KR20170140228A - 바이어스 항을 통한 딥 신경망들에서의 톱-다운 정보의 병합 - Google Patents

바이어스 항을 통한 딥 신경망들에서의 톱-다운 정보의 병합 Download PDF

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KR20170140228A
KR20170140228A KR1020177030865A KR20177030865A KR20170140228A KR 20170140228 A KR20170140228 A KR 20170140228A KR 1020177030865 A KR1020177030865 A KR 1020177030865A KR 20177030865 A KR20177030865 A KR 20177030865A KR 20170140228 A KR20170140228 A KR 20170140228A
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bias
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adjusting
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레건 블라이스 토월
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퀄컴 인코포레이티드
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    • 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
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/048—Activation functions
    • G06N7/005—
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

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KR1020177030865A 2015-04-28 2016-03-11 바이어스 항을 통한 딥 신경망들에서의 톱-다운 정보의 병합 Withdrawn KR20170140228A (ko)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
US201562154097P 2015-04-28 2015-04-28
US62/154,097 2015-04-28
US14/848,288 2015-09-08
US14/848,288 US10325202B2 (en) 2015-04-28 2015-09-08 Incorporating top-down information in deep neural networks via the bias term
PCT/US2016/022158 WO2016175925A1 (en) 2015-04-28 2016-03-11 Incorporating top-down information in deep neural networks via the bias term

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KR20170140228A true KR20170140228A (ko) 2017-12-20

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KR1020177030865A Withdrawn KR20170140228A (ko) 2015-04-28 2016-03-11 바이어스 항을 통한 딥 신경망들에서의 톱-다운 정보의 병합

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US (1) US10325202B2 (enExample)
EP (1) EP3289527A1 (enExample)
JP (1) JP2018518740A (enExample)
KR (1) KR20170140228A (enExample)
CN (1) CN107533665A (enExample)
AU (1) AU2016256315A1 (enExample)
BR (1) BR112017022983A2 (enExample)
WO (1) WO2016175925A1 (enExample)

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US11842280B2 (en) 2017-05-05 2023-12-12 Nvidia Corporation Loss-scaling for deep neural network training with reduced precision
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US10108538B1 (en) 2017-07-31 2018-10-23 Google Llc Accessing prologue and epilogue data
US11144815B2 (en) * 2017-12-04 2021-10-12 Optimum Semiconductor Technologies Inc. System and architecture of neural network accelerator
KR102153791B1 (ko) * 2017-12-20 2020-09-08 연세대학교 산학협력단 인공 신경망을 위한 디지털 뉴런, 인공 뉴런 및 이를 포함하는 추론 엔진
US11531930B2 (en) * 2018-03-12 2022-12-20 Royal Bank Of Canada System and method for monitoring machine learning models
CN108977897B (zh) * 2018-06-07 2021-11-19 浙江天悟智能技术有限公司 基于局部内在可塑性回声状态网络的熔纺工艺控制方法
US20210350236A1 (en) * 2018-09-28 2021-11-11 National Technology & Engineering Solutions Of Sandia, Llc Neural network robustness via binary activation
US11481667B2 (en) * 2019-01-24 2022-10-25 International Business Machines Corporation Classifier confidence as a means for identifying data drift
US20200242771A1 (en) * 2019-01-25 2020-07-30 Nvidia Corporation Semantic image synthesis for generating substantially photorealistic images using neural networks
DE102019217444A1 (de) * 2019-11-12 2021-05-12 Robert Bosch Gmbh Verfahren und Vorrichtung zur Klassifizierung digitaler Bilddaten
US10929748B1 (en) * 2019-11-26 2021-02-23 Mythic, Inc. Systems and methods for implementing operational transformations for restricted computations of a mixed-signal integrated circuit
US12154032B2 (en) * 2020-02-04 2024-11-26 Dsp Group Ltd. Post-training control of the bias of neural networks
KR20210158697A (ko) * 2020-06-24 2021-12-31 삼성전자주식회사 뉴로모픽 장치 및 뉴로모픽 장치를 이용하여 뉴럴 네트워크를 구현하는 방법
US12216740B2 (en) 2021-01-08 2025-02-04 Bank Of America Corporation Data source evaluation platform for improved generation of supervised learning models
CN113473580B (zh) * 2021-05-14 2024-04-26 南京信息工程大学滨江学院 异构网络中基于深度学习的用户关联联合功率分配方法

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JP2000112914A (ja) * 1998-10-09 2000-04-21 Toshiba Corp 神経回路網の学習方法
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WO2010030794A1 (en) 2008-09-10 2010-03-18 Digital Infuzion, Inc. Machine learning methods and systems for identifying patterns in data
US8239336B2 (en) 2009-03-09 2012-08-07 Microsoft Corporation Data processing using restricted boltzmann machines
US8754802B2 (en) 2010-08-26 2014-06-17 Lawrence Livermore National Security, Llc Determining root correspondence between previously and newly detected objects
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CN103778414A (zh) * 2014-01-17 2014-05-07 杭州电子科技大学 基于深度神经网络的实时人脸识别方法
CN104200224A (zh) * 2014-08-28 2014-12-10 西北工业大学 基于深度卷积神经网络的无价值图像去除方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020091259A1 (ko) * 2018-10-29 2020-05-07 에스케이텔레콤 주식회사 비대칭 tanh 활성 함수를 이용한 예측 성능의 개선

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WO2016175925A1 (en) 2016-11-03
US10325202B2 (en) 2019-06-18
US20160321542A1 (en) 2016-11-03
JP2018518740A (ja) 2018-07-12
EP3289527A1 (en) 2018-03-07
AU2016256315A1 (en) 2017-10-05
BR112017022983A2 (pt) 2018-07-24
CN107533665A (zh) 2018-01-02

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Patent event date: 20171025

Patent event code: PA01051R01D

Comment text: International Patent Application

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