DK3356999T3 - System til anvendelse af et konvolutionsnetværk til rumlige data - Google Patents
System til anvendelse af et konvolutionsnetværk til rumlige data Download PDFInfo
- Publication number
- DK3356999T3 DK3356999T3 DK16854180.3T DK16854180T DK3356999T3 DK 3356999 T3 DK3356999 T3 DK 3356999T3 DK 16854180 T DK16854180 T DK 16854180T DK 3356999 T3 DK3356999 T3 DK 3356999T3
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- Prior art keywords
- space data
- convolution network
- convolution
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Classifications
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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
- G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
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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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
- G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
- G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
- G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/50—Molecular design, e.g. of drugs
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
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- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Medical Informatics (AREA)
- Multimedia (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Biomedical Technology (AREA)
- General Engineering & Computer Science (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Databases & Information Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Chemical & Material Sciences (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Physiology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Evolutionary Biology (AREA)
- Crystallography & Structural Chemistry (AREA)
- Medicinal Chemistry (AREA)
- Pharmacology & Pharmacy (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201562236962P | 2015-10-04 | 2015-10-04 | |
US15/050,983 US9373059B1 (en) | 2014-05-05 | 2016-02-23 | Systems and methods for applying a convolutional network to spatial data |
US15/187,018 US10002312B2 (en) | 2014-05-05 | 2016-06-20 | Systems and methods for applying a convolutional network to spatial data |
PCT/US2016/055369 WO2017062382A1 (en) | 2015-10-04 | 2016-10-04 | Systems and methods for applying a convolutional network to spatial data |
Publications (1)
Publication Number | Publication Date |
---|---|
DK3356999T3 true DK3356999T3 (da) | 2020-02-03 |
Family
ID=58488391
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
DK16854180.3T DK3356999T3 (da) | 2015-10-04 | 2016-10-04 | System til anvendelse af et konvolutionsnetværk til rumlige data |
Country Status (8)
Country | Link |
---|---|
EP (2) | EP3356999B1 (da) |
JP (1) | JP6975140B2 (da) |
CN (1) | CN108140131B (da) |
DK (1) | DK3356999T3 (da) |
ES (1) | ES2772687T3 (da) |
HU (1) | HUE047807T2 (da) |
SG (1) | SG11201802759YA (da) |
WO (1) | WO2017062382A1 (da) |
Families Citing this family (26)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10795836B2 (en) | 2017-04-17 | 2020-10-06 | Microsoft Technology Licensing, Llc | Data processing performance enhancement for neural networks using a virtualized data iterator |
US11521712B2 (en) | 2017-05-19 | 2022-12-06 | Accutar Biotechnology Inc. | Computational method for classifying and predicting ligand docking conformations |
KR102601604B1 (ko) * | 2017-08-04 | 2023-11-13 | 삼성전자주식회사 | 뉴럴 네트워크의 파라미터들을 양자화하는 방법 및 장치 |
CA3098321A1 (en) | 2018-06-01 | 2019-12-05 | Grail, Inc. | Convolutional neural network systems and methods for data classification |
CN109033505A (zh) * | 2018-06-06 | 2018-12-18 | 东北大学 | 一种基于深度学习的超快冷温度控制方法 |
CN108880568A (zh) * | 2018-07-06 | 2018-11-23 | 中国石油大学(华东) | 一种基于卷积神经网络的串行抵消列表译码参数优化方法 |
EP3821434B1 (en) * | 2018-09-21 | 2024-07-24 | DeepMind Technologies Limited | Machine learning for determining protein structures |
US11455439B2 (en) * | 2018-11-28 | 2022-09-27 | Robert Bosch Gmbh | Neural network force field computational algorithms for molecular dynamics computer simulations |
CN109800231B (zh) * | 2019-01-17 | 2020-12-08 | 浙江大学 | 一种基于Flink的实时轨迹co-movement运动模式检测方法 |
WO2020170101A1 (en) * | 2019-02-19 | 2020-08-27 | King Abdullah University Of Science And Technology | Reduced feature generation for signal classification based on a position weight matrix |
