Nowe metody uczenia reprezentacji w Deep Learningu

2017/24/T/ST6/00487

Descriptors

  • ST6_7: Sztuczna inteligencja, systemy inteligentne i wieloagentowe
  • ST6_11: Uczenie maszynowe, statystyczne przetwarzanie danych i zastosowanie w przetwarzaniu sygnałów

Panel

ST6 – Computer science and informatics: informatics and information systems, computer science, scientific computing, intelligent systems

Host institution

Uniwersytet Jagielloński, Wydział Matematyki i Informatyki

Voivodeship: małopolskie

Other projects carried out by the institution »

Principal investigator (from the host institution)

Stanisław Kamil Jastrzebski

Number of co-investigators in the project: 2

Call

ETIUDA 5 – announced on 2016-12-15

Amount awarded
63 481 PLN
Project start date
2017-10-02
Project end date
2018-04-01
Project duration
6 months (the same as in the proposal)
Project status
Project settled

Project description

Download the project description in a pdf file

Note — project descriptions were prepared by the authors of the applications themselves and placed in the system in an unchanged form.

Information in the final report

Articles in post-conference publications (3)

  1. On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

    Authors:
    S. Jastrzębski, Z. Kenton, N. Ballas, A. Fischer, Y. Bengio, A. Storkey
    Conference:
    International Conference on Learning Representations 2019 (year: 2019, pages: N/A), Publisher: N/A
    Date:
    conference 06.05.2019-09.05.2019
    Status:
    Published
    DOI:
    link to the publication
  2. Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function

    Authors:
    W. Tarnowski, P. Warchoł, S. Jastrzębski, J. Tabor, M. A. Nowak
    Conference:
    The 22nd International Conference on Artificial Intelligence and Statistics (year: 2019, pages: 2221-2230), Publisher: PMLR
    Date:
    conference 16-18.04.2019
    Status:
    Published
    DOI:
    link to the publication
  3. Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio

    Authors:
    S. Jastrzębski, Z. Kenton, D. Arpit, N. Ballas, A. Fischer, Y. Bengio, A. Storkey
    Conference:
    Artificial Neural Networks and Machine Learning – ICANN 2018 (year: 2018, pages: 392--402), Publisher: Springer International Publishing
    Date:
    conference 4-7.10.2018
    Status:
    Published

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