MX2018013242A - Metodo, aparato y programa de computadora para generar sistemas de aprendizaje automatizados, robustos y sistemas de aprendizaje automatizados formados de prueba. - Google Patents
Metodo, aparato y programa de computadora para generar sistemas de aprendizaje automatizados, robustos y sistemas de aprendizaje automatizados formados de prueba.Info
- Publication number
- MX2018013242A MX2018013242A MX2018013242A MX2018013242A MX2018013242A MX 2018013242 A MX2018013242 A MX 2018013242A MX 2018013242 A MX2018013242 A MX 2018013242A MX 2018013242 A MX2018013242 A MX 2018013242A MX 2018013242 A MX2018013242 A MX 2018013242A
- Authority
- MX
- Mexico
- Prior art keywords
- neural network
- learning systems
- automatic learning
- computer program
- output
- Prior art date
Links
- 238000000034 method Methods 0.000 title abstract 2
- 238000004590 computer program Methods 0.000 title 1
- 238000013528 artificial neural network Methods 0.000 abstract 9
- 238000012986 modification Methods 0.000 abstract 1
- 230000004048 modification Effects 0.000 abstract 1
- 230000000644 propagated effect Effects 0.000 abstract 1
Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
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- 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
- G06N3/094—Adversarial learning
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- 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
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- 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
- G06N3/047—Probabilistic or stochastic 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
- G06N3/048—Activation functions
-
- 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
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Automation & Control Theory (AREA)
- Probability & Statistics with Applications (AREA)
- Image Analysis (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Feedback Control In General (AREA)
Abstract
La presente invención pertenece a un método para entrenar la red neural 100, un método para probar la red neuronal 100 así como un método para detectar ejemplos adversos, los cuales pueden engañar a la red neural 100, una clasificación superpuesta es propagada hacia atrás a través de la segunda red neural 500, Maryland el valor de salida de la segunda red neural 500 es utilizado para determinar si la entrada de la red neuronal 100 es un ejemplo adverso; los métodos establecidos de la presente invención se basan en esta utilización de la segunda red neural 500; la presente invención además pertenece a un programa de computadora y un aparato los cuales están configurados para llevar a cabo dichos métodos.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201862677896P | 2018-05-30 | 2018-05-30 | |
US201862736858P | 2018-09-26 | 2018-09-26 |
Publications (1)
Publication Number | Publication Date |
---|---|
MX2018013242A true MX2018013242A (es) | 2019-12-02 |
Family
ID=66589347
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
MX2018013242A MX2018013242A (es) | 2018-05-30 | 2018-10-29 | Metodo, aparato y programa de computadora para generar sistemas de aprendizaje automatizados, robustos y sistemas de aprendizaje automatizados formados de prueba. |
Country Status (9)
Country | Link |
---|---|
US (2) | US11386328B2 (es) |
EP (1) | EP3576021A1 (es) |
KR (1) | KR20190136893A (es) |
