CN110268422B - 利用强化学习的设备布局优化 - Google Patents

利用强化学习的设备布局优化 Download PDF

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CN110268422B
CN110268422B CN201880011282.1A CN201880011282A CN110268422B CN 110268422 B CN110268422 B CN 110268422B CN 201880011282 A CN201880011282 A CN 201880011282A CN 110268422 B CN110268422 B CN 110268422B
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layout
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sequence
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CN110268422A (zh
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S.本吉奥
M.诺鲁齐
B.斯坦纳
J.A.迪安
H.H.范
A.米霍塞尼
Q.V.勒
N.库马
Y.周
R.M.拉森
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    • 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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CN201880011282.1A 2017-03-24 2018-03-23 利用强化学习的设备布局优化 Active CN110268422B (zh)

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US201762476618P 2017-03-24 2017-03-24
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PCT/US2018/024155 WO2018175972A1 (en) 2017-03-24 2018-03-23 Device placement optimization with reinforcement learning

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US20240062062A1 (en) 2024-02-22
CN110268422A (zh) 2019-09-20
US20200279163A1 (en) 2020-09-03
EP4553715A3 (en) 2025-08-13
US10692003B2 (en) 2020-06-23
EP3559868A1 (en) 2019-10-30
EP4553715A2 (en) 2025-05-14
KR20190113928A (ko) 2019-10-08
JP2020512639A (ja) 2020-04-23
CN117648971A (zh) 2024-03-05
EP3559868B1 (en) 2025-05-07
US11803747B2 (en) 2023-10-31
US20190303761A1 (en) 2019-10-03
KR102208989B1 (ko) 2021-01-28
JP6790286B2 (ja) 2020-11-25
WO2018175972A1 (en) 2018-09-27

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