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Data entry constitutes a fundamental component of the machine learning pipeline, yet it frequently results in the introduction of labelling errors.
Backpropagation and stochastic gradient descent method
Shun-ichi Amari · 1993
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Convolutional deep belief networks on cifar-10
Alex Krizhevsky and Geoff Hinton · 2010
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep learning using rectified linear units (relu)
Abien Fred Agarap · 2018
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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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Dataco smart supply chain for big data analysis, 2019
Fabian Constante, Fernando Silvia, and Antonio Pereira · 2019
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Does learning require memorization? A short tale about a long tail
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Pervasive label errors in test sets destabilize machine learning benchmarks
Curtis G. Northcutt, Anish Athalye, and Jonas Mueller · 2021
Interpretability, then what? editing machine learning models to reflect human knowledge and values
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, and Rich Caruana · 2022
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Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 2023
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Fast machine unlearning without retraining through selective synaptic dampening
Jack Foster, Stefan Schoepf, and Alexandra Brintrup · 2023
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Aging with grace: Lifelong model editing with discrete key-value adaptors
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi · 2023
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Editing a classifier by rewriting its prediction rules
Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, and Aleksander Madry · 2021
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Repairing neural networks by leaving the right past behind
Ryutaro Tanno, Melanie F Pradier, Aditya Nori, and Yingzhen Li · 2022
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Machine unlearning in learned databases: An experimental analysis
Meghdad Kurmanji, Eleni Triantafillou, and Peter Triantafillou
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou
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Sangamesh Kodge, Gobinda Saha, and Kaushik Roy · 2023
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Fast yet effective machine unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli · 2023
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Zero-shot machine unlearning at scale via lipschitz regularization
Jack Foster, Kyle Fogarty, Stefan Schoepf, Cengiz Öztireli, and Alexandra Brintrup · 2024
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