Barak A. Pearlmutter

TL;DR: This work compares the ability of different data modeling methods to represent normal behavior accurately and to recognize intrusions and concludes that for this particular problem, weaker methods than HMMs are likely sufficient.

TL;DR: This work suggests a two-stage separation process: a priori selection of a possibly overcomplete signal dictionary in which the sources are assumed to be sparsely representable, followed by unmixing the sources by exploiting the their sparse representability.

TL;DR: Automatic differentiation (AD) is a family of techniques similar to backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs as discussed by the authors, which is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization.

TL;DR: Automatic differentiation (AD) is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs as discussed by the authors, which is a small but established field with applications in areas including computational uid dynamics, atmospheric sciences, and engineering design optimization.

TL;DR: A procedure for finding E/wij, where E is an error functional of the temporal trajectory of the states of a continuous recurrent network and wij are the weights of that network, which seems particularly suited for temporally continuous domains.