Dmitry Kropotov

Researcher at Moscow State University
Email: kropotov -at- bayesgroup.ru

Research topics

  • Sparse Bayesian Learning
  • Variational Inference
  • Bayesian Feature Design
  • Textural Image Segmentation
  • Deep Learning

Selected publications

  • D. Elshin, D. Kropotov. MRF Energy Minimization Approach with Epitomic Textural Global Term for Image Segmentation Problems. In Proceedings of Bilateral Russian-Indian Workshop on Emerging Applications of Computer Vision, 2011 pdf
  • D. Kropotov, D. Vetrov, L. Wolf, T. Hassner. Variational Relevance Vector Machine for Tabular Data. In Proceedings of 2nd Asian Conference on Machine Learning (ACML), 2010 pdf
  • D. Kropotov, D. Laptev, A. Osokin, D. Vetrov. Variational Segmentation Algorithms with Label Frequency Constraints. Pattern Recognition and Image Analysis, Volume 20, Number 3, 324-334, 2010 link
  • D. Kropotov, D. Vetrov. General Solutions for Information-Based and Bayesian Approaches to Model Selection in Linear Regression and Their Equivalence. Pattern Recognition and Image Analysis, Volume 19, Number 3, 447-455, 2009 link
  • D.P. Vetrov, D.A. Kropotov, N.O. Ptashko. An Efficient Method for Feature Selection in Linear Regression Based on an Extended Akaike’s Information Criterion. Computational Mathematics and Mathematical Physics, Volume 49, Number 11, 1972-1985, 2009 link
  • D. Kropotov, D. Vetrov. On One Method of Non-Diagonal Regularization in Sparse Bayesian Learning. In Proceedings of 24th International Conference on Machine Learning (ICML), 2007 pdf
  • D. Kropotov, D. Vetrov. Fuzzy Rules Generation Method for Pattern Recognition Problems. Lecture Notes in Computer Science (LNCS), Vol. 4578, 203–210, 2007 link

Projects

  • Variational Optimization in Bayesian Models

Teaching

  • Bayesian Methods in Machine Learning
  • Graphical Models
  • Mathematical Foundations of Prediction Theory
  • Optimization in Machine Learning

Software