Dmytro Perekrestenko MSc EPFL EE

Note: Dmytro Perekrestenko is no longer with our group.

Publications

• Journal Papers and Manuscripts
  • High-dimensional distribution generation through deep neural networks
    D. Perekrestenko, L. Eberhard, and H. Bölcskei, Partial Differential Equations and Applications, Springer, invited paper, Vol. 2, Article No. 64, Sept. 2021.
  • Deep neural network approximation theory
    D. Elbrächter, D. Perekrestenko, P. Grohs, and H. Bölcskei, IEEE Transactions on Information Theory, invited feature paper, Vol. 67, No. 5, pp. 2581-2623, May 2021.

• Conference, Symposium, and Workshop Papers
  • Constructive universal high-dimensional distribution generation through deep ReLU networks
    D. Perekrestenko, S. Müller, and H. Bölcskei, Proc. of the 37th International Conference on Machine Learning (ICML), Vienna, Austria, July 2020.
  • Convolutional recurrent neural networks for electrocardiogram classification
    M. Zihlmann, D. Perekrestenko, and M. Tschannen, 2017 Computing in Cardiology (CinC), Rennes, France, pp. 1-4, Sept. 2017.
  • Faster coordinate descent via adaptive importance sampling
    D. Perekrestenko, V. Cevher, and M. Jaggi, Proc. of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida, USA, Apr. 2017.

Supervised Theses

• Diploma Theses
  • Eberhard, Léandre, "Capabilities of Generative Networks to Represent High-Dimensional Distributions," Spring semester 2020
    Supervisor(s): Dmytro Perekrestenko
  • Müller, Stephan, "Representational Capabilities of Generative Networks," Spring semester 2019
    Supervisor(s): Dmytro Perekrestenko

• Student Projects
  • Ray, Siddhant, "Attentive Neural Networks for News Classification," Spring semester 2021
    Supervisor(s): Dmytro Perekrestenko
  • Zhan, Qipeng, "Fundamental Limits of Distribution Approximation through Deep Generative Networks," Spring semester 2020
    Supervisor(s): Dmytro Perekrestenko
  • Schenkel, Philipp, "Recurrent Neural Networks for Oenological Review Generation," Fall semester 2019
    Supervisor(s): Dmytro Perekrestenko
  • Rade, Rahul, "Recreation of arXiv Citation Graph Using Recurrent Neural Networks," Fall semester 2019
    Supervisor(s): Dmytro Perekrestenko, Michael Lerjen
  • Cid Ornelas, Gustavo, "Multimodal Emotion Recognition Using Lexical-Acoustic Language Descriptions," Spring semester 2019
    Supervisor(s): Dmytro Perekrestenko
  • Hamdan, Sami, "Grounded Language Learning of Visual-Lexical Color Descriptions," Fall semester 2018
    Supervisor(s): Dmytro Perekrestenko
  • Gruening, Simon, "Numerical Experiments on Deep Neural Network Function Approximation," Fall semester 2018
    Supervisor(s): Dmytro Perekrestenko
  • Sridmar, Gautam, "Optimal Approximation with ReLU Neural Networks," Spring semester 2018
    Supervisor(s): Dmytro Perekrestenko
  • Montazeri, Kristófer, "Efficient Neural Networks for Keyword Spotting," Spring semester 2018
    Supervisor(s): Michael Tschannen, Dmytro Perekrestenko
  • Ruffiner, Yannick, "Convolutional Recurrent Neural Networks for Electrocardiogram Classification," Fall semester 2017
    Supervisor(s): Dmytro Perekrestenko, Michael Tschannen
  • Laumer, Fabian, "Convolutional Recurrent Neural Networks for Heart Sound Segmentation," Spring semester 2017
    Supervisor(s): Michael Tschannen, Dmytro Perekrestenko
  • Zihlmann, Martin, "A Convolutional Recurrent Neural Network for Atrial Fibrillation Detection in Single Lead ECGs," Spring semester 2017
    Supervisor(s): Michael Tschannen, Dmytro Perekrestenko