CN111919226B - 执行机器学习的装置和方法 - Google Patents

执行机器学习的装置和方法 Download PDF

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CN111919226B
CN111919226B CN201980022623.XA CN201980022623A CN111919226B CN 111919226 B CN111919226 B CN 111919226B CN 201980022623 A CN201980022623 A CN 201980022623A CN 111919226 B CN111919226 B CN 111919226B
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CN111919226A (zh
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韩亮
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Pingtouge Shanghai Semiconductor Co Ltd
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
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    • G06N3/044—Recurrent networks, e.g. Hopfield networks
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    • 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
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
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    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
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    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/048—Activation functions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
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    • G06N3/0495—Quantised networks; Sparse networks; Compressed networks
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    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
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    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means

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CN201980022623.XA 2018-04-27 2019-04-26 执行机器学习的装置和方法 Active CN111919226B (zh)

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US201862663955P 2018-04-27 2018-04-27
US62/663,955 2018-04-27
PCT/US2019/029450 WO2019210237A1 (en) 2018-04-27 2019-04-26 Method and system for performing machine learning

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CN111919226B true CN111919226B (zh) 2025-01-07

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CN111679959B (zh) * 2020-04-23 2025-04-01 平安科技（深圳）有限公司 计算机性能数据确定方法、装置、计算机设备及存储介质
US20210375441A1 (en) * 2020-05-29 2021-12-02 Regents Of The University Of Minnesota Using clinical notes for icu management
JP7537734B2 (ja) * 2020-08-24 2024-08-21 アシオット株式会社 情報処理装置、及びプログラム
DE102020124174A1 (de) * 2020-09-16 2022-03-17 Elektronische Fahrwerksysteme GmbH Verfahren zur Bereitstellung einer maschinell gelernten Steuerfunktion zur Fahrzeugsteuerung anhand bereitgestellter Fahrzeugsensordaten
WO2022168104A1 (en) * 2021-02-04 2022-08-11 Telefonaktiebolaget Lm Ericsson (Publ) Building an explainable machine learning model
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EP4399644A1 (en) * 2021-10-06 2024-07-17 Google LLC Exploring heterogeneous characteristics of layers in asr models for more efficient training
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WO2019210237A1 (en) 2019-10-31
EP3785179A4 (en) 2021-12-29
US20190332940A1 (en) 2019-10-31
US11308395B2 (en) 2022-04-19
CN111919226A (zh) 2020-11-10
EP3785179A1 (en) 2021-03-03
JP7162074B2 (ja) 2022-10-27
JP2021522574A (ja) 2021-08-30

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