CN (1) | CN110554602A (es) |
AU (1) | AU2018256516A1 (es) |
BR (1) | BR102019001258A2 (es) |
CA (1) | CA3022728A1 (es) |
DE (1) | DE102018218586A1 (es) |
MX (1) | MX2018013242A (es) |
Families Citing this family (28)
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US11547052B1 (en) * | 2018-10-10 | 2023-01-10 | Hydro-Gear Limited Partnership | Audible operator feedback for riding lawn mower applications |
US11200438B2 (en) | 2018-12-07 | 2021-12-14 | Dus Operating Inc. | Sequential training method for heterogeneous convolutional neural network |
US11200318B2 (en) * | 2018-12-28 | 2021-12-14 | Mcafee, Llc | Methods and apparatus to detect adversarial malware |
US11625487B2 (en) * | 2019-01-24 | 2023-04-11 | International Business Machines Corporation | Framework for certifying a lower bound on a robustness level of convolutional neural networks |
US11068069B2 (en) * | 2019-02-04 | 2021-07-20 | Dus Operating Inc. | Vehicle control with facial and gesture recognition using a convolutional neural network |
US20210150306A1 (en) * | 2019-11-14 | 2021-05-20 | Qualcomm Incorporated | Phase selective convolution with dynamic weight selection |
CN111027628B (zh) * | 2019-12-12 | 2022-03-11 | 支付宝(杭州)信息技术有限公司 | 一种模型确定方法和系统 |
US10846407B1 (en) | 2020-02-11 | 2020-11-24 | Calypso Ai Corp | Machine learning model robustness characterization |
US11568021B2 (en) | 2020-02-21 | 2023-01-31 | Alibaba Group Holding Limited | Vector-vector multiplication techniques for processing systems |
CN111368886B (zh) * | 2020-02-25 | 2023-03-21 | 华南理工大学 | 一种基于样本筛选的无标注车辆图片分类方法 |
DE102020202870A1 (de) * | 2020-03-06 | 2021-09-09 | Robert Bosch Gesellschaft mit beschränkter Haftung | Verfahren zur Validierung und Auswahl auf maschinellem Lernen basierender Modelle zur Zustandsüberwachung einer Maschine |
CN111401292B (zh) * | 2020-03-25 | 2023-05-26 | 成都东方天呈智能科技有限公司 | 一种融合红外图像训练的人脸识别网络构建方法 |
CN111461307B (zh) * | 2020-04-02 | 2022-04-29 | 武汉大学 | 一种基于生成对抗网络的通用扰动生成方法 |
EP3896613B1 (en) * | 2020-04-14 | 2024-06-05 | Robert Bosch GmbH | Device and method for training a classifier and assessing the robustness of a classifier |
US20210334646A1 (en) * | 2020-04-28 | 2021-10-28 | International Business Machines Corporation | Robustness-aware quantization for neural networks against weight perturbations |
DE102020114339A1 (de) * | 2020-05-28 | 2021-12-02 | Ebm-Papst Mulfingen Gmbh & Co. Kg | Verfahren zum Betreiben eines Ventilatorsystems und Ventilatorsystem mit einem rückwärtsgekrümmten Radialventilator |
CN111709878B (zh) * | 2020-06-17 | 2023-06-23 | 北京百度网讯科技有限公司 | 人脸超分辨率实现方法、装置、电子设备及存储介质 |
DE102020208737A1 (de) | 2020-07-13 | 2022-01-13 | Volkswagen Aktiengesellschaft | Verfahren und Vorrichtung zum Bewerten und Zertifizieren einer Robustheit eines KI-basierten Informationsverarbeitungssystems |
KR102598909B1 (ko) * | 2020-09-03 | 2023-11-06 | 부산대학교 산학협력단 | 적대적 사례에 강인한 심층 신경망 모델을 위한 입력 장치 및 방법 |
US11687619B2 (en) * | 2020-10-02 | 2023-06-27 | Robert Bosch Gmbh | Method and system for an adversarial training using meta-learned initialization |
DE102020213057A1 (de) | 2020-10-15 | 2022-04-21 | Volkswagen Aktiengesellschaft | Verfahren und Vorrichtung zum Überprüfen eines beim teilautomatisierten oder vollautomatisierten Steuern eines Fahrzeugs verwendeten KI-basierten Informationsverarbeitungssystems |
