JP2018129033A5 - - Google Patents
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- JP2018129033A5 JP2018129033A5 JP2017231460A JP2017231460A JP2018129033A5 JP 2018129033 A5 JP2018129033 A5 JP 2018129033A5 JP 2017231460 A JP2017231460 A JP 2017231460A JP 2017231460 A JP2017231460 A JP 2017231460A JP 2018129033 A5 JP2018129033 A5 JP 2018129033A5
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- neural network
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- 238000013528 artificial neural network Methods 0.000 claims 16
- 230000004913 activation Effects 0.000 claims 10
- 238000000034 method Methods 0.000 claims 9
- 210000002569 neuron Anatomy 0.000 claims 9
- 238000012545 processing Methods 0.000 claims 2
- 230000003044 adaptive effect Effects 0.000 claims 1
- 238000004364 calculation method Methods 0.000 claims 1
- 239000011159 matrix material Substances 0.000 claims 1
- 238000012544 monitoring process Methods 0.000 claims 1
- 238000007670 refining Methods 0.000 claims 1
- 230000003068 static effect Effects 0.000 claims 1
- 238000012956 testing procedure Methods 0.000 claims 1
- 238000012549 training Methods 0.000 claims 1
- 230000001052 transient effect Effects 0.000 claims 1
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP16205831.7 | 2016-12-21 | ||
| EP16205831.7A EP3340129B1 (en) | 2016-12-21 | 2016-12-21 | Artificial neural network class-based pruning |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2018129033A JP2018129033A (ja) | 2018-08-16 |
| JP2018129033A5 true JP2018129033A5 (enExample) | 2020-05-14 |
| JP6755849B2 JP6755849B2 (ja) | 2020-09-16 |
Family
ID=57583087
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2017231460A Active JP6755849B2 (ja) | 2016-12-21 | 2017-12-01 | 人工ニューラルネットワークのクラスに基づく枝刈り |
Country Status (6)
| Country | Link |
|---|---|
| US (1) | US10552737B2 (enExample) |
| EP (1) | EP3340129B1 (enExample) |
| JP (1) | JP6755849B2 (enExample) |
| KR (1) | KR102110486B1 (enExample) |
| CN (1) | CN108229667B (enExample) |
| TW (1) | TWI698807B (enExample) |
Families Citing this family (31)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108268947A (zh) * | 2016-12-30 | 2018-07-10 | 富士通株式会社 | 用于提高神经网络的处理速度的装置和方法及其应用 |
| US10892050B2 (en) * | 2018-04-13 | 2021-01-12 | International Business Machines Corporation | Deep image classification of medical images |
| KR101989793B1 (ko) | 2018-10-22 | 2019-06-17 | 한국기술교육대학교 산학협력단 | 컨볼루션 신경망을 위한 가속기 인식 가지 치기 방법 및 기록 매체 |
| KR102796861B1 (ko) | 2018-12-10 | 2025-04-17 | 삼성전자주식회사 | 인공 신경망을 압축하기 위한 장치 및 방법 |
| KR102865734B1 (ko) * | 2019-01-02 | 2025-09-26 | 삼성전자주식회사 | 뉴럴 네트워크 최적화 장치 및 뉴럴 네트워크 최적화 방법 |
| US11551093B2 (en) * | 2019-01-22 | 2023-01-10 | Adobe Inc. | Resource-aware training for neural networks |
| KR102214837B1 (ko) * | 2019-01-29 | 2021-02-10 | 주식회사 디퍼아이 | 컨벌루션 신경망 파라미터 최적화 방법, 컨벌루션 신경망 연산방법 및 그 장치 |
| JP7099968B2 (ja) * | 2019-01-31 | 2022-07-12 | 日立Astemo株式会社 | 演算装置 |
| JP7225876B2 (ja) | 2019-02-08 | 2023-02-21 | 富士通株式会社 | 情報処理装置、演算処理装置および情報処理装置の制御方法 |
| DE102019202816A1 (de) * | 2019-03-01 | 2020-09-03 | Robert Bosch Gmbh | Training neuronaler Netzwerke für effizientes Implementieren auf Hardware |
| JP7150651B2 (ja) * | 2019-03-22 | 2022-10-11 | 株式会社日立ソリューションズ・テクノロジー | ニューラルネットワークのモデル縮約装置 |
| KR102368962B1 (ko) | 2019-03-22 | 2022-03-03 | 국민대학교산학협력단 | 멤리스터 어레이 회로를 제어하기 위한 게이트 회로를 포함하는 뉴럴 네트워크 시스템 |
| JP2022095999A (ja) * | 2019-04-19 | 2022-06-29 | 国立大学法人北海道大学 | ニューラル計算装置、および、ニューラル計算方法 |
| KR102782971B1 (ko) | 2019-05-08 | 2025-03-18 | 삼성전자주식회사 | 인공 신경망 모델을 트레이닝하는 컴퓨팅 장치, 인공 신경망 모델을 트레이닝하는 방법 및 이를 저장하는 메모리 시스템 |
| JP6787444B1 (ja) | 2019-05-23 | 2020-11-18 | 沖電気工業株式会社 | ニューラルネットワーク軽量化装置、ニューラルネットワーク軽量化方法およびプログラム |
| CN112070221B (zh) * | 2019-05-31 | 2024-01-16 | 中科寒武纪科技股份有限公司 | 运算方法、装置及相关产品 |
| US11514311B2 (en) * | 2019-07-03 | 2022-11-29 | International Business Machines Corporation | Automated data slicing based on an artificial neural network |
| WO2021040921A1 (en) * | 2019-08-29 | 2021-03-04 | Alibaba Group Holding Limited | Systems and methods for providing vector-wise sparsity in a neural network |
