CA3168451A1 - Knowledge distillation and gradient pruning-based compression of artificial intelligence-based base caller - Google Patents
Knowledge distillation and gradient pruning-based compression of artificial intelligence-based base caller Download PDFInfo
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- CA3168451A1 CA3168451A1 CA3168451A CA3168451A CA3168451A1 CA 3168451 A1 CA3168451 A1 CA 3168451A1 CA 3168451 A CA3168451 A CA 3168451A CA 3168451 A CA3168451 A CA 3168451A CA 3168451 A1 CA3168451 A1 CA 3168451A1
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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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
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
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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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
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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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
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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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
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- 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
- G06N3/0464—Convolutional networks [CNN, ConvNet]
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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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0475—Generative networks
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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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
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- G—PHYSICS
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- 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
- G06N3/0495—Quantised networks; Sparse networks; Compressed networks
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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
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
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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
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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
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G—PHYSICS
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N3/08—Learning methods
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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]
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
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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
- G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
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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
- 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
- G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
-
- 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
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Databases & Information Systems (AREA)
- Biotechnology (AREA)
- Multimedia (AREA)
- Bioethics (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Neurology (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Signal Processing (AREA)
- Image Analysis (AREA)
- Compression, Expansion, Code Conversion, And Decoders (AREA)
- Apparatus Associated With Microorganisms And Enzymes (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202062979385P | 2020-02-20 | 2020-02-20 | |
| US62/979,385 | 2020-02-20 | ||
| US17/176,151 US12354008B2 (en) | 2020-02-20 | 2021-02-15 | Knowledge distillation and gradient pruning-based compression of artificial intelligence-based base caller |
| US17/176,151 | 2021-02-15 | ||
| PCT/US2021/018422 WO2021168014A1 (en) | 2020-02-20 | 2021-02-17 | Knowledge distillation and gradient pruning-based compression of artificial intelligence-based base caller |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA3168451A1 true CA3168451A1 (en) | 2021-08-26 |
Family
ID=77366220
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA3168451A Pending CA3168451A1 (en) | 2020-02-20 | 2021-02-17 | Knowledge distillation and gradient pruning-based compression of artificial intelligence-based base caller |
Country Status (12)
| Country | Link |
|---|---|
| US (2) | US12354008B2 (https=) |
| EP (1) | EP4107737B1 (https=) |
| JP (1) | JP7754822B2 (https=) |
| KR (1) | KR20220144372A (https=) |
| CN (1) | CN115136245A (https=) |
| AU (1) | AU2021224592A1 (https=) |
| BR (1) | BR112022016409A2 (https=) |
| CA (1) | CA3168451A1 (https=) |
| IL (1) | IL295568B1 (https=) |
| MX (1) | MX2022010275A (https=) |
| MY (1) | MY209788A (https=) |
| WO (1) | WO2021168014A1 (https=) |
Families Citing this family (26)
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| US11210554B2 (en) | 2019-03-21 | 2021-12-28 | Illumina, Inc. | Artificial intelligence-based generation of sequencing metadata |
| CA3168435A1 (en) | 2020-02-20 | 2021-08-26 | Illumina Inc. | Artificial intelligence-based many-to-many base calling |
| WO2021255516A1 (en) * | 2020-06-15 | 2021-12-23 | Universidade Do Porto | Multi-convolutional two-dimensional attention unit for analysis of a multivariable time series three-dimensional input data |
| US12254062B2 (en) * | 2020-08-26 | 2025-03-18 | International Business Machines Corporation | Domain adaptation |
| KR102232138B1 (ko) * | 2020-11-17 | 2021-03-25 | (주)에이아이매틱스 | 지식 증류 기반 신경망 아키텍처 탐색 방법 |
| US20220301657A1 (en) | 2021-03-16 | 2022-09-22 | Illumina, Inc. | Tile location and/or cycle based weight set selection for base calling |
| CN113159173B (zh) * | 2021-04-20 | 2024-04-26 | 北京邮电大学 | 一种结合剪枝与知识蒸馏的卷积神经网络模型压缩方法 |
| US11620993B2 (en) * | 2021-06-09 | 2023-04-04 | Merlyn Mind, Inc. | Multimodal intent entity resolver |
| EP4125003A1 (en) * | 2021-07-16 | 2023-02-01 | Tata Consultancy Services Limited | System and method for learning disentangled representations for temporal causal inference |
| CN114140619B (zh) * | 2021-10-31 | 2024-09-06 | 浪潮电子信息产业股份有限公司 | 图像数据生成方法、模型训练方法、装置、设备及介质 |
| CN113987187B (zh) * | 2021-11-09 | 2024-06-28 | 重庆大学 | 基于多标签嵌入的舆情文本分类方法、系统、终端及介质 |
| CN114170599B (zh) * | 2021-12-14 | 2024-08-23 | 华中科技大学 | 一种基于蒸馏比较的异常物体分割方法 |
| CN114492731B (zh) * | 2021-12-23 | 2024-12-20 | 北京达佳互联信息技术有限公司 | 图像处理模型的训练方法及装置、电子设备 |
| CN114299993B (zh) * | 2021-12-24 | 2024-12-03 | 中北大学 | 一种基于数据蒸馏的多枪声混合智能分类识别系统及方法 |
| CN114780709B (zh) * | 2022-03-22 | 2023-04-07 | 北京三快在线科技有限公司 | 文本匹配方法、装置及电子设备 |
| CN114998648B (zh) * | 2022-05-16 | 2024-07-23 | 电子科技大学 | 一种基于梯度架构搜索的表现预测压缩方法 |
| CN119895436A (zh) * | 2022-08-29 | 2025-04-25 | 蒂普爱可斯有限公司 | 人工神经网络的分布式计算系统及方法 |
| CN116542320B (zh) * | 2023-05-06 | 2025-05-30 | 广东工业大学 | 一种基于持续学习的小样本事件检测方法和系统 |
| CN116451771B (zh) * | 2023-06-14 | 2023-09-15 | 中诚华隆计算机技术有限公司 | 图像分类卷积神经网络压缩方法及芯粒器件数据分配方法 |
| US20250021801A1 (en) * | 2023-07-12 | 2025-01-16 | Canon Medical Systems Corporation | Mapping method and apparatus |
| CN117095217A (zh) * | 2023-08-22 | 2023-11-21 | 电子科技大学 | 多阶段对比知识蒸馏方法 |
| CN117372785B (zh) * | 2023-12-04 | 2024-03-26 | 吉林大学 | 一种基于特征簇中心压缩的图像分类方法 |
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| CN118942549A (zh) * | 2024-04-30 | 2024-11-12 | 深圳市真迈生物科技有限公司 | 碱基识别方法、装置、设备及存储介质 |
| CN118570601B (zh) * | 2024-08-05 | 2024-10-29 | 广东海洋大学 | 一种联合高级视觉任务的红外与可见光图像融合方法 |
| CN120762420B (zh) * | 2025-09-09 | 2025-11-07 | 蜂巢智行(上海)技术有限公司 | 基于vla架构与蒸馏学习的无人物流车驾驶决策方法及系统 |
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