Searching Authority |
CNIPA
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| Recordal of a Change of the Applicant's Name/Address |
Change of Applicant's Name and Address
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| Type of Assignment |
The Standard Agent's Assignment
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| Applicant's Legal Status |
Legal Entity
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| Entry into National Phase under |
Chapter I
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| Patent Delivery |
Send the Letters Patent by Courier
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| 译文 |
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Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 2691 | |
| EPO | Filing, Examination, Granting | 24784 | |
| Japan | Filing, Examination, Granting | 3010 | |
| South Korea | Filing, Examination, Granting | 3800 | |
| USA | Filing, Examination, Granting | 8540 |
Total:
42,825
The term for entry into the National Phase has expired. This quotation is for informational purposes only
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Abstract[English]
Current online training procedures for artificial intelligence/machine learning (AI/ML) models for wireless communication generally suffer from high communication overhead and/or significant delays in training, particularly when training data is exchanged over an unreliable/hostile communication channel. In some embodiments, different reliability modes are used to provide different levels of reliability to provide a tradeoff between overhead reductions and training performance.[French]
Les procédures d'apprentissage en ligne actuelles pour des modèles d'intelligence artificielle/d'apprentissage machine (IA/ML) pour une communication sans fil souffrent généralement d'une surcharge de communication élevée et/ou de retards significatifs de l'entraînement, en particulier lorsque des données d'entraînement sont échangées sur un canal de communication non fiable/hostile. Dans certains modes de réalisation de l'invention, différents modes de fiabilité sont utilisés pour fournir différents niveaux de fiabilité pour fournir un compromis entre les réductions de surcharge et la performance d'entraînement.