D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 63 Citations 65,067 284 World Ranking 1672 National Ranking 11

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Aapo Hyvärinen mostly deals with Independent component analysis, Artificial intelligence, Pattern recognition, Blind signal separation and Applied mathematics. His work deals with themes such as Data mining, Functional magnetic resonance imaging, Neural coding, Algorithm and Principal component analysis, which intersect with Independent component analysis. His study in Neural coding is interdisciplinary in nature, drawing from both Artificial neural network, Probabilistic logic, Receptive field and Visual cortex.

The study incorporates disciplines such as Estimator and Projection pursuit in addition to Algorithm. Aapo Hyvärinen has researched Artificial intelligence in several fields, including Machine learning and Computer vision. His Feature extraction study, which is part of a larger body of work in Pattern recognition, is frequently linked to Noise, bridging the gap between disciplines.

His most cited work include:

  • Independent Component Analysis (8216 citations)
  • Independent component analysis: algorithms and applications (6221 citations)
  • Fast and robust fixed-point algorithms for independent component analysis (4957 citations)

What are the main themes of his work throughout his whole career to date?

Aapo Hyvärinen focuses on Artificial intelligence, Independent component analysis, Pattern recognition, Algorithm and Nonlinear system. His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision. Aapo Hyvärinen connects Independent component analysis with Blind signal separation in his study.

His Pattern recognition research incorporates elements of Image processing, Maximization and Visual cortex. The concepts of his Algorithm study are interwoven with issues in Estimator, Representation, Probabilistic logic and Cluster analysis. His Probabilistic logic study combines topics from a wide range of disciplines, such as Quadratic equation and Jacobian matrix and determinant.

He most often published in these fields:

  • Artificial intelligence (59.10%)
  • Independent component analysis (44.18%)
  • Pattern recognition (40.90%)

What were the highlights of his more recent work (between 2016-2021)?

  • Algorithm (24.78%)
  • Artificial intelligence (59.10%)
  • Nonlinear system (13.13%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Algorithm, Artificial intelligence, Nonlinear system, Artificial neural network and Identifiability. His Algorithm study integrates concerns from other disciplines, such as Probabilistic logic, Parametric statistics, Normalizing constant and Cluster analysis. His work carried out in the field of Artificial intelligence brings together such families of science as Data modeling, Machine learning and Pattern recognition.

As a part of the same scientific family, Aapo Hyvärinen mostly works in the field of Pattern recognition, focusing on Functional magnetic resonance imaging and, on occasion, Video based and Information processing. His work in Artificial neural network addresses subjects such as Maximum likelihood, which are connected to disciplines such as Autoregressive model. His biological study spans a wide range of topics, including Working memory, Noise and Extension.

Between 2016 and 2021, his most popular works were:

  • Variational Autoencoders and Nonlinear ICA: A Unifying Framework (76 citations)
  • Variational Autoencoders and Nonlinear ICA: A Unifying Framework (76 citations)
  • Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning (47 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Machine learning

Aapo Hyvärinen spends much of his time researching Nonlinear system, Algorithm, Identifiability, Latent variable and Feature learning. Nonlinear system combines with fields such as Applied mathematics, Variable, Independent component analysis, Multivariate statistics and Bivariate analysis in his work. His Independent component analysis research includes themes of Subspace topology, Pooling, Measure, Current and Simple.

His Algorithm study incorporates themes from Stochastic gradient descent, Curse of dimensionality, Stability, Generative model and Multilayer perceptron. His research on Identifiability also deals with topics like

  • Statistical physics which intersects with area such as Data mining, Gaussian process and Conditioning,
  • Statistical assumption, Generalization, Probabilistic logic and Function space most often made with reference to Transformation,
  • Function that intertwine with fields like Energy, Interpretation, Balanced flow and Concentration of measure. His Feature learning research is multidisciplinary, relying on both Artificial neural network, Data collection, Data modeling and Time series.

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.

