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- Christina Warrender, Stephanie Forrest, Barak A. Pearlmutter
- IEEE Symposium on Security and Privacy
- 1999

Intrusion detection systems rely on a wide variety of observable data to distinguish between legitimate and illegitimate activities. In this paper we study one such observable— sequences of system… (More)

- Kevin J. Lang, Barak A. Pearlmutter, Rodney A. Price
- ICGI
- 1998

This paper first describes the structure and results of the Abbadingo One DFA Learning Competition. The competition was designed to encourage work on algorithms that scale well—both to larger DFAs… (More)

- Michael Zibulevsky, Barak A. Pearlmutter
- Neural Computation
- 2001

The blind source separation problem is to extract the underlying source signals from a set of linear mixtures, where the mixing matrix is unknown. This situation is common in acoustics, radio,… (More)

- Barak A. Pearlmutter
- Neural Computation
- 1994

Just storing the Hessian H (the matrix of second derivatives 2E/wiwj 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… (More)

- Barak A. Pearlmutter
- IEEE Trans. Neural Networks
- 1995

Surveys learning algorithms for recurrent neural networks with hidden units and puts the various techniques into a common framework. The authors discuss fixed point learning algorithms, namely… (More)

- Barak A. Pearlmutter
- Neural Computation
- 1988

Many neural network learning procedures compute gradients of the errors on the output layer of units after they have settled to their final values. We describe a procedure for finding E/wij, where E… (More)

- Paul D. O'Grady, Barak A. Pearlmutter
- 2006 16th IEEE Signal Processing Society Workshop…
- 2006

Discovering a representation which allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by… (More)

Barak A. Pearlmuttery Lucas C. Parraz yDept. of Cog. Sci., UCSD, La Jolla, California, USA, barak.pearlmutter@alumni.cs.cmu.edu zSiemens Corporate Research, Princeton, New Jersey, USA,… (More)

- Lucas Parra, Chris Alvino, Akaysha Tang, Barak A. Pearlmutter, Paul Sajda
- NeuroImage
- 2002

Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies on averaging over multiple trials to extract statistically relevant differences between two or more… (More)

- Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Radul
- Journal of Machine Learning Research
- 2017

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply “autodiff”, is a… (More)