JP7613588B2 - 学習システムおよび学習方法 - Google Patents

学習システムおよび学習方法 Download PDF

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JP7613588B2
JP7613588B2 JP2023534452A JP2023534452A JP7613588B2 JP 7613588 B2 JP7613588 B2 JP 7613588B2 JP 2023534452 A JP2023534452 A JP 2023534452A JP 2023534452 A JP2023534452 A JP 2023534452A JP 7613588 B2 JP7613588 B2 JP 7613588B2
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智之 吉山
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NEC Corp
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    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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JP2023534452A 2021-07-12 2021-07-12 学習システムおよび学習方法 Active JP7613588B2 (ja)

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PCT/JP2021/026148 WO2023286129A1 (ja) 2021-07-12 2021-07-12 学習システムおよび学習方法

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JPWO2023286129A1 JPWO2023286129A1 (https=) 2023-01-19
JPWO2023286129A5 JPWO2023286129A5 (https=) 2024-04-04
JP7613588B2 true JP7613588B2 (ja) 2025-01-15

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WO2025104771A1 (ja) * 2023-11-13 2025-05-22 日本電気株式会社 情報処理装置、情報処理システム、情報処理方法、及びプログラム

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CHENG, Xin et al.,Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices,arXiv.org, [online],2020年06月13日,[検索日 2024.08.22], Retrieved from the Internet: <URL: https://arxiv.org/pdf/2006.03259.pdf>
HAO, Weituo et al.,WAFFLe: Weight Anonymized Factorization for Federated Learning,arXiv.org, [online],2020年08月13日,[検索日 2024.08.22], Retrieved from the Internet: <URL: https://arxiv.org/pdf/2008.05687.pdf>
YANG, Brandon et al.,CondConv: Conditionally Parameterized Convolutions for Efficient Inference,arXiv.org, [online],2020年09月04日,[検索日 2024.08.22], Retrieved from the Internet: <URL: https://arxiv.org/pdf/1904.04971.pdf>

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