BR112023019673A2 - REPORTS FOR MACHINE LEARNING MODEL UPDATES - Google Patents

REPORTS FOR MACHINE LEARNING MODEL UPDATES

Info

Publication number
BR112023019673A2
BR112023019673A2 BR112023019673A BR112023019673A BR112023019673A2 BR 112023019673 A2 BR112023019673 A2 BR 112023019673A2 BR 112023019673 A BR112023019673 A BR 112023019673A BR 112023019673 A BR112023019673 A BR 112023019673A BR 112023019673 A2 BR112023019673 A2 BR 112023019673A2
Authority
BR
Brazil
Prior art keywords
neural network
reports
network parameters
machine learning
learning model
Prior art date
Application number
BR112023019673A
Other languages
Portuguese (pt)
Inventor
Hamed Pezeshki
Taesang Yoo
Tao Luo
Original Assignee
Qualcomm Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US17/694,467 external-priority patent/US20220335294A1/en
Application filed by Qualcomm Inc filed Critical Qualcomm Inc
Publication of BR112023019673A2 publication Critical patent/BR112023019673A2/en

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0813—Configuration setting characterised by the conditions triggering a change of settings
    • H04L41/082—Configuration setting characterised by the conditions triggering a change of settings the condition being updates or upgrades of network functionality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/06—Generation of reports
    • H04L43/065—Generation of reports related to network devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

relatórios para atualizações de modelo de aprendizado de máquina. um receptor recebe, a partir de um transmissor, uma rede neural de referência. o receptor treina a rede neural de referência para obter parâmetros de rede neural atualizados para a rede neural de referência. o receptor relata ao transmissor em resposta a um disparo, uma diferença entre os parâmetros de rede neural atualizados e os parâmetros de rede neural anteriores para a rede neural de referência. o disparo pode ser baseado em uma função de perda, uma magnitude da diferença entre os parâmetros de rede neural atualizados e os parâmetros de rede neural anteriores e/ou uma diferença entre o desempenho da rede neural de referência com os parâmetros de rede neural atualizados e o desempenho da rede neural de referência com os parâmetros de rede neural anteriores.reports for machine learning model updates. a receiver receives, from a transmitter, a reference neural network. the receiver trains the reference neural network to obtain updated neural network parameters for the reference neural network. the receiver reports to the transmitter in response to a trigger, a difference between the updated neural network parameters and the previous neural network parameters for the reference neural network. triggering may be based on a loss function, a magnitude of difference between the updated neural network parameters and the previous neural network parameters, and/or a difference between the performance of the reference neural network with the updated neural network parameters and the performance of the reference neural network with the previous neural network parameters.

BR112023019673A 2021-04-20 2022-03-15 REPORTS FOR MACHINE LEARNING MODEL UPDATES BR112023019673A2 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US202163177180P 2021-04-20 2021-04-20
US17/694,467 US20220335294A1 (en) 2021-04-20 2022-03-14 Reporting for machine learning model updates
PCT/US2022/020355 WO2022225627A1 (en) 2021-04-20 2022-03-15 Reporting for machine learning model updates

Publications (1)

Publication Number Publication Date
BR112023019673A2 true BR112023019673A2 (en) 2023-10-31

Family

ID=81326940

Family Applications (1)

Application Number Title Priority Date Filing Date
BR112023019673A BR112023019673A2 (en) 2021-04-20 2022-03-15 REPORTS FOR MACHINE LEARNING MODEL UPDATES

Country Status (4)

Country Link
EP (1) EP4327251A1 (en)
KR (1) KR20230173664A (en)
BR (1) BR112023019673A2 (en)
WO (1) WO2022225627A1 (en)

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2572537A (en) * 2018-03-27 2019-10-09 Nokia Technologies Oy Generating or obtaining an updated neural network
CN111611610B (en) * 2020-04-12 2023-05-30 西安电子科技大学 Federal learning information processing method, system, storage medium, program, and terminal

Also Published As

Publication number Publication date
EP4327251A1 (en) 2024-02-28
WO2022225627A1 (en) 2022-10-27
KR20230173664A (en) 2023-12-27

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