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We investigate generalized versions of the Iteratively Regularized Landweber Method, initially introduced in [Appl.
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A. Papoulis · 1962
Earlier work this paper cites.
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H. W. Engl, M. Hanke, and A. Neubauer · 1996
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.
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Earlier work this paper cites.
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