期刊
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2017, PT I
卷 10613, 期 -, 页码 219-226出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-68600-4_26
关键词
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We revisit the Frank-Wolfe algorithm for constrained convex optimization and show that it can be implemented as a simple recurrent neural network with softmin activation functions. As an example for a practical application of this result, we discuss how to train such a network to act as an associative memory.
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