What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
His primary areas of investigation include Artificial intelligence, Pattern recognition, Blind signal separation, Backpropagation and Algorithm.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Non-negative matrix factorization and Signal processing.
The Pattern recognition study combines topics in areas such as Data modeling, Representation, Sensory system and Magnetoencephalography.
His Blind signal separation research is multidisciplinary, incorporating elements of Context, Matrix, Independent component analysis and Sparse approximation.
His Backpropagation research integrates issues from Intersection, Differential operator and Stochastic gradient descent.
His Algorithm research includes themes of Second derivative, Hessian matrix and Applied mathematics.
His most cited work include:
- Detecting intrusions using system calls: alternative data models (1021 citations)
- Blind Source Separation by Sparse Decomposition in a Signal Dictionary (698 citations)
- Learning state space trajectories in recurrent neural networks (568 citations)
What are the main themes of his work throughout his whole career to date?
Barak A. Pearlmutter mainly focuses on Artificial intelligence, Algorithm, Automatic differentiation, Pattern recognition and Speech recognition.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Blind signal separation and Signal processing.
His studies deal with areas such as Transformation, Functional programming, Theoretical computer science and Operator as well as Automatic differentiation.
While the research belongs to areas of Pattern recognition, Barak A. Pearlmutter spends his time largely on the problem of Representation, intersecting his research to questions surrounding Non-negative matrix factorization.
In his research, Evoked potential is intimately related to Stimulus, which falls under the overarching field of Speech recognition.
Barak A. Pearlmutter works mostly in the field of Artificial neural network, limiting it down to topics relating to Deep learning and, in certain cases, Recurrent neural network, as a part of the same area of interest.
He most often published in these fields:
- Artificial intelligence (42.41%)
- Algorithm (19.37%)
- Automatic differentiation (18.32%)
What were the highlights of his more recent work (between 2012-2020)?
- Automatic differentiation (18.32%)
- Artificial intelligence (42.41%)
- Computation (6.28%)
In recent papers he was focusing on the following fields of study:
Barak A. Pearlmutter mainly investigates Automatic differentiation, Artificial intelligence, Computation, Parallel computing and Machine learning.
His studies in Automatic differentiation integrate themes in fields like Field, Chain rule and Linear algebra.
His specific area of interest is Artificial intelligence, where Barak A. Pearlmutter studies Artificial neural network.
His Computation research incorporates elements of Norm, Overhead, Lambda calculus and Non-negative matrix factorization.
As part of the same scientific family, Barak A. Pearlmutter usually focuses on Machine learning, concentrating on Variety and intersecting with Propagation of uncertainty, Differential calculus, Transformation and Machine translation.
The various areas that Barak A. Pearlmutter examines in his Pattern recognition study include FastICA and Synthetic data.
Between 2012 and 2020, his most popular works were:
- Automatic differentiation in machine learning: a survey (521 citations)
- Automatic differentiation in machine learning: a survey (81 citations)
- Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Barak A. Pearlmutter focuses on Artificial intelligence, Automatic differentiation, Machine learning, Independent component analysis and Programming language.
Barak A. Pearlmutter studies Variety which is a part of Artificial intelligence.
His Automatic differentiation research is multidisciplinary, incorporating perspectives in Toolbox, Backpropagation and Linear algebra.
His Machine learning research incorporates themes from Intersection and Field.
His biological study spans a wide range of topics, including Download, Principal component analysis, Synthetic data and Natural language processing.
His work in the fields of Programming language, such as Operator, Optimizing compiler, Preprocessor and Fortran, intersects with other areas such as Order.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
