Danielle Hinton

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Just storing the Hessian H (the matrix of second derivatives @2E=@wi@wj of the error E with respect to each pair of weights) of a large neural network is difficult. Since a common use of a large matrix like H is to compute its product with various vectors, we derive a technique that directly calculates Hv, where v is an arbitrary vector. This allows H to be(More)
The capacity of the non-coherent channel in a two-user-multi-access channel is investigated when a block length of N is used. The work here builds on the already investigated case of a single user. The lower and upper limit for the capacity region in this non-coherent multi access channel is established. In particular, it is shown that the capacity(More)
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