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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 calls into the kernel of an operating system. Using system-call data sets generated by several different programs, we compare the ability of different data… (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, medical signal and image processing, hyperspectral imaging, and other areas. We suggest a two-stage separation process: a priori selection of a possibly overcomplete… (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 and to sparser training data. We then describe and discuss the winning algorithm of Rodney Price, which orders state merges according to the amount of evidence in… (More)

- Barak A. Pearlmutter
- International 1989 Joint Conference on Neural…
- 1989

A number of procedures are described for finding delta E/ delta W/sub ij/ where E is an error functional of the temporal trajectory of the states of a continuous recurrent network and w/sub ij/ are the weights of that network. Computing these quantities allows one to perform gradient descent in the weights to minimize E, so these procedures form the kernels… (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 recurrent backpropagation and deterministic Boltzmann machines, and nonfixed point algorithms, namely backpropagation through time, Elman's history cutoff, and… (More)

— Source separation arises in a surprising number of signal processing applications, from speech recognition to EEG analysis. In the square linear blind source separation problem without time delays, one must find an unmixing matrix which can detangle the result of mixing unknown independent sources through an unknown mixing matrix. The recently introduced… (More)

- Edmund C Lalor, Barak A Pearlmutter, Richard B Reilly, Gary McDarby, John J Foxe
- NeuroImage
- 2006

Faster and less obtrusive means for measuring a Visual Evoked Potential would be valuable in clinical testing and basic neuroscience research. This study presents a method for accomplishing this by smoothly modulating the luminance of a visual stimulus using a stochastic process. Despite its visually unobtrusive nature, the rich statistical structure of the… (More)

- Barak A. Pearlmutter, Lucas C. Parra
- NIPS
- 1996

In the square linear blind source separation problem, one must nd a linear unmixing operator which can detangle the result xi(t) of mixing n unknown independent sources si(t) through an unknown n n mixing matrix A(t) of causal linear lters: xi = P j aij sj . We cast the problem as one of maximum likelihood density estimation, and in that framework introduce… (More)

- Akaysha C. Tang, Barak A. Pearlmutter, Natalie A. Malaszenko, Dan B. Phung, Bethany C. Reeb
- Neural Computation
- 2002

We applied second-order blind identification (SOBI), an independent component analysis method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated… (More)

- Paul D. O'Grady, Barak A. Pearlmutter, Scott T. Rickard
- Int. J. Imaging Systems and Technology
- 2005

Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources. When the information about the mixing process and… (More)