CN109959911A (zh) * | 2019-03-25 | 2019-07-02 | 清华大学 | 基于激光雷达的多目标自主定位方法及装置 |
CA3138861A1 (en) * | 2019-05-02 | 2020-12-10 | Board Of Regents, The University Of Texas System | System and method for increasing synthesized protein stability |
CN110265092B (zh) * | 2019-05-10 | 2021-08-24 | 江苏理工学院 | 基于人工智能的抗体-抗原分子对接评价方法和*** |
CN110163369B (zh) * | 2019-05-21 | 2021-05-11 | 北京迈格威科技有限公司 | 图像识别与神经网络模型的训练方法、装置和*** |
CN110600125B (zh) * | 2019-09-18 | 2022-05-24 | 山东浪潮科学研究院有限公司 | 一种基于人工智能的体态分析助手***及传输方法 |
CN110675488B (zh) * | 2019-09-24 | 2023-02-28 | 电子科技大学 | 基于深度学习的创意三维体素模型的建模***的构建方法 |
CN110995277B (zh) * | 2019-12-06 | 2021-06-01 | 浙江大学 | 一种多层神经网络辅助的罚对偶分解信道译码方法 |
CN112086145B (zh) * | 2020-09-02 | 2024-04-16 | 腾讯科技(深圳)有限公司 | 一种化合物活性预测方法、装置、电子设备和存储介质 |
CN112488309B (zh) * | 2020-12-21 | 2023-10-20 | 清华大学深圳国际研究生院 | 基于临界阻尼动量的深度神经网络的训练方法及*** |
CN112668536B (zh) * | 2021-01-06 | 2023-08-25 | 北京理工大学 | 一种基于机载光电视频的轻量化旋转目标检测识别方法 |
CN114822717A (zh) * | 2021-01-28 | 2022-07-29 | 腾讯科技(深圳)有限公司 | 基于人工智能的药物分子处理方法、装置、设备及存储介质 |
CN115083537A (zh) * | 2021-03-10 | 2022-09-20 | 腾讯科技(深圳)有限公司 | 分子骨架跃迁的处理方法、装置、介质及电子设备 |
CN113066539A (zh) * | 2021-03-22 | 2021-07-02 | 上海商汤智能科技有限公司 | 预测方法及相关装置、设备 |
CN113721293B (zh) * | 2021-08-30 | 2022-07-01 | 电子科技大学 | 一种基于深度学习的多波地震信号人工智能匹配方法 |
WO2023055949A1 (en) * | 2021-10-01 | 2023-04-06 | Atomwise Inc. | Characterization of interactions between compounds and polymers using negative pose data and model conditioning |
CN114708931B (zh) * | 2022-04-22 | 2023-01-24 | 中国海洋大学 | 结合机器学习和构象计算提高药-靶活性预测精度的方法 |
Family Cites Families (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6081766A (en) * | 1993-05-21 | 2000-06-27 | Axys Pharmaceuticals, Inc. | Machine-learning approach to modeling biological activity for molecular design and to modeling other characteristics |
US7236615B2 (en) * | 2004-04-21 | 2007-06-26 | Nec Laboratories America, Inc. | Synergistic face detection and pose estimation with energy-based models |
EP2031528A4 (en) * | 2006-05-26 | 2009-06-17 | Univ Kyoto | ESTIMATING A PROTEIN-COMPOUND INTERACTION AND RATIONAL DESIGN OF A LIBRARY OF COMPOUNDS BASED ON CHEMICAL GENOMIC INFORMATION |
CN102222178B (zh) * | 2011-03-31 | 2014-12-10 | 清华大学深圳研究生院 | 一种筛选和/或设计针对多靶标的药物的方法 |
CN102663214B (zh) * | 2012-05-09 | 2013-11-06 | 四川大学 | 一种集成药物靶标预测***的构建和预测方法 |
JP5905781B2 (ja) * | 2012-06-13 | 2016-04-20 | 学校法人沖縄科学技術大学院大学学園 | 相互作用予測装置、相互作用予測方法、および、プログラム |
CN102930152B (zh) * | 2012-10-26 | 2016-08-03 | 中国科学院上海药物研究所 | 一种模拟配体分子与靶标受体反应并计算预测该反应的热力学与动力学参数的方法和*** |
CN102930181B (zh) * | 2012-11-07 | 2015-05-27 | 四川大学 | 基于分子描述符的蛋白质-配体亲和力预测方法 |
US9668699B2 (en) * | 2013-10-17 | 2017-06-06 | Siemens Healthcare Gmbh | Method and system for anatomical object detection using marginal space deep neural networks |
US20150278441A1 (en) * | 2014-03-25 | 2015-10-01 | Nec Laboratories America, Inc. | High-order semi-Restricted Boltzmann Machines and Deep Models for accurate peptide-MHC binding prediction |
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2016
- 2016-10-04 CN CN201680057614.0A patent/CN108140131B/zh active Active
- 2016-10-04 EP EP16854180.3A patent/EP3356999B1/en active Active
- 2016-10-04 HU HUE16854180A patent/HUE047807T2/hu unknown
- 2016-10-04 SG SG11201802759YA patent/SG11201802759YA/en unknown
- 2016-10-04 WO PCT/US2016/055369 patent/WO2017062382A1/en active Application Filing
- 2016-10-04 JP JP2018516040A patent/JP6975140B2/ja active Active
- 2016-10-04 EP EP19211265.4A patent/EP3680820B1/en active Active
- 2016-10-04 DK DK16854180.3T patent/DK3356999T3/da active
- 2016-10-04 ES ES16854180T patent/ES2772687T3/es active Active
Also Published As
Publication number | Publication date |
---|---|
SG11201802759YA (en) | 2018-05-30 |
CN108140131B (zh) | 2021-09-14 |
CN108140131A (zh) | 2018-06-08 |
HUE047807T2 (hu) | 2020-05-28 |
JP2019501433A (ja) | 2019-01-17 |
ES2772687T3 (es) | 2020-07-08 |
EP3356999B1 (en) | 2019-11-27 |
EP3680820A1 (en) | 2020-07-15 |
EP3680820B1 (en) | 2021-12-01 |
EP3356999A1 (en) | 2018-08-08 |
JP6975140B2 (ja) | 2021-12-01 |
EP3356999A4 (en) | 2019-07-03 |
WO2017062382A1 (en) | 2017-04-13 |
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