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- W1916961551 abstract "Information theoretic criteria for neural network adaptation laws have become an important focus of attention. We consider the problem of adaptively maximizing the entropy of the outputs of a deterministic feedforward neural network with real valued stochastic input signals, as considered by Bell and Sejnowski (1995). We give a new explanation for the relevance of output information (entropy) maximization for source separation applications and reinterpret Bell and Sejnowski's approach in a more general context of probability density estimation. This insight is the basis for a generalization of the approach, and we consider a family of gradient based algorithms." @default.
- W1916961551 created "2016-06-24" @default.
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- W1916961551 date "2002-11-22" @default.
- W1916961551 modified "2023-09-27" @default.
- W1916961551 title "Local adaptive algorithms for information maximization in neural networks, and application to source separation" @default.
- W1916961551 cites W1977067929 @default.
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- W1916961551 doi "https://doi.org/10.1109/icassp.1997.599541" @default.
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