PL3396623T3 - Uczenie głębokie zależne od kontekstu w czasie rzeczywistym - Google Patents

Uczenie głębokie zależne od kontekstu w czasie rzeczywistym

Info

Publication number
PL3396623T3
PL3396623T3 PL18162611.0T PL18162611T PL3396623T3 PL 3396623 T3 PL3396623 T3 PL 3396623T3 PL 18162611 T PL18162611 T PL 18162611T PL 3396623 T3 PL3396623 T3 PL 3396623T3
Authority
PL
Poland
Prior art keywords
real time
deep learning
context dependent
time context
dependent deep
Prior art date
Application number
PL18162611.0T
Other languages
English (en)
Inventor
Lev Faivishevsky
Tomer BAR-ON
Yaniv Fais
Jacob Subag
Jeremie Dreyfuss
Amit Bleiweiss
Tomer Schwartz
Raanan Yonatan YEHEZKEL ROHEKAR
Michael Behar
Amitai Armon
Uzi Sarel
Original Assignee
Intel Corporation
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
Application filed by Intel Corporation filed Critical Intel Corporation
Publication of PL3396623T3 publication Critical patent/PL3396623T3/pl

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Classifications

    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00Machine learning
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00Machine learning
    • G06N20/10Machine learning using kernel methods, e.g. support vector machines [SVM]
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/044Recurrent networks, e.g. Hopfield networks
    • G06N3/0442Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/0464Convolutional networks [CNN, ConvNet]
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/09Supervised learning
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/098Distributed learning, e.g. federated learning
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00General purpose image data processing
    • G06T1/20Processor architectures; Processor configuration, e.g. pipelining
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02DCLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00Energy efficient computing, e.g. low power processors, power management or thermal management

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Neurology (AREA)
  • Image Processing (AREA)
  • Image Generation (AREA)
PL18162611.0T 2017-04-24 2018-03-19 Uczenie głębokie zależne od kontekstu w czasie rzeczywistym PL3396623T3 (pl)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US15/494,887 US11238338B2 (en) 2017-04-24 2017-04-24 Real time context dependent deep learning

Publications (1)

Publication Number Publication Date
PL3396623T3 true PL3396623T3 (pl) 2026-03-09

Family

ID=61873277

Family Applications (1)

Application Number Title Priority Date Filing Date
PL18162611.0T PL3396623T3 (pl) 2017-04-24 2018-03-19 Uczenie głębokie zależne od kontekstu w czasie rzeczywistym

Country Status (5)

Country Link
US (4) US11238338B2 (pl)
EP (2) EP4592939A3 (pl)
CN (2) CN120671750A (pl)
ES (1) ES3056687T3 (pl)
PL (1) PL3396623T3 (pl)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11238338B2 (en) 2017-04-24 2022-02-01 Intel Corporation Real time context dependent deep learning
CN109583328B (zh) * 2018-11-13 2021-09-03 东南大学 一种嵌入稀疏连接的深度卷积神经网络字符识别方法
CN113383346A (zh) 2018-12-18 2021-09-10 莫维迪厄斯有限公司 神经网络压缩
US11695619B2 (en) * 2019-02-07 2023-07-04 T-Mobile Usa, Inc. System and method for root cause analysis of call failures in a communication network
US11586895B1 (en) * 2019-06-17 2023-02-21 Green Mountain Semiconductor, Inc. Recursive neural network using random access memory
US12430464B2 (en) * 2023-09-07 2025-09-30 International Business Machines Corporation Data leakage protection using generative large language models

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7873812B1 (en) 2004-04-05 2011-01-18 Tibet MIMAR Method and system for efficient matrix multiplication in a SIMD processor architecture
WO2012141677A1 (en) 2011-04-11 2012-10-18 Hewlett-Packard Development Company, L.P. Performing a task in a system having different types of hardware resources
US9477925B2 (en) * 2012-11-20 2016-10-25 Microsoft Technology Licensing, Llc Deep neural networks training for speech and pattern recognition
US20160026912A1 (en) * 2014-07-22 2016-01-28 Intel Corporation Weight-shifting mechanism for convolutional neural networks
US10223333B2 (en) 2014-08-29 2019-03-05 Nvidia Corporation Performing multi-convolution operations in a parallel processing system
US10803381B2 (en) * 2014-09-09 2020-10-13 Intel Corporation Fixed point integer implementations for neural networks
US9940575B2 (en) * 2015-06-04 2018-04-10 Yahoo Holdings, Inc. Image searching
US10002402B2 (en) * 2015-07-23 2018-06-19 Sony Corporation Learning convolution neural networks on heterogeneous CPU-GPU platform
CN105159840B (zh) * 2015-10-16 2018-11-02 华中科技大学 一种闪存器件的软信息提取方法
CN106293942A (zh) * 2016-08-10 2017-01-04 中国科学技术大学苏州研究院 基于多机多卡的神经网络负载均衡优化方法和系统
US10891538B2 (en) 2016-08-11 2021-01-12 Nvidia Corporation Sparse convolutional neural network accelerator
US10997496B2 (en) 2016-08-11 2021-05-04 Nvidia Corporation Sparse convolutional neural network accelerator
GB201620235D0 (en) * 2016-11-29 2017-01-11 Microsoft Technology Licensing Llc Neural network data entry system
US11238338B2 (en) 2017-04-24 2022-02-01 Intel Corporation Real time context dependent deep learning

Also Published As

Publication number Publication date
US20230394305A1 (en) 2023-12-07
EP3396623B1 (en) 2025-10-01
CN108734284A (zh) 2018-11-02
US20250322233A1 (en) 2025-10-16
EP4592939A3 (en) 2025-11-05
EP3396623A1 (en) 2018-10-31
EP4592939A2 (en) 2025-07-30
US12223427B2 (en) 2025-02-11
ES3056687T3 (en) 2026-02-24
US11238338B2 (en) 2022-02-01
US20180307982A1 (en) 2018-10-25
US11704564B2 (en) 2023-07-18
CN120671750A (zh) 2025-09-19
US20220076118A1 (en) 2022-03-10

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