JP7697824B2 - メタ学習データ拡張フレームワーク - Google Patents

メタ学習データ拡張フレームワーク Download PDF

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JP7697824B2
JP7697824B2 JP2021096983A JP2021096983A JP7697824B2 JP 7697824 B2 JP7697824 B2 JP 7697824B2 JP 2021096983 A JP2021096983 A JP 2021096983A JP 2021096983 A JP2021096983 A JP 2021096983A JP 7697824 B2 JP7697824 B2 JP 7697824B2
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リ，ユーリャン
ワン，シャオラン
ミャオ，ジェンジー
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Recruit Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/217—Database tuning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/284—Lexical analysis, e.g. tokenisation or collocates
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/40—Processing or translation of natural language
    • G06F40/55—Rule-based translation
    • G06F40/56—Natural language generation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
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    • G06N3/084—Backpropagation, e.g. using gradient descent
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    • 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
    • G06N3/0895—Weakly supervised learning, e.g. semi-supervised or self-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
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    • G06N3/0985—Hyperparameter optimisation; Meta-learning; Learning-to-learn
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
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JP2021096983A 2021-04-30 2021-06-10 メタ学習データ拡張フレームワーク Active JP7697824B2 (ja)

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US17/246,354 2021-04-30
US17/246,354 US20220351071A1 (en) 2021-04-30 2021-04-30 Meta-learning data augmentation framework

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JP2022171502A JP2022171502A (ja) 2022-11-11
JP2022171502A5 JP2022171502A5 (https=) 2024-04-04
JP7697824B2 true JP7697824B2 (ja) 2025-06-24

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JP2022098219A (ja) * 2020-12-21 2022-07-01 富士通株式会社 学習プログラム、学習方法、および学習装置
US12423614B2 (en) 2021-05-31 2025-09-23 International Business Machines Corporation Faithful and efficient sample-based model explanations
US20220383096A1 (en) * 2021-05-31 2022-12-01 International Business Machines Corporation Explaining Neural Models by Interpretable Sample-Based Explanations
JP2024098791A (ja) * 2023-01-11 2024-07-24 株式会社東芝 情報処理装置、情報処理方法及び情報処理プログラム
CN116166789B (zh) * 2023-03-23 2025-07-25 中国科学院软件研究所 一种方法命名精准推荐和审查方法

Citations (2)

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Publication number Priority date Publication date Assignee Title
WO2019222734A1 (en) 2018-05-18 2019-11-21 Google Llc Learning data augmentation policies
JP2020187734A (ja) 2019-05-10 2020-11-19 富士通株式会社 遺伝モデルに基づきディープニューラルネットワーク（ｄｎｎ）を訓練することにおけるデータ拡張

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US10769491B2 (en) * 2017-09-01 2020-09-08 Sri International Machine learning system for generating classification data and part localization data for objects depicted in images
US11875120B2 (en) * 2021-02-22 2024-01-16 Robert Bosch Gmbh Augmenting textual data for sentence classification using weakly-supervised multi-reward reinforcement learning

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019222734A1 (en) 2018-05-18 2019-11-21 Google Llc Learning data augmentation policies
JP2020187734A (ja) 2019-05-10 2020-11-19 富士通株式会社 遺伝モデルに基づきディープニューラルネットワーク（ｄｎｎ）を訓練することにおけるデータ拡張

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Chaitra Hegde, 外1名，"Unsupervised Paraphase Generation using Pre-trained Language Models"，[online], [text]，2020年06月09日，[取得日 2025.01.31], 取得先＜https://arxiv.org/pdf/2006.05477＞
Chetanya Rastogi, 外1名，"Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment Classification"，[online], [text]，2020年07月02日，[取得日 2025.01.31], 取得先＜https://arxiv.org/pdf/2007.00875＞
Yuliang Li, 外3名，"Deep Entity Matching with Pre-Trained Language Models"，[online], [text]，2020年09月02日，[取得日 2025.01.31], 取得先＜https://arxiv.org/pdf/2004.00584＞
Zhengjie Miao, 外3名，"Snippext: Semi-supervised Opinion Mining with Augmented Data"，[online], [text]，2020年02月07日，[取得日 2025.01.31], 取得先＜https://arxiv.org/pdf/2002.03049＞

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US20220351071A1 (en) 2022-11-03
JP2022171502A (ja) 2022-11-11

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