Leonid E Zhukov

Publications³³

• Chapter Tsvigun A., Shelmanov A., Kuzmin G., Sanochkin L., Larionov D., Gusev G., Avetisyan M., Zhukov L. E. Towards Computationally Feasible Deep Active Learning, in: Findings of the Association for Computational Linguistics: NAACL 2022. Seattle : Association for Computational Linguistics, 2022. P. 1198-1218. doi
• Chapter Vazhentsev A., Kuzmin G., Shelmanov A., Tsvigun A., Zhukov L. E. Uncertainty Estimation of Transformer Predictions for Misclassification Detection, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Vol. 1: Long Papers. Association for Computational Linguistics, 2022. P. 8237-8252. doi
• Article Lomov I., Lyubimov M., Makarov I., Zhukov L. E. Fault detection in Tennessee Eastman process with temporal deep learning models // Journal of Industrial Information Integration. 2021. Vol. 23. Article 100216. doi
• Chapter Anna Averchenkova, Alina Akhmetzyanova, Sudarikov K., Stanislav Petrov, Makarov I., Pendiukhov M., Zhukov L. E. Collaborator Recommender System, in: Network Algorithms, Data Mining, and Applications. Springer Proceedings in Mathematics & Statistics. Springer, 2020. doi P. 101-119. doi
• Article Makarov I., Gerasimova O., Sulimov P., Zhukov L. E. Dual network embedding for representing research interests in the link prediction problem on co-authorship networks // PeerJ Computer Science. 2019. P. 1-20. doi
• Chapter Makarov I., Gerasimova O., Sulimov P., Zhukov L. E. Application of Graph Embedding to Constructing Graph-based Recommender System, in: Proceedings of WebSci’18 Main Conference Poster Session. Aachen : CEUR Workshop Proceedings, 2018. Ch. 1. P. 1-2. (in press)
• Chapter Makarov I., Gerasimova O., Sulimov P., Zhukov L. E. Co-authorship Network Embedding and Recommending Collaborators via Network Embedding, in: Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science / Ed. by W. M. van der Aalst, V. Batagelj, G. Glavaš,, D. I. Ignatov, M. Khachay, O. Koltsova, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch,, A. Napoli,, A. Savchenko, A. Panchenko,, P. M. Pardalos, M. Pelillo,. Vol. 11179. Berlin : Springer, 2018. doi P. 32-38. doi
• Chapter Kostyakova Nadezhda, Karpov I., Makarov I., Zhukov L. E. Commercial Astroturfing Detection in Social Networks, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics Vol. 247. Springer, 2018. doi P. 309-318. doi
• Chapter Laptsuev Rodion, Ananyeva Marina, Meinster Dmitry, Karpov I., Makarov I., Zhukov L. E. Information Propagation Strategies in Online Social Networks, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics Vol. 247. Springer, 2018. doi P. 319-328. doi
• Chapter Makarov I., Gerasimova O., Sulimov P., Ksenia Korovina, Zhukov L. E. Joint Node-Edge Network Embedding for Link Prediction, in: Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science / Ed. by W. M. van der Aalst, V. Batagelj, G. Glavaš,, D. I. Ignatov, M. Khachay, O. Koltsova, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch,, A. Napoli,, A. Savchenko, A. Panchenko,, P. M. Pardalos, M. Pelillo,. Vol. 11179. Berlin : Springer, 2018. doi P. 20-31. doi
• Chapter Makarov I., Gerasimova O., Sulimov P., Zhukov L. E. Recommending Co-authorship via Network Embeddings and Feature Engineering: The case of National Research University Higher School of Economics, in: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries. NY : Association for Computing Machinery (ACM), 2018. P. 365-366. doi
• Chapter Makarov I., Bulanov O., Olga Gerasimova, Natalia Meshcheryakova, Karpov I., Zhukov L. E. Scientific Matchmaker: Collaborator Recommender System, in: Analysis of Images, Social Networks and Texts. 6th International Conference, 2017, Revised Selected Papers / Ed. by W. M. van der Aalst, D. I. Ignatov, M. Khachay, S. Kuznetsov, V. Lempitsky, I. A. Lomazova, A. Napoli, A. Panchenko, P. M. Pardalos, A. V. Savchenko, S. Wasserman. Vol. 10716. Cham : Springer, 2018. doi P. 404-410. doi
• Chapter Kurmukov A., Dodonova Y., Burova M., Mussabayeva A., Petrov D., Faskowitz J., Zhukov L. E. Topological modules of human brain networks are anatomically embedded: evidence from modularity analysis at multiple scales, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics Vol. 247. Springer, 2018. doi P. 299-308. doi
• Article Kurmukov A., Ananyeva M., Dodonova Y., Zhukov L. E. Classifying Phenotypes Based on the Community Structure of Human Brain Networks // Lecture Notes in Computer Science. 2017. P. 3-11. doi (in press)
• Chapter Makarov I., Bulanov O., Zhukov L. E. Co-author Recommender System, in: Models, Algorithms, and Technologies for Network Analysis. Springer Proceedings in Mathematics & Statistics / Ed. by V. A. Kalyagin, A. I. Nikolaev, P. M. Pardalos, O. Prokopyev. Vol. 197. Springer, 2017. doi P. 251-257. doi
• Chapter Kurmukov A., Dodonova Y., Zhukov L. E. Machine learning application to human brain network studies: a kernel approach, in: Models, Algorithms, and Technologies for Network Analysis. Springer Proceedings in Mathematics & Statistics / Ed. by V. A. Kalyagin, A. I. Nikolaev, P. M. Pardalos, O. Prokopyev. Vol. 197. Springer, 2017. doi P. 229-249.
