EP3507746A4 - Systems and methods for learning and predicting time-series data using deep multiplicative networks - Google Patents

Systems and methods for learning and predicting time-series data using deep multiplicative networks Download PDF

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Publication number
EP3507746A4
EP3507746A4 EP17847459.9A EP17847459A EP3507746A4 EP 3507746 A4 EP3507746 A4 EP 3507746A4 EP 17847459 A EP17847459 A EP 17847459A EP 3507746 A4 EP3507746 A4 EP 3507746A4
Authority
EP
European Patent Office
Prior art keywords
multiplicative
learning
networks
deep
systems
Prior art date
Legal status (The legal status 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 status listed.)
Withdrawn
Application number
EP17847459.9A
Other languages
German (de)
French (fr)
Other versions
EP3507746A1 (en
Inventor
Paul BURCHARD
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Goldman Sachs and Co LLC
Original Assignee
Goldman Sachs and Co LLC
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 US15/666,379 external-priority patent/US10839316B2/en
Priority claimed from US15/681,942 external-priority patent/US11353833B2/en
Application filed by Goldman Sachs and Co LLC filed Critical Goldman Sachs and Co LLC
Publication of EP3507746A1 publication Critical patent/EP3507746A1/en
Publication of EP3507746A4 publication Critical patent/EP3507746A4/en
Withdrawn legal-status Critical Current

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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
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • G06N3/045—Combinations of networks

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Image Analysis (AREA)
EP17847459.9A 2016-09-01 2017-08-30 Systems and methods for learning and predicting time-series data using deep multiplicative networks Withdrawn EP3507746A4 (en)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201662382774P 2016-09-01 2016-09-01
US15/666,379 US10839316B2 (en) 2016-08-08 2017-08-01 Systems and methods for learning and predicting time-series data using inertial auto-encoders
US15/681,942 US11353833B2 (en) 2016-08-08 2017-08-21 Systems and methods for learning and predicting time-series data using deep multiplicative networks
PCT/US2017/049358 WO2018045021A1 (en) 2016-09-01 2017-08-30 Systems and methods for learning and predicting time-series data using deep multiplicative networks

Publications (2)

Publication Number Publication Date
EP3507746A1 EP3507746A1 (en) 2019-07-10
EP3507746A4 true EP3507746A4 (en) 2020-06-10

Family

ID=61301606

Family Applications (1)

Application Number Title Priority Date Filing Date
EP17847459.9A Withdrawn EP3507746A4 (en) 2016-09-01 2017-08-30 Systems and methods for learning and predicting time-series data using deep multiplicative networks

Country Status (5)

Country Link
EP (1) EP3507746A4 (en)
CN (1) CN109643387A (en)
AU (1) AU2017321524B2 (en)
CA (1) CA3033753A1 (en)
WO (1) WO2018045021A1 (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110175338B (en) * 2019-05-31 2023-09-26 北京金山数字娱乐科技有限公司 Data processing method and device
CN111241688B (en) * 2020-01-15 2023-08-25 北京百度网讯科技有限公司 Method and device for monitoring composite production process
CN111709785B (en) * 2020-06-18 2023-08-22 抖音视界有限公司 Method, apparatus, device and medium for determining user retention time
CN112581031B (en) * 2020-12-30 2023-10-17 杭州朗阳科技有限公司 Method for implementing real-time monitoring of motor abnormality by Recurrent Neural Network (RNN) through C language
CN114024587B (en) * 2021-10-29 2024-07-16 北京邮电大学 Feedback network encoder, architecture and training method based on full-connection layer sharing

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6125105A (en) * 1997-06-05 2000-09-26 Nortel Networks Corporation Method and apparatus for forecasting future values of a time series
US9146546B2 (en) * 2012-06-04 2015-09-29 Brain Corporation Systems and apparatus for implementing task-specific learning using spiking neurons
WO2014203042A1 (en) * 2013-06-21 2014-12-24 Aselsan Elektronik Sanayi Ve Ticaret Anonim Sirketi Method for pseudo-recurrent processing of data using a feedforward neural network architecture
US11080587B2 (en) * 2015-02-06 2021-08-03 Deepmind Technologies Limited Recurrent neural networks for data item generation

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
JUNYOUNG CHUNG ET AL: "Gated Feedback Recurrent Neural Networks", 17 June 2015 (2015-06-17), XP055688073, Retrieved from the Internet <URL:https://arxiv.org/pdf/1502.02367.pdf> [retrieved on 20200421] *
KAISHENG YAO ET AL: "Depth-Gated LSTM", 25 August 2015 (2015-08-25), XP055688413, Retrieved from the Internet <URL:https://arxiv.org/pdf/1508.03790.pdf> [retrieved on 20200422] *
MOEZ BACCOUCHE ET AL: "Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification", PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE, 3 September 2012 (2012-09-03), Surrey, UK, pages 124.1 - 124.12, XP055688925, ISBN: 978-1-901725-46-9, DOI: 10.5244/C.26.124 *
ROHOLLAH SOLTANI ET AL: "Higher Order Recurrent Neural Networks", 30 April 2016 (2016-04-30), XP055688282, Retrieved from the Internet <URL:https://arxiv.org/pdf/1605.00064.pdf> [retrieved on 20200422] *
See also references of WO2018045021A1 *

Also Published As

Publication number Publication date
EP3507746A1 (en) 2019-07-10
CA3033753A1 (en) 2018-03-08
WO2018045021A1 (en) 2018-03-08
CN109643387A (en) 2019-04-16
AU2017321524B2 (en) 2022-03-10
AU2017321524A1 (en) 2019-02-28

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