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Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i.i.d).
Multitask connectionist learning
Richard A Caruana · 1993
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The parallel transfer of task knowledge using dynamic learning rates based on a measure of relatedness
Daniel L Silver and Robert E Mercer · 1996
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Semi-supervised learning literature survey
Xiaojin Jerry Zhu · 2005
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Curriculum learning
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Dataset shift in machine learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 2009
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Active learning literature survey
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Bayesian active learning for classification and preference learning
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Applying active learning to high-throughput phenotyping algorithms for electronic health records data
Yukun Chen, Robert J Carroll, Eugenia R McPeek Hinz, Anushi Shah, Anne E Eyler, Joshua C Denny, and Hua Xu · 2013
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Mimic-III, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
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Deep Bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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icarl: Incremental classifier and representation learning
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Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally
Zongwei Zhou, Jae Shin, Lei Zhang, Suryakanth Gurudu, Michael Gotway, and Jianming Liang · 2017
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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
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Pytorch: An imperative style, high-performance deep learning library
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Experience replay for continual learning
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Data parameters: A new family of parameters for learning a differentiable curriculum
Shreyas Saxena, Oncel Tuzel, and Dennis DeCoste · 2019
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O-medal: Online active deep learning for medical image analysis
Asim Smailagic, Pedro Costa, Alex Gaudio, Kartik Khandelwal, Mostafa Mirshekari, Jonathon Fagert, Devesh Walawalkar, Susu Xu, Adrian Galdran, Pei Zhang, et al · 2019
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Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 2019
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Selective experience replay for lifelong learning
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Medal: Accurate and robust deep active learning for medical image analysis
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Icebreaker: element-wise active information acquisition with bayesian deep latent gaussian model
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Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Awni Y Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H Tison, Codie Bourn, Mintu P Turakhia, and Andrew Y Ng · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Online continual learning with maximal interfered retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, and Lucas Page-Caccia
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A global and updatable ecg beat classification system based on recurrent neural networks and active learning
Guijin Wang, Chenshuang Zhang, Yongpan Liu, Huazhong Yang, Dapeng Fu, Haiqing Wang, and Ping Zhang · 2019
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Alps: Active learning via perturbations
Dani Kiyasseh, Tingting Zhu, and David A Clifton · 2020
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Continual learning for domain adaptation in chest x-ray classification
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Classification of 12-lead ECGs: the PhysioNet - computing in cardiology challenge 2020 (version 1.0.1)
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A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients
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