What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Mathematical analysis
Vladimir Kolmogorov mainly focuses on Cut, Artificial intelligence, Image segmentation, Graph cuts in computer vision and Algorithm.
The concepts of his Cut study are interwoven with issues in Graph theory, Theoretical computer science, Mathematical optimization and Minimum cut.
His Minimum cut research includes themes of Graph, Directed graph, Analysis of parallel algorithms, Benchmark and Combinatorial optimization.
His Artificial intelligence research includes elements of Computer vision and Pattern recognition.
Vladimir Kolmogorov undertakes interdisciplinary study in the fields of Graph cuts in computer vision and Markov process through his research.
His research on Algorithm frequently connects to adjacent areas such as Dual polyhedron.
His most cited work include:
- "GrabCut": interactive foreground extraction using iterated graph cuts (4799 citations)
- An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision (4041 citations)
- What energy functions can be minimized via graph cuts (2770 citations)
What are the main themes of his work throughout his whole career to date?
Vladimir Kolmogorov mainly investigates Artificial intelligence, Algorithm, Discrete mathematics, Combinatorics and Cut.
His Artificial intelligence research incorporates themes from Graph theory, Computer vision and Pattern recognition.
His research integrates issues of Maximum flow problem, Mathematical optimization, Inference and Message passing in his study of Algorithm.
Vladimir Kolmogorov has included themes like Function and Submodular set function in his Discrete mathematics study.
His Combinatorics research is multidisciplinary, relying on both Characterization and Relaxation.
In the field of Cut, his study on Graph cuts in computer vision overlaps with subjects such as Energy minimization.
He most often published in these fields:
- Artificial intelligence (31.91%)
- Algorithm (26.95%)
- Discrete mathematics (26.24%)
What were the highlights of his more recent work (between 2014-2021)?
- Discrete mathematics (26.24%)
- Combinatorics (24.82%)
- Constraint satisfaction problem (12.77%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Discrete mathematics, Combinatorics, Constraint satisfaction problem, Function and Upper and lower bounds.
The Commutative property, Time complexity and Lovász local lemma research Vladimir Kolmogorov does as part of his general Discrete mathematics study is frequently linked to other disciplines of science, such as Variable, therefore creating a link between diverse domains of science.
His work on Matroid is typically connected to Omega as part of general Combinatorics study, connecting several disciplines of science.
His studies in Function integrate themes in fields like Computational complexity theory and Algorithm.
The study incorporates disciplines such as Variation, Indicator function and Linear map in addition to Algorithm.
Vladimir Kolmogorov brings together Artificial intelligence and Energy minimization to produce work in his papers.
Between 2014 and 2021, his most popular works were:
- Proofs of Space (154 citations)
- The Power of Linear Programming for General-Valued CSPs (52 citations)
- A New Look at Reweighted Message Passing (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Mathematical analysis
Vladimir Kolmogorov focuses on Discrete mathematics, Combinatorics, Function, Finite set and Constraint satisfaction problem.
His work in the fields of Time complexity, Lemma and Lovász local lemma overlaps with other areas such as State space.
His Time complexity research is multidisciplinary, incorporating elements of Graph, Open problem, Directed acyclic graph and Type.
His Combinatorics research incorporates elements of Current, Upper and lower bounds, Hash function and Logarithm.
His Function research is multidisciplinary, incorporating perspectives in Computational complexity theory and Algorithm.
His research on Finite set also deals with topics like
- Linear programming relaxation, which have a strong connection to Tree, Domain, Submodular set function and Arity,
- Algebraic number which is related to area like Domain and Conjecture.
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