What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Algorithm, Artificial intelligence, Artificial neural network, Blind signal separation and Pattern recognition.
His Algorithm research is multidisciplinary, incorporating elements of Information theory, Mutual information, Divergence and Information geometry.
His Artificial intelligence research incorporates themes from Convergence and Autocorrelation.
He has researched Artificial neural network in several fields, including Stability and Mathematical optimization, Gradient method.
His Gradient method study incorporates themes from Gradient descent and Applied mathematics.
His studies deal with areas such as Non-negative matrix factorization, Independent component analysis, Source separation, Blind deconvolution and Sparse approximation as well as Blind signal separation.
His most cited work include:
- Natural gradient works efficiently in learning (2024 citations)
- A New Learning Algorithm for Blind Signal Separation (1834 citations)
- Methods of information geometry (1783 citations)
What are the main themes of his work throughout his whole career to date?
Shun-ichi Amari mostly deals with Artificial intelligence, Algorithm, Artificial neural network, Information geometry and Applied mathematics.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
The Algorithm study combines topics in areas such as Independent component analysis, Non-negative matrix factorization, Blind signal separation and Signal processing.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Stability, Fisher information and Topology.
Shun-ichi Amari works mostly in the field of Information geometry, limiting it down to topics relating to Divergence and, in certain cases, Pure mathematics.
The study incorporates disciplines such as Estimator, Gravitational singularity, Mathematical optimization and Metric in addition to Applied mathematics.
He most often published in these fields:
- Artificial intelligence (29.18%)
- Algorithm (25.65%)
- Artificial neural network (21.18%)
What were the highlights of his more recent work (between 2010-2021)?
- Information geometry (18.82%)
- Artificial intelligence (29.18%)
- Combinatorics (5.65%)
In recent papers he was focusing on the following fields of study:
His main research concerns Information geometry, Artificial intelligence, Combinatorics, Artificial neural network and Pattern recognition.
His Information geometry research includes themes of Probability distribution, Structure, Information integration, Simplex and Divergence.
His Probability distribution research incorporates themes from Exponential family, Applied mathematics, Kullback–Leibler divergence, Statistical inference and Entropy.
His studies in Artificial intelligence integrate themes in fields like Structure, Machine learning, Computer vision and Signal processing.
His Artificial neural network research incorporates elements of Normalization, Fisher information and Attractor.
His Pattern recognition research focuses on Feature and how it connects with Feature vector.
Between 2010 and 2021, his most popular works were:
- Information Geometry and Its Applications (344 citations)
- Measuring integrated information from the decoding perspective (126 citations)
- Bayesian Robust Tensor Factorization for Incomplete Multiway Data (112 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Information geometry, Artificial intelligence, Probability distribution, Divergence and Kullback–Leibler divergence.
His Information geometry research is multidisciplinary, relying on both Structure, Simplex, Combinatorics, Affine transformation and Differential geometry.
Shun-ichi Amari combines subjects such as Machine learning, Blind signal separation and Pattern recognition with his study of Artificial intelligence.
His Probability distribution research integrates issues from Exponential family, Applied mathematics and Geometry, Invariant.
His Applied mathematics research is multidisciplinary, incorporating elements of Entropy and Regularization.
As part of the same scientific family, Shun-ichi Amari usually focuses on Divergence, concentrating on Pure mathematics and intersecting with Bhattacharyya distance and Multivariate normal distribution.
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