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Found 2 papers, 1 papers with code
A Theoretical View of Linear Backpropagation and Its Convergence
This paper serves as a complement and somewhat an extension to Guo et al.'s paper, by providing theoretical analyses on LinBP in neural-network-involved learning tasks, including adversarial attack and model training.
Linear Backpropagation Leads to Faster Convergence
In this paper, we study the very recent method called ``linear backpropagation'' (LinBP), which modifies the standard backpropagation and can improve the transferability in black-box adversarial attack.
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