From df15f4f158cf0d867da539abb123f49958a34258 Mon Sep 17 00:00:00 2001 From: nicl-nno Date: Mon, 5 Aug 2024 13:11:24 +0300 Subject: [PATCH] New citation --- README.rst | 29 ++++++++--------------------- README_en.rst | 32 +++++++++----------------------- 2 files changed, 17 insertions(+), 44 deletions(-) diff --git a/README.rst b/README.rst index 5ccf5641..084ddf9c 100644 --- a/README.rst +++ b/README.rst @@ -167,29 +167,16 @@ GOLEM можно установить с помощью ``pip``: Цитирование =========== -Если вы используете наш проект в своей работе или исследовании, мы будем признательны за цитирование. +Если вы используете наш проект в своей работе или исследовании, мы будем признательны за цитирование: -@article{nikitin2021automated, - title = {Automated evolutionary approach for the design of composite machine learning pipelines}, - author = {Nikolay O. Nikitin and Pavel Vychuzhanin and Mikhail Sarafanov and Iana S. Polonskaia and Ilia Revin and Irina V. Barabanova and Gleb Maximov and Anna V. Kalyuzhnaya and Alexander Boukhanovsky}, - journal = {Future Generation Computer Systems}, - year = {2021}, - issn = {0167-739X}, - doi = {https://doi.org/10.1016/j.future.2021.08.022}} +@inproceedings{pinchuk2024golem, + title={GOLEM: Flexible Evolutionary Design of Graph Representations of Physical and Digital Objects}, + author={Pinchuk, Maiia and Kirgizov, Grigorii and Yamshchikova, Lyubov and Nikitin, Nikolay and Deeva, Irina and Shakhkyan, Karine and Borisov, Ivan and Zharkov, Kirill and Kalyuzhnaya, Anna}, + booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, + pages={1668--1675}, + year={2024} +} -Публикации, описывающие применение GOLEM для прикладных задач: -============================================================== - -В данных публикациях описывается применение алгоритмов GOLEM и основанных на нем решений -для различных прикладных задач. - -- Алгоритмы поиска оптимального пайплайна машинного обучения для прогнозирования временных рядов: Sarafanov M., Pokrovskii V., Nikitin N. O. Evolutionary Automated Machine Learning for Multi-Scale Decomposition and Forecasting of Sensor Time Series //2022 IEEE Congress on Evolutionary Computation (CEC). – IEEE, 2022. – С. 01-08. - -- Алгоритмы идентификации структуры уравнения для акустических данных: Hvatov A. Data-Driven Approach for the Floquet Propagator Inverse Problem Solution //ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). – IEEE, 2022. – С. 3813-3817. - -- Алгоритмы идентификации структуры дифференциальных уравнений в частных производных: Maslyaev M., Hvatov A. Solver-Based Fitness Function for the Data-Driven Evolutionary Discovery of Partial Differential Equations //2022 IEEE Congress on Evolutionary Computation (CEC). – IEEE, 2022. – С. 1-8. - -- Алгоритмы структурного обучения сетей: Deeva I., Kalyuzhnaya A. V., Alexander V. Boukhanovsky Adaptive Learning Algorithm for Bayesian Networks Based on Kernel Mixtures Distributions//International Journal of Artificial Intelligence. – 2023. - Т.21. - №. 1. - С. 90. .. |docs| image:: https://readthedocs.org/projects/thegolem/badge/?version=latest :target: https://thegolem.readthedocs.io/en/latest/?badge=latest diff --git a/README_en.rst b/README_en.rst index 01dba7ce..7abd34d9 100644 --- a/README_en.rst +++ b/README_en.rst @@ -167,29 +167,15 @@ Contacts Citation ======== -If you use our project in your work or research, we would appreciate citations. - -@article{nikitin2021automated, - title = {Automated evolutionary approach for the design of composite machine learning pipelines}, - author = {Nikolay O. Nikitin and Pavel Vychuzhanin and Mikhail Sarafanov and Iana S. Polonskaia and Ilia Revin and Irina V. Barabanova and Gleb Maximov and Anna V. Kalyuzhnaya and Alexander Boukhanovsky}, - journal = {Future Generation Computer Systems}, - year = {2021}, - issn = {0167-739X}, - doi = {https://doi.org/10.1016/j.future.2021.08.022}} - -Papers that describe applications of GOLEM: -=========================================== - -There are various cases solved with GOLEM's algorithms: - -- Algorithms for time series forecasting pipeline design: Sarafanov M., Pokrovskii V., Nikitin N. O. Evolutionary Automated Machine Learning for Multi-Scale Decomposition and Forecasting of Sensor Time Series //2022 IEEE Congress on Evolutionary Computation (CEC). – IEEE, 2022. – С. 01-08. - -- Algorithms for acoustic equation discovery: Hvatov A. Data-Driven Approach for the Floquet Propagator Inverse Problem Solution //ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). – IEEE, 2022. – С. 3813-3817. - -- Algorithms for PDE discovery: Maslyaev M., Hvatov A. Solver-Based Fitness Function for the Data-Driven Evolutionary Discovery of Partial Differential Equations //2022 IEEE Congress on Evolutionary Computation (CEC). – IEEE, 2022. – С. 1-8. - -- Algorithms for structural learning of Bayesian Networks: Deeva I., Kalyuzhnaya A. V., Alexander V. Boukhanovsky Adaptive Learning Algorithm for Bayesian Networks Based on Kernel Mixtures Distributions//International Journal of Artificial Intelligence. – 2023. - Т.21. - №. 1. - С. 90. - +If you use our project in your work or research, we would appreciate citations: + +@inproceedings{pinchuk2024golem, + title={GOLEM: Flexible Evolutionary Design of Graph Representations of Physical and Digital Objects}, + author={Pinchuk, Maiia and Kirgizov, Grigorii and Yamshchikova, Lyubov and Nikitin, Nikolay and Deeva, Irina and Shakhkyan, Karine and Borisov, Ivan and Zharkov, Kirill and Kalyuzhnaya, Anna}, + booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, + pages={1668--1675}, + year={2024} +} .. |docs| image:: https://readthedocs.org/projects/thegolem/badge/?version=latest :target: https://thegolem.readthedocs.io/en/latest/?badge=latest