World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
14906
World Ranking
8587
National Ranking
30

Overview

Barak A. Pearlmutter is affiliated with the National University of Ireland, Maynooth. Their research primarily focuses on the field of Computer Science, with specializations across several subfields.

The main areas of study for Pearlmutter include:

  • Artificial Intelligence
  • Statistical and Nonlinear Physics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Human-Computer Interaction

Their documented research topics encompass:

  • Neural Networks and Applications
  • Machine Learning and Algorithms
  • Model Reduction and Neural Networks
  • Logic, Reasoning, and Knowledge
  • Virtual Reality Applications and Impacts
  • Adversarial Robustness in Machine Learning
  • Computational Physics and Python Applications

Pearlmutter has published extensively in various venues, notably:

  • arXiv (Cornell University)
  • 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)
  • Electronic Proceedings in Theoretical Computer Science
  • Science of Computer Programming
  • International Journal of Advanced Computer Science and Applications

Recent papers include:

  • Gradients without Backpropagation, 2022, arXiv (Cornell University)
  • Visualization of AI Systems in Virtual Reality: A Comprehensive Review, 2023, International Journal of Advanced Computer Science and Applications
  • Comparing differentiable logics for learning with logical constraints, 2025, Science of Computer Programming
  • Neural ODEs for Informative Missingness in Multivariate Time Series, 2020, arXiv (Cornell University)
  • Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE, 2021, 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)

Frequent collaborators in Pearlmutter's work include:

  • Mansura Habiba
  • Rosemary Monahan
  • Thomas Flinkow
  • Medet Inkarbekov
  • Atılım Güneş Baydin

Best Publications

  • Automatic differentiation in machine learning: a survey

    Atılım Günes Baydin;Barak A. Pearlmutter;Alexey Andreyevich Radul;Jeffrey Mark Siskind

  • Detecting intrusions using system calls: alternative data models

    C. Warrender;S. Forrest;B. Pearlmutter

  • Blind Source Separation by Sparse Decomposition in a Signal Dictionary

    Michael Zibulevsky;Barak A. Pearlmutter

  • Learning state space trajectories in recurrent neural networks

    Barak A. Pearlmutter

  • Gradient calculations for dynamic recurrent neural networks: a survey

    B.A. Pearlmutter

  • Fast exact multiplication by the Hessian

    Barak A. Pearlmutter

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

    Kevin J. Lang;Barak A. Pearlmutter;Rodney A. Price

  • Blind source separation by sparse decomposition

    Michael Zibulevsky;Barak A. Pearlmutter;Pau Bofill;Pavel Kisilev

  • A Context-Sensitive Generalization of ICA

    Barak A. Pearlmutter;Lucas C. Parra

  • Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA

    Barak A. Pearlmutter;Lucas C. Parra

  • Linear Spatial Integration for Single-Trial Detection in Encephalography

    Lucas C. Parra;Christopher V. Alvino;Akaysha C. Tang;Barak A. Pearlmutter

  • Survey of sparse and non-sparse methods in source separation

    Paul D. O'Grady;Barak A. Pearlmutter;Scott T. Rickard

  • Equivalence Proofs for Multi-Layer Perceptron Classifiers and the Bayesian Discriminant Function

    John B. Hampshire;Barak A. Pearlmutter

  • Dynamic recurrent neural networks

    Barak A. Pearlmutter

  • Hemodynamics for Brain-Computer Interfaces

    F. Matthews;B.A. Pearlmutter;T.E. Ward;C. Soraghan

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

    Edmund C. Lalor;Barak A. Pearlmutter;Richard B. Reilly;Richard B. Reilly;Gary McDarby

  • Independent components of magnetoencephalography: localization

    Akaysha C. Tang;Barak A. Pearlmutter;Natalie A. Malaszenko;Dan B. Phung

  • Automatic Learning Rate Maximization by On-Line Estimation of the Hessian's Eigenvectors

    Yann LeCun;Patrice Y. Simard;Barak Pearlmutter

  • Convolutive Non-Negative Matrix Factorisation with a Sparseness Constraint

    Paul O'Grady;Barak Pearlmutter

  • Blind Source Separation by Sparse Decomposition

    Michael Zibulevsky;Barak A. Pearlmutter

  • Hemodynamics for brain-computer interfaces: optical correlates of control signals

    Fiachra Matthews;Barak A. Pearlmutter;Tomas E. Ward;Christopher Soraghan

Frequent Co-Authors

Edmund C. Lalor
Edmund C. Lalor University of Rochester
John J. Foxe
John J. Foxe University of Rochester
Michael Zibulevsky
Michael Zibulevsky Technion – Israel Institute of Technology
Anthony M. Zador
Anthony M. Zador Cold Spring Harbor Laboratory
Richard B. Reilly
Richard B. Reilly Trinity College Dublin
Vince D. Calhoun
Vince D. Calhoun Georgia State University
Lucas C. Parra
Lucas C. Parra City College of New York
Tomas E. Ward
Tomas E. Ward Dublin City University
Roderick Murray-Smith
Roderick Murray-Smith University of Glasgow
Richard H. Middleton
Richard H. Middleton University of Newcastle Australia

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