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Where Is the Learning in Learning Analytics?: A Systematic Literature Review to Identify Measures of Affected Learning

Lifelong Technology-Enhanced Learning, p.88-100

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Learning Systems: Machine-Learning in Software Products and Learning-Based Analysis of Software Systems: Special Track at ISoLA 2016

LEVERAGING APPLICATIONS OF FORMAL METHODS, VERIFICATION AND VALIDATION, 2016, p.651-654

2016

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Learning Loss for Active Learning

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, p.93-102

2019

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Meta-Transfer Learning for Few-Shot Learning

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, p.403-412

2019

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Learning to Prompt for Continual Learning

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, p.139-149

2022

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Deep Mutual Learning

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p.4320-4328

2018

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Large Scale Incremental Learning

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, p.374-382

2019

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Model-Contrastive Federated Learning

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, p.10708-10717

2021

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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

2019 IEEE Symposium on Security and Privacy (SP), 2019, p.739-753

2019

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Privacy-Preserving Deep Learning

Proceedings of the 22nd ACM SIGSAC Conference on computer and communications security, 2015, p.1310-1321

2015

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Exploring Simple Siamese Representation Learning

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, p.15745-15753

2021

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Representation Learning by Learning to Count

2017 IEEE International Conference on Computer Vision (ICCV), 2017, p.5899-5907

2017

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Learning Without Memorizing

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, p.5133-5141

2019

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Learning from Synthetic Humans

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, p.4627-4635

2017

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Learning to Compare: Relation Network for Few-Shot Learning

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p.1199-1208

2018

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Optimizing Federated Learning on Non-IID Data with Reinforcement Learning

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, 2020, p.1698-1707

2020

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Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

2018 IEEE Symposium on Security and Privacy (SP), 2018, p.19-35

2018

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Task-Free Continual Learning

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, p.11246-11255

2019

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Variational Adversarial Active Learning

2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, p.5971-5980

2019

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Noise2Void - Learning Denoising From Single Noisy Images

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, p.2124-2132

2019

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Learning Graphs to Match

2013 IEEE International Conference on Computer Vision, 2013, p.25-32

2013

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Learning Non-maximum Suppression

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, p.6469-6477

2017

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Task Agnostic Meta-Learning for Few-Shot Learning

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, p.11711-11719

2019

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Parametric Contrastive Learning

2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, p.695-704

2021

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Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, p.9042-9051

2021

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Where Is the Learning in Learning Analytics?: A Systematic Literature Review to Identify Measures of Affected Learning

Learning Systems: Machine-Learning in Software Products and Learning-Based Analysis of Software Systems: Special Track at ISoLA 2016

Learning Loss for Active Learning

Meta-Transfer Learning for Few-Shot Learning

Learning to Prompt for Continual Learning

Deep Mutual Learning

Large Scale Incremental Learning

Model-Contrastive Federated Learning

Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

Privacy-Preserving Deep Learning

Exploring Simple Siamese Representation Learning

Representation Learning by Learning to Count

Learning Without Memorizing

Learning from Synthetic Humans

Learning to Compare: Relation Network for Few-Shot Learning

Optimizing Federated Learning on Non-IID Data with Reinforcement Learning

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

Task-Free Continual Learning

Variational Adversarial Active Learning

Noise2Void - Learning Denoising From Single Noisy Images

Learning Graphs to Match

Learning Non-maximum Suppression

Task Agnostic Meta-Learning for Few-Shot Learning

Parametric Contrastive Learning

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

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