SG11201911777QA - Deep learning-based techniques for pre-training deep convolutional neural networks - Google Patents

Deep learning-based techniques for pre-training deep convolutional neural networks

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Publication number
SG11201911777QA
SG11201911777QA SG11201911777QA SG11201911777QA SG11201911777QA SG 11201911777Q A SG11201911777Q A SG 11201911777QA SG 11201911777Q A SG11201911777Q A SG 11201911777QA SG 11201911777Q A SG11201911777Q A SG 11201911777QA SG 11201911777Q A SG11201911777Q A SG 11201911777QA
Authority
SG
Singapore
Prior art keywords
convolutional neural
neural networks
based techniques
training
deep
Prior art date
Application number
SG11201911777QA
Other languages
English (en)
Inventor
Kai-How FARH
Hong Gao
Padigepati Samskruthi Reddy
Original Assignee
Illumina Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US16/160,903 external-priority patent/US10423861B2/en
Priority claimed from US16/407,149 external-priority patent/US10540591B2/en
Application filed by Illumina Inc filed Critical Illumina Inc
Publication of SG11201911777QA publication Critical patent/SG11201911777QA/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • 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
    • 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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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
    • 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
    • G06V10/82—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
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble 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
    • G06N3/044—Recurrent networks, e.g. Hopfield 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Molecular Biology (AREA)
  • General Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Computing Systems (AREA)
  • Biotechnology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Chemical & Material Sciences (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Databases & Information Systems (AREA)
  • Genetics & Genomics (AREA)
  • Bioethics (AREA)
  • Multimedia (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Epidemiology (AREA)
  • Public Health (AREA)
  • Image Analysis (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Machine Translation (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
SG11201911777QA 2018-10-15 2019-05-09 Deep learning-based techniques for pre-training deep convolutional neural networks SG11201911777QA (en)

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
US16/160,903 US10423861B2 (en) 2017-10-16 2018-10-15 Deep learning-based techniques for training deep convolutional neural networks
PCT/US2018/055840 WO2019079166A1 (en) 2017-10-16 2018-10-15 TECHNIQUES BASED ON DEEP LEARNING LEARNING OF NEURONAL NETWORKS WITH DEEP CONVOLUTION
PCT/US2018/055881 WO2019079182A1 (en) 2017-10-16 2018-10-15 SEMI-SUPERVISED APPRENTICESHIP FOR THE LEARNING OF A SET OF NEURONAL NETWORKS WITH DEEP CONVOLUTION
PCT/US2018/055878 WO2019079180A1 (en) 2017-10-16 2018-10-15 NEURONAL NETWORKS WITH DEEP CONVOLUTION OF VARIANT CLASSIFICATION
US16/160,986 US11315016B2 (en) 2017-10-16 2018-10-15 Deep convolutional neural networks for variant classification
US16/160,968 US11798650B2 (en) 2017-10-16 2018-10-15 Semi-supervised learning for training an ensemble of deep convolutional neural networks
US16/407,149 US10540591B2 (en) 2017-10-16 2019-05-08 Deep learning-based techniques for pre-training deep convolutional neural networks
PCT/US2019/031621 WO2020081122A1 (en) 2018-10-15 2019-05-09 Deep learning-based techniques for pre-training deep convolutional neural networks

Publications (1)

Publication Number Publication Date
SG11201911777QA true SG11201911777QA (en) 2020-05-28

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SG11201911777QA SG11201911777QA (en) 2018-10-15 2019-05-09 Deep learning-based techniques for pre-training deep convolutional neural networks
SG10202108013QA SG10202108013QA (en) 2018-10-15 2019-05-09 Deep learning-based techniques for pre-training deep convolutional neural networks

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SG10202108013QA SG10202108013QA (en) 2018-10-15 2019-05-09 Deep learning-based techniques for pre-training deep convolutional neural networks

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JP (3) JP6888123B2 (he)
KR (1) KR102165734B1 (he)
CN (2) CN111328419B (he)
AU (2) AU2019272062B2 (he)
IL (2) IL271091B (he)
NZ (1) NZ759665A (he)
SG (2) SG11201911777QA (he)
WO (1) WO2020081122A1 (he)

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CN111830408B (zh) * 2020-06-23 2023-04-18 朗斯顿科技(北京)有限公司 一种基于边缘计算和深度学习的电机故障诊断系统及方法
CN112003735B (zh) * 2020-07-28 2021-11-09 四川大学 一种感知风险的深度学习驱动的极限传输容量调整方法
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KR102279056B1 (ko) * 2021-01-19 2021-07-19 주식회사 쓰리빌리언 지식전이를 이용한 유전자변이의 병원성 예측 시스템
CN113299345B (zh) * 2021-06-30 2024-05-07 中国人民解放军军事科学院军事医学研究院 病毒基因分类的方法、装置及电子设备
CN113539354B (zh) * 2021-07-19 2023-10-27 浙江理工大学 一种高效预测革兰氏阴性菌ⅲ型和ⅳ型效应蛋白的方法
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Also Published As

Publication number Publication date
JP2021501923A (ja) 2021-01-21
CN113705585A (zh) 2021-11-26
AU2021269351A1 (en) 2021-12-09
AU2019272062B2 (en) 2021-08-19
JP2021152907A (ja) 2021-09-30
JP7200294B2 (ja) 2023-01-06
AU2021269351B2 (en) 2023-12-14
WO2020081122A1 (en) 2020-04-23
IL271091B (he) 2021-05-31
JP6888123B2 (ja) 2021-06-16
KR20200044731A (ko) 2020-04-29
KR102165734B1 (ko) 2020-10-14
NZ759665A (en) 2022-07-01
CN111328419B (zh) 2021-10-19
IL271091A (he) 2020-04-30
JP2023052011A (ja) 2023-04-11
AU2019272062A1 (en) 2020-04-30
SG10202108013QA (en) 2021-09-29
IL282689A (he) 2021-06-30
CN111328419A (zh) 2020-06-23

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