Adaptable Learning 적응 학습

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adaptable learning rate 10.1016/j.ymssp.2020.107170

적응 학습률

A method for quantitative sound quality prediction of the PEV nonstationary interior noise based on tacho-tracking psychoacoustic metrics and deep CNNs with adaptable learning rate trees (ALRT-CNNs) is presented to solve the aforementioned problems.

앞서 언급한 문제를 해결하기 위해 타코 추적 심리음향 메트릭과 ALRT-CNN(adaptable learning rate tree)이 있는 심층 CNN을 기반으로 하는 PEV 비정상 내부 소음의 정량적 음질 예측 방법이 제시됩니다.

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adaptable learning rate 10.3390/APP11041442

적응 학습률

Afterward, an 18-layer Residual Network (ResNet18), which contains the center loss function with the embedding layer, is designed to train the aggregated features with an adaptable learning rate.

그 후, 임베딩 레이어가 있는 중심 손실 함수를 포함하는 18레이어 ResNet18(Residual Network)이 적응 가능한 학습률로 집계된 기능을 훈련하도록 설계되었습니다.

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10.1108/jwl-09-2020-0152

Originality/value This paper empirically demonstrates the nexus of work and learning as experienced by professionals in their day-to-day work, as well as the ways in which fluid work influences flexible and adaptable learning through participation in work.

독창성/가치 이 논문은 일상 업무에서 전문가들이 경험한 바와 같이 업무와 학습의 연관성과 유동적인 업무가 업무 참여를 통해 유연하고 적응 가능한 학습에 영향을 미치는 방식을 경험적으로 보여줍니다.

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10.1109/EDUCON.2019.8725075

It was created with four main objectives: 1) explore new communication and information technologies that promote flexible, adaptable learning; 2) help students detect opportunities for improvement in their learning and skills; 3) motivate the students to commit themselves to their self-learning; and, 4) explore different support tools that help the students reflect on their learning process.

4가지 주요 목표로 만들어졌습니다. 1) 유연하고 적응 가능한 학습을 촉진하는 새로운 커뮤니케이션 및 정보 기술을 탐색합니다. 2) 학생들이 학습 및 기술 향상을 위한 기회를 감지하도록 도와줍니다. 3) 학생들이 스스로 학습하도록 동기를 부여합니다. 4) 학생들이 학습 과정에 대해 성찰하는 데 도움이 되는 다양한 지원 도구를 탐색합니다.

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10.1109/COASE.2019.8843029

Kinesthetic teaching and Dynamic Movement Primitives (DMPs) enable fast and adaptable learning of robot tasks based on a human demonstration.

운동 감각 교육 및 DMP(Dynamic Movement Primitives)를 통해 인간 시연을 기반으로 로봇 작업을 빠르고 적응력 있게 학습할 수 있습니다.

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Adaptable Learning 적응 학습

Encyclopedia 백과사전

Dec 02, 2024

Adaptable Learning(적응 학습)란 무엇입니까?

Adaptable Learning 적응 학습 - Originality/value This paper empirically demonstrates the nexus of work and learning as experienced by professionals in their day-to-day work, as well as the ways in which fluid work influences flexible and adaptable learning through participation in work. ^[1] It was created with four main objectives: 1) explore new communication and information technologies that promote flexible, adaptable learning; 2) help students detect opportunities for improvement in their learning and skills; 3) motivate the students to commit themselves to their self-learning; and, 4) explore different support tools that help the students reflect on their learning process. ^[2] Kinesthetic teaching and Dynamic Movement Primitives (DMPs) enable fast and adaptable learning of robot tasks based on a human demonstration. ^[3]
독창성/가치 이 논문은 일상 업무에서 전문가들이 경험한 바와 같이 업무와 학습의 연관성과 유동적인 업무가 업무 참여를 통해 유연하고 적응 가능한 학습에 영향을 미치는 방식을 경험적으로 보여줍니다. ^[1] 4가지 주요 목표로 만들어졌습니다. 1) 유연하고 적응 가능한 학습을 촉진하는 새로운 커뮤니케이션 및 정보 기술을 탐색합니다. 2) 학생들이 학습 및 기술 향상을 위한 기회를 감지하도록 도와줍니다. 3) 학생들이 스스로 학습하도록 동기를 부여합니다. 4) 학생들이 학습 과정에 대해 성찰하는 데 도움이 되는 다양한 지원 도구를 탐색합니다. ^[2] 운동 감각 교육 및 DMP(Dynamic Movement Primitives)를 통해 인간 시연을 기반으로 로봇 작업을 빠르고 적응력 있게 학습할 수 있습니다. ^[3]

adaptable learning rate 적응 학습률

A method for quantitative sound quality prediction of the PEV nonstationary interior noise based on tacho-tracking psychoacoustic metrics and deep CNNs with adaptable learning rate trees (ALRT-CNNs) is presented to solve the aforementioned problems. ^[1] Afterward, an 18-layer Residual Network (ResNet18), which contains the center loss function with the embedding layer, is designed to train the aggregated features with an adaptable learning rate. ^[2]
앞서 언급한 문제를 해결하기 위해 타코 추적 심리음향 메트릭과 ALRT-CNN(adaptable learning rate tree)이 있는 심층 CNN을 기반으로 하는 PEV 비정상 내부 소음의 정량적 음질 예측 방법이 제시됩니다. ^[1] 그 후, 임베딩 레이어가 있는 중심 손실 함수를 포함하는 18레이어 ResNet18(Residual Network)이 적응 가능한 학습률로 집계된 기능을 훈련하도록 설계되었습니다. ^[2]