Best Publications

Independent component analysis: algorithms and applications

A. Hyvärinen;E. Oja.
Neural Networks (2000)

22492 Citations

Independent Component Analysis

Aapo Hyvarinen;Juha Karhunen;Erkki Oja.
(2001)

8362 Citations

Fast and robust fixed-point algorithms for independent component analysis

A. Hyvarinen.
IEEE Transactions on Neural Networks (1999)

8015 Citations

A fast fixed-point algorithm for independent component analysis

Aapo Hyvärinen;Erkki Oja.
Neural Computation (1997)

4709 Citations

Survey on Independent Component Analysis

A. Hyvärinen.
Neural Computing Surveys (1999)

1931 Citations

Validating the independent components of neuroimaging time series via clustering and visualization.

Johan Himberg;Aapo Hyvärinen;Fabrizio Esposito.
NeuroImage (2004)

1189 Citations

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Michael Gutmann;Aapo Hyvärinen.
international conference on artificial intelligence and statistics (2010)

1110 Citations

A Linear Non-Gaussian Acyclic Model for Causal Discovery

Shohei Shimizu;Patrik O. Hoyer;Aapo Hyvärinen;Antti Kerminen.
Journal of Machine Learning Research (2006)

1042 Citations

A fast fixed-point algorithm for independent component analysis of complex valued signals.

Ella Bingham;Aapo Hyvärinen.
International Journal of Neural Systems (2000)

1035 Citations

Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces

Aapo Hyvärinen;Patrik Hoyer.
Neural Computation (2000)

725 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Aapo Hyvärinen

Vince D. Calhoun

Vince D. Calhoun

Georgia State University

Publications: 358

Tulay Adali

Tulay Adali

University of Maryland, Baltimore County

Publications: 133

Andrzej Cichocki

Andrzej Cichocki

Systems Research Institute

Publications: 117

Fabian J. Theis

Fabian J. Theis

Technical University of Munich

Publications: 100

Bernhard Schölkopf

Bernhard Schölkopf

Max Planck Institute for Intelligent Systems

Publications: 90

Carlos G. Puntonet

Carlos G. Puntonet

University of Granada

Publications: 89

Christian Jutten

Christian Jutten

Grenoble Alpes University

Publications: 84

Shoko Araki

Shoko Araki

NTT (Japan)

Publications: 81

Shoji Makino

Shoji Makino

Waseda University

Publications: 78

Hiroshi Sawada

Hiroshi Sawada

NTT (Japan)

Publications: 77

Kun Zhang

Kun Zhang

Carnegie Mellon University

Publications: 73

Stephen M. Smith

Stephen M. Smith

University of Oxford

Publications: 66

Fabrizio Esposito

Fabrizio Esposito

University of Campania "Luigi Vanvitelli"

Publications: 66

Klaus-Robert Müller

Klaus-Robert Müller

Technical University of Berlin

Publications: 65

Christian F. Beckmann

Christian F. Beckmann

Radboud University Nijmegen

Publications: 64

Yoshua Bengio

Yoshua Bengio

University of Montreal

Publications: 60

Trending Scientists

Iain M. Johnstone

Iain M. Johnstone

Stanford University

Claudio Gentile

Claudio Gentile

Google (United States)

Tong Sun

Tong Sun

City, University of London

Carlos Téllez

Carlos Téllez

University of Zaragoza

Nobuo Tanaka

Nobuo Tanaka

Kyoto Institute of Technology

Lutz Mädler

Lutz Mädler

University of Bremen

Costas Panayiotou

Costas Panayiotou

Aristotle University of Thessaloniki

Tomas Marques-Bonet

Tomas Marques-Bonet

Pompeu Fabra University

Jens Krause

Jens Krause

Technical University of Berlin

Lorenzo Lamattina

Lorenzo Lamattina

National University of Mar del Plata

Sylvie Robine

Sylvie Robine

Institute Curie

Kei Sakamoto

Kei Sakamoto

University of Copenhagen

Daniel J. Clauw

Daniel J. Clauw

University of Michigan–Ann Arbor

Jany Rademakers

Jany Rademakers

Netherlands Institute for Health Services Research

M. Villata

M. Villata

National Institute for Astrophysics

Joseph D. Twicken

Joseph D. Twicken

Ames Research Center

Something went wrong. Please try again later.