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Machine unlearning has great significance in guaranteeing model security and protecting user privacy.
Some methods for classification and analysis of multivariate observations
James MacQueen et al · 1967
Earlier work this paper cites.
Taxicab geometry
Eugene F Krause · 1973
Earlier work this paper cites.
Characterizations of an empirical influence function for detecting influential cases in regression
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Earlier work this paper cites.
Residuals and influence in regression
R Dennis Cook and Sanford Weisberg · 1982
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J. Cohen · 1989
Earlier work this paper cites.
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
Roger Ratcliff · 1990
Earlier work this paper cites.
A survey of decision tree classifier methodology
S. Rasoul Safavian and David A. Landgrebe · 1991
Earlier work this paper cites.
Mosaic organization of dna nucleotides
C K Peng, Sergey V. Buldyrev, Shlomo Havlin, Michael Simons, Harry Eugene Stanley, and Ary L. Goldberger · 1994
Earlier work this paper cites.
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Earlier work this paper cites.
Long short-term memory
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Earlier work this paper cites.
The vanishing gradient problem during learning recurrent neural nets and problem solutions
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Earlier work this paper cites.
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Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
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Earlier work this paper cites.
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