• Chapter Rustem M. Khayrullin, Makarov I., Zhukov L. E. Predicting Psychology Attributes of a Social Network User, in: Proceedings of the Fourth Workshop on Experimental Economics and Machine Learning (EEML 2017), Dresden, Germany, September 17-18, 2017 / Ed. by R. Tagiew, D. I. Ignatov, A. Hilbert, K. Heinrich, R. Delhibabu. Vol. 1968. Aachen : CEUR Workshop Proceedings, 2017. P. 2-8.
• Article Ананьева М. Е., Курмуков А. И., Додонова Ю. А., Жуков Л. Е., Гутман Б., Фасковиц Дж., Джаханшад Н., Томпсон П. Оценивание сходства разбиений графов на пересекающиеся сообщества // Сборник трудов 41-й междисциплинарной школы-конференции ИППИ РАН "Информационные технологии и системы 2017". 2017. С. 7-15. (in press)
• Chapter Petrov D., Dodonova Y., Zhukov L. E., Belyaev M. Boosting connectome classification via combination of geometric and topological normalizations, in: PRNI 2016. The 6th International Workshop on Pattern Recognition in Neuroimaging. Trento, Italy, June 22nd – 24th, 2016. NY : IEEE, 2016. P. 1-4. doi
• Chapter Kurmukov A., Dodonova Y., Zhukov L. E. Classification of normal and pathological brain networks based on similarity in graph partitions, in: 16th IEEE International Conference on Data Mining Workshops (ICDMW). NY : IEEE Computer Society, 2016. P. 107-112. doi
• Chapter Dodonova Y., Korolev S., Tkachev A., Petrov D., Zhukov L. E., Belyaev M. Classification of structural brain networks based on information divergence of graph spectra, in: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). NY : IEEE, 2016. doi doi
• Preprint Dodonova Y., Belyaev M., Tkachev A., Petrov D., Zhukov L. E. Kernel classification of connectomes based on earth mover's distance between graph spectra / Cornell University Library. 2016.
• Chapter Dodonova Y., Belyaev M., Tkachev A., Petrov D., Zhukov L. E. Yulia Dodonova, Mikhail Belyaev, Anna Tkachev, Dmitry Petrov, Leonid Zhukov. Kernel Classification Of Connectomes Based On Earth Mover’s Distance Between Graph Spectra, in BACON: Workshop on Brain Analysis using Connectivity Networks / MICCAI 2016, in: Proceedings of the 19th International Conference on Medical Image Computing and Computer Assisted Intervention, October 17-21, 2016, Athens, Greece, Springer. Athens : Springer, 2016. Ch. 5. P. 1-10.
• Chapter Курмуков А. И., Додонова Ю. А., Жуков Л. Е. Классификация расстройств аутистического спектра и нормального развития на основе сходства разбиений сетевых структур мозга // В кн.: Сборник статей конференции "Информационные технологии и системы" (ИТиС'16). М. : ИППИ РАН, 2016. С. 501-507.
• Chapter Petrov D., Dodonova Y., Zhukov L. E. Differences in Structural Connectomes between Typically Developing and Autism Groups, in: "Информационные технологии и системы 2015". St. Petersburg : Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. P. 1-15.
• Chapter Korolev S., Zhukov L. E. Supervised Learning for Link Prediction Using Similarity Indices, in: "Информационные технологии и системы 2015" 39-я междисциплинарная школа-конференция 7 – 11 сентября, Олимпийская деревня, Сочи, Россия. St. Petersburg : Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. P. 1-8.
• Chapter Додонова Ю., Петров Д., Жуков Л. Е. Сравнение эффективности ядер SVM-классификатора для различения пола на основе структурных коннектом // В кн.: "Информационные технологии и системы 2015" 39-я междисциплинарная школа-конференция 7 – 11 сентября, Олимпийская деревня, Сочи, Россия. St. Petersburg : Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. С. 1-13.
• Article Zhukov L. E., Sukharev J., Popescul A. Learning Alternative Name Spellings // Information Retrieval. 2014
• Chapter Zhukov L. E., Sukharev J., Popescul A. Parallel corpus approach for name matching in record linkage, in: Proceedings of 14th International Conference on Data Mining (ICDM 2014). NY : IEEE Computer Society, 2014. P. 995-1000.
• Article Ignatov D. I., Kuznetsov S., Zhukov L. E., Poelmans J. Can triconcepts become triclusters? // International Journal of General