What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Machine learning
- Algebra
His scientific interests lie mostly in Curse of dimensionality, Tensor, Applied mathematics, Density matrix renormalization group and Rank.
The various areas that Ivan V. Oseledets examines in his Curse of dimensionality study include Eigenvalues and eigenvectors, Matrix and Algorithm, Singular value decomposition, Computation.
To a larger extent, he studies Algebra with the aim of understanding Singular value decomposition.
His research integrates issues of Backpropagation, Representation, Dimensionality reduction, Convolutional neural network and Least squares in his study of Tensor.
His research on Applied mathematics frequently links to adjacent areas such as Tangent space.
Ivan V. Oseledets has researched Rank in several fields, including Pure mathematics, Combinatorics, Iterative method, Preconditioner and Numerical analysis.
His most cited work include:
- Tensor-Train Decomposition (1125 citations)
- Breaking the Curse of Dimensionality, Or How to Use SVD in Many Dimensions (324 citations)
- TT-cross approximation for multidimensional arrays (298 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Applied mathematics, Algorithm, Artificial intelligence, Rank and Artificial neural network.
The study incorporates disciplines such as Tensor, Matrix product state, Discretization, Low-rank approximation and Computation in addition to Applied mathematics.
Ivan V. Oseledets combines subjects such as Discrete mathematics and Eigenvalues and eigenvectors with his study of Computation.
His Algorithm study also includes
- Matrix that intertwine with fields like Pure mathematics and Linear system,
- Factorization that intertwine with fields like Sparse matrix.
His studies deal with areas such as Combinatorics, Compression and Tensor as well as Rank.
His studies in Tensor integrate themes in fields like Mathematical analysis, Approximation algorithm, Curse of dimensionality and Dimensionality reduction.
He most often published in these fields:
- Applied mathematics (29.62%)
- Algorithm (23.00%)
- Artificial intelligence (19.86%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (19.86%)
- Artificial neural network (13.94%)
- Algorithm (23.00%)
In recent papers he was focusing on the following fields of study:
Ivan V. Oseledets mostly deals with Artificial intelligence, Artificial neural network, Algorithm, Applied mathematics and Deep learning.
His Artificial intelligence research incorporates themes from Machine learning, Recommender system and Euclidean geometry.
His Artificial neural network study combines topics in areas such as Convolutional neural network, Pattern recognition and Data mining.
His Algorithm research incorporates elements of Optimal design, Compression and Interpolation.
His Applied mathematics research includes elements of Discretization, Stochastic gradient descent, Partial differential equation and Rank.
He works mostly in the field of Simple, limiting it down to concerns involving Combinatorics and, occasionally, Ring, Tensor and Computation.
Between 2019 and 2021, his most popular works were:
- Hyperbolic Image Embeddings (25 citations)
- Deep neural networks predicting oil movement in a development unit (20 citations)
- Tensor Train Decomposition on TensorFlow (T3F) (11 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Machine learning
- Mathematical analysis
His primary areas of investigation include Rank, Theoretical computer science, Artificial intelligence, Artificial neural network and Tensor.
Ivan V. Oseledets has included themes like Tensor, Combinatorics, Ring, Compression and Computation in his Rank study.
His work deals with themes such as Permutation, Numerical stability, Degeneracy, Algorithm and Convolutional neural network, which intersect with Compression.
His Theoretical computer science research is multidisciplinary, incorporating elements of Adversarial system and Multiple image.
Ivan V. Oseledets frequently studies issues relating to Tucker decomposition and Artificial intelligence.
His study in Tensor is interdisciplinary in nature, drawing from both Approximation algorithm, Matrix decomposition, Iterative method, Sparse matrix and Dimensionality reduction.
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