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random_projections [2018/08/29 10:34]
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random_projections [2018/11/03 16:49] (current)
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 We obtain the inverse of Fisher information explicitly. We then have an explicit form of the natural gradient, without relying on the numerical matrix inversion, which drastically speeds up stochastic gradient learning. We obtain the inverse of Fisher information explicitly. We then have an explicit form of the natural gradient, without relying on the numerical matrix inversion, which drastically speeds up stochastic gradient learning.
 +
 +https://​arxiv.org/​abs/​1412.6616v2 Outperforming Word2Vec on Analogy Tasks with Random Projections
 +
 +https://​arxiv.org/​abs/​1712.04323v2 Deep Echo State Network (DeepESN): A Brief Survey
 +
 +https://​arxiv.org/​abs/​1803.07125v2 Local Binary Pattern Networks
 +
 +In this paper, we tackle the problem using
 +a strategy different from the existing literature by proposing local
 +binary pattern networks or LBPNet, that is able to learn and perform
 +binary operations in an end-to-end fashion. LBPNet1 uses local binary
 +comparisons and random projection in place of conventional convolution
 +(or approximation of convolution) operations. These operations can
 +be implemented efficiently on different platforms including direct hardware
 +implementation