DE102020213058A1 (de) | 2020-10-15 | 2022-04-21 | Volkswagen Aktiengesellschaft | Verfahren und Vorrichtung zum teilautomatisierten oder vollautomatisierten Steuern eines Fahrzeugs |
JP2022085164A (ja) | 2020-11-27 | 2022-06-08 | ロベルト・ボッシュ・ゲゼルシャフト・ミト・ベシュレンクテル・ハフツング | データ処理装置、ニューラルネットワークの深層学習の方法及びプログラム |
US11907334B2 (en) * | 2020-12-08 | 2024-02-20 | International Business Machines Corporation | Neural network negative rule extraction |
EP4083859A1 (en) * | 2021-04-30 | 2022-11-02 | Robert Bosch GmbH | Improved training of classifiers and/or regressors on uncertain training data |
CN114200841B (zh) * | 2021-12-13 | 2023-05-23 | 电子科技大学 | 一种基于模糊反步的网联汽车系统安全控制方法 |
CN115114395B (zh) * | 2022-04-15 | 2024-03-19 | 腾讯科技(深圳)有限公司 | 内容检索及模型训练方法、装置、电子设备和存储介质 |
CN114756214B (zh) * | 2022-06-15 | 2022-08-12 | 中国海洋大学 | 基于OpenCV和插件的图像处理方法、装置 |
Family Cites Families (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4884216A (en) * | 1987-11-09 | 1989-11-28 | Michael Kuperstein | Neural network system for adaptive sensory-motor coordination of multijoint robots for single postures |
US8688616B2 (en) * | 2010-06-14 | 2014-04-01 | Blue Prism Technologies Pte. Ltd. | High-dimensional data analysis |
US8712940B2 (en) * | 2011-05-31 | 2014-04-29 | International Business Machines Corporation | Structural plasticity in spiking neural networks with symmetric dual of an electronic neuron |
CN104392143B (zh) * | 2014-12-09 | 2017-05-17 | 北京四方继保自动化股份有限公司 | 一种自适应量子神经网络汽轮机故障趋势预测方法 |
CN105512725B (zh) * | 2015-12-14 | 2018-08-28 | 杭州朗和科技有限公司 | 一种神经网络的训练方法和设备 |
US11144889B2 (en) * | 2016-04-06 | 2021-10-12 | American International Group, Inc. | Automatic assessment of damage and repair costs in vehicles |
US10810481B2 (en) * | 2017-01-11 | 2020-10-20 | Thomas Danaher Harvey | Method and system to count movements of persons from vibrations in a floor |
DE102018200724A1 (de) | 2017-04-19 | 2018-10-25 | Robert Bosch Gmbh | Verfahren und Vorrichtung zum Verbessern der Robustheit gegen "Adversarial Examples" |
WO2019227294A1 (zh) * | 2018-05-28 | 2019-12-05 | 华为技术有限公司 | 图像处理方法、相关设备及计算机存储介质 |
DE102018208763A1 (de) | 2018-06-04 | 2019-12-05 | Robert Bosch Gmbh | Verfahren, Vorrichtung und Computerprogramm zum Betreiben eines maschinellen Lernsystems |
-
2018
- 2018-10-29 US US16/173,698 patent/US11386328B2/en active Active
- 2018-10-29 US US16/173,126 patent/US11676025B2/en active Active
- 2018-10-29 MX MX2018013242A patent/MX2018013242A/es unknown
- 2018-10-29 KR KR1020180130138A patent/KR20190136893A/ko unknown
- 2018-10-29 CN CN201811268424.0A patent/CN110554602A/zh active Pending
- 2018-10-30 CA CA3022728A patent/CA3022728A1/en active Pending
- 2018-10-30 AU AU2018256516A patent/AU2018256516A1/en not_active Abandoned
- 2018-10-30 DE DE102018218586.7A patent/DE102018218586A1/de active Pending
-
2019
- 2019-01-22 BR BR102019001258A patent/BR102019001258A2/pt not_active IP Right Cessation
- 2019-05-16 EP EP19174931.6A patent/EP3576021A1/en active Pending
Also Published As
Publication number | Publication date |
---|---|
CN110554602A (zh) | 2019-12-10 |
DE102018218586A1 (de) | 2020-01-09 |
US11676025B2 (en) | 2023-06-13 |
US11386328B2 (en) | 2022-07-12 |
US20200026996A1 (en) | 2020-01-23 |
AU2018256516A1 (en) | 2019-12-19 |
CA3022728A1 (en) | 2019-11-30 |
KR20190136893A (ko) | 2019-12-10 |
US20190370660A1 (en) | 2019-12-05 |
BR102019001258A2 (pt) | 2019-12-03 |
EP3576021A1 (en) | 2019-12-04 |
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