# Barak A. Pearlmutter

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- Publications
- Influence

Detecting intrusions using system calls: alternative data models

- Christina Warrender, S. Forrest, Barak A. Pearlmutter
- Computer Science
- Proceedings of the IEEE Symposium on Security…
- 1999

Intrusion detection systems rely on a wide variety of observable data to distinguish between legitimate and illegitimate activities. We study one such observable-sequences of system calls into the… Expand

Results of the Abbadingo One DFA Learning Competition and a New Evidence-Driven State Merging Algorithm

- K. Lang, Barak A. Pearlmutter, R. Price
- Computer Science
- ICGI
- 12 July 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… Expand

Fast Exact Multiplication by the Hessian

- Barak A. Pearlmutter
- Mathematics, Computer Science
- 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… Expand

Automatic differentiation in machine learning: a survey

- Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Radul, J. Siskind
- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 20 February 2015

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

Learning State Space Trajectories in Recurrent Neural Networks

- Barak A. Pearlmutter
- Computer Science
- Neural Computation
- 1 June 1989

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… Expand

Gradient calculations for dynamic recurrent neural networks: a survey

- Barak A. Pearlmutter
- Computer Science, Medicine
- IEEE Trans. Neural Networks
- 1 September 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… Expand

Blind Source Separation by Sparse Decomposition in a Signal Dictionary

- Michael Zibulevsky, Barak A. Pearlmutter
- Computer Science, Mathematics
- Neural Computation
- 1 April 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,… Expand

A Context-Sensitive Generalization of ICA

- Barak A. Pearlmutter, L. Parra
- Mathematics
- 1996

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,… Expand

- 228
- 14
- Open Access

Survey of sparse and non‐sparse methods in source separation

- Paul D. O'Grady, Barak A. Pearlmutter, S. Rickard
- Computer Science
- Int. J. Imaging Syst. Technol.
- 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… Expand

The VESPA: A method for the rapid estimation of a visual evoked potential

- E. Lalor, Barak A. Pearlmutter, R. Reilly, G. McDarby, J. Foxe
- Medicine, Computer Science
- NeuroImage
- 1 October 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… Expand