PL3396623T3 - Uczenie głębokie zależne od kontekstu w czasie rzeczywistym - Google Patents
Uczenie głębokie zależne od kontekstu w czasie rzeczywistymInfo
- 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
Links
Classifications
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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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
-
- 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]
-
- 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
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- 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
- G06N3/09—Supervised learning
-
- 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
- G06N3/098—Distributed learning, e.g. federated learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/20—Processor architectures; Processor configuration, e.g. pipelining
-
- Y—GENERAL 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy 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)
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)
| 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)
| 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 |
-
2017
- 2017-04-24 US US15/494,887 patent/US11238338B2/en active Active
-
2018
- 2018-03-19 EP EP25182501.4A patent/EP4592939A3/en active Pending
- 2018-03-19 EP EP18162611.0A patent/EP3396623B1/en active Active
- 2018-03-19 ES ES18162611T patent/ES3056687T3/es active Active
- 2018-03-19 PL PL18162611.0T patent/PL3396623T3/pl unknown
- 2018-04-23 CN CN202510764825.9A patent/CN120671750A/zh active Pending
- 2018-04-23 CN CN201810366629.6A patent/CN108734284A/zh active Pending
-
2021
- 2021-08-17 US US17/404,153 patent/US11704564B2/en active Active
-
2023
- 2023-05-30 US US18/325,744 patent/US12223427B2/en active Active
-
2024
- 2024-12-27 US US19/003,966 patent/US20250322233A1/en active Pending
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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