| JP7111671B2 (ja) | 2019-09-05 | 2022-08-02 | 株式会社東芝 | 学習装置、学習システム、および学習方法 |
| DE102019128715A1 (de) * | 2019-10-24 | 2021-04-29 | Krohne Messtechnik Gmbh | Verfahren zur Erzeugung eines neuronalen Netzes für ein Feldgerät zur Vorhersage von Feldgerätfehlern und ein entsprechendes System |
| US11816574B2 (en) * | 2019-10-25 | 2023-11-14 | Alibaba Group Holding Limited | Structured pruning for machine learning model |
| JP7396117B2 (ja) | 2020-02-27 | 2023-12-12 | オムロン株式会社 | モデル更新装置、方法、及びプログラム |
| JP7495833B2 (ja) * | 2020-07-07 | 2024-06-05 | 株式会社日立ソリューションズ・テクノロジー | Ｄｎｎモデル圧縮システム |
| EP3945470A1 (en) * | 2020-07-31 | 2022-02-02 | Aptiv Technologies Limited | Methods and systems for reducing the complexity of a computational network |
| KR20220048832A (ko) | 2020-10-13 | 2022-04-20 | 삼성전자주식회사 | 인공 신경망 프루닝 방법 및 장치 |
| US12340313B2 (en) * | 2020-11-30 | 2025-06-24 | Moffett International Co., Limited | Neural network pruning method and system via layerwise analysis |
| JP7700650B2 (ja) * | 2021-11-25 | 2025-07-01 | 富士通株式会社 | モデル削減プログラム、装置、及び方法 |
| GB202118066D0 (en) | 2021-12-14 | 2022-01-26 | Univ Dublin | Class separation aware artificial neural network pruning method |
| US12061632B2 (en) * | 2022-03-29 | 2024-08-13 | Treasure Data, Inc. | Interactive adaptation of machine learning models for time series data |
| KR20240028055A (ko) * | 2022-08-24 | 2024-03-05 | 동국대학교 산학협력단 | 사이버 공격에 강인한 인공 신경망 구조 재구성 장치 및 방법 |
| US20250190694A1 (en) * | 2023-12-07 | 2025-06-12 | International Business Machines Corporation | Limiting undesired large language model (llm) output |
Family Cites Families (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5787408A (en) * | 1996-08-23 | 1998-07-28 | The United States Of America As Represented By The Secretary Of The Navy | System and method for determining node functionality in artificial neural networks |
| JP2000298661A (ja) * | 1999-04-15 | 2000-10-24 | Fuji Xerox Co Ltd | ニューラルネットワーク装置 |
| US7308418B2 (en) * | 2004-05-24 | 2007-12-11 | Affinova, Inc. | Determining design preferences of a group |
| JP4175296B2 (ja) * | 2004-06-25 | 2008-11-05 | キャタピラージャパン株式会社 | 建設機械のデータ処理装置及び建設機械のデータ処理方法 |
| JP2009516246A (ja) * | 2005-11-15 | 2009-04-16 | ベルナデット ガーナー | ニューラルネットワークのトレーニング方法 |
| US7800490B2 (en) * | 2008-01-09 | 2010-09-21 | Sensormatic Electronics, LLC | Electronic article surveillance system neural network minimizing false alarms and failures to deactivate |
| JP5860054B2 (ja) * | 2010-10-27 | 2016-02-16 | ソリド デザイン オートメーション インコーポレイティド | まれな不良事象率を識別するための方法およびシステム |
| US9147155B2 (en) * | 2011-08-16 | 2015-09-29 | Qualcomm Incorporated | Method and apparatus for neural temporal coding, learning and recognition |
| KR101359352B1 (ko) * | 2012-06-28 | 2014-02-21 | 한국과학기술원 | 시각신경 회로장치 및 이를 이용한 시각신경 모방 시스템 |
| US9406017B2 (en) * | 2012-12-24 | 2016-08-02 | Google Inc. | System and method for addressing overfitting in a neural network |
| CN103209417B (zh) * | 2013-03-05 | 2016-01-20 | 北京邮电大学 | 基于神经网络的频谱占用状态的预测方法以及装置 |
| US9747554B2 (en) * | 2013-05-24 | 2017-08-29 | Qualcomm Incorporated | Learning device with continuous configuration capability |
| US9721204B2 (en) * | 2013-10-28 | 2017-08-01 | Qualcomm Incorporated | Evaluation of a system including separable sub-systems over a multidimensional range |
| US10373054B2 (en) * | 2015-04-19 | 2019-08-06 | International Business Machines Corporation | Annealed dropout training of neural networks |
| CN107545303B (zh) * | 2016-01-20 | 2021-09-07 | 中科寒武纪科技股份有限公司 | 用于稀疏人工神经网络的计算装置和运算方法 |
| US11315018B2 (en) * | 2016-10-21 | 2022-04-26 | Nvidia Corporation | Systems and methods for pruning neural networks for resource efficient inference |
-
2016
- 2016-12-21 EP EP16205831.7A patent/EP3340129B1/en active Active
-
2017
- 2017-10-25 TW TW106136613A patent/TWI698807B/zh active
- 2017-11-28 CN CN201711214867.7A patent/CN108229667B/zh active Active
- 2017-12-01 JP JP2017231460A patent/JP6755849B2/ja active Active
- 2017-12-15 KR KR1020170173396A patent/KR102110486B1/ko active Active
- 2017-12-21 US US15/851,173 patent/US10552737B2/en active Active
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