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WO2022147584A3 - Deep- reinforcement learning (rl), weight-resonant system and method for fixed-horizon search of optimality - Google Patents

Deep- reinforcement learning (rl), weight-resonant system and method for fixed-horizon search of optimality Download PDF

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Publication number
WO2022147584A3
WO2022147584A3 PCT/US2022/026747 US2022026747W WO2022147584A3 WO 2022147584 A3 WO2022147584 A3 WO 2022147584A3 US 2022026747 W US2022026747 W US 2022026747W WO 2022147584 A3 WO2022147584 A3 WO 2022147584A3
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Prior art keywords
neural network
optimality
deep
weight
fixed
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.)
Ceased
Application number
PCT/US2022/026747
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French (fr)
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WO2022147584A2 (en
Inventor
Masood Seyed Mortazavi
Ning Yan
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FutureWei Technologies Inc
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FutureWei Technologies Inc
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Priority to PCT/US2022/026747 priority Critical patent/WO2022147584A2/en
Priority to CN202280093122.2A priority patent/CN118871916A/en
Publication of WO2022147584A2 publication Critical patent/WO2022147584A2/en
Publication of WO2022147584A3 publication Critical patent/WO2022147584A3/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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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
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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
    • 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
    • G06N3/0499—Feedforward networks
    • 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
    • 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
    • G06N3/092—Reinforcement learning
    • 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
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/20—Protein or domain folding
    • 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)
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  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Complex Calculations (AREA)

Abstract

According to embodiments, a neural network running on at least one processor receives a constant input for a configuration design requiring N dimensions. The neural network outputs N probability distributions. The at least one processor generates a batch of sample configurations for the configuration design based on the N probability distributions. Each sample configuration of the batch of sample configurations corresponds to a different full configuration of a system. The at least one processor outputs the batch of sample configurations to an evaluator external to the neural network. The at least one processor updates parameters of the neural network based on a loss function.
PCT/US2022/026747 2022-04-28 2022-04-28 Deep- reinforcement learning (rl), weight-resonant system and method for fixed-horizon search of optimality Ceased WO2022147584A2 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/US2022/026747 WO2022147584A2 (en) 2022-04-28 2022-04-28 Deep- reinforcement learning (rl), weight-resonant system and method for fixed-horizon search of optimality
CN202280093122.2A CN118871916A (en) 2022-04-28 2022-04-28 Deep reinforcement learning (RL) weight resonance system and method for fixed-horizon search for optimality

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2022/026747 WO2022147584A2 (en) 2022-04-28 2022-04-28 Deep- reinforcement learning (rl), weight-resonant system and method for fixed-horizon search of optimality

Publications (2)

Publication Number Publication Date
WO2022147584A2 WO2022147584A2 (en) 2022-07-07
WO2022147584A3 true WO2022147584A3 (en) 2023-02-09

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PCT/US2022/026747 Ceased WO2022147584A2 (en) 2022-04-28 2022-04-28 Deep- reinforcement learning (rl), weight-resonant system and method for fixed-horizon search of optimality

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CN (1) CN118871916A (en)
WO (1) WO2022147584A2 (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113869496A (en) * 2021-09-30 2021-12-31 华为技术有限公司 Acquisition method of neural network, data processing method and related equipment

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021053135A1 (en) * 2019-09-20 2021-03-25 Oslo Universitetssykehus Histological image analysis

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021053135A1 (en) * 2019-09-20 2021-03-25 Oslo Universitetssykehus Histological image analysis

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
NIKLAS W A GEBAUER ET AL: "Generating equilibrium molecules with deep neural networks", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 26 October 2018 (2018-10-26), XP080928276 *

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Publication number Publication date
WO2022147584A2 (en) 2022-07-07
CN118871916A (en) 2024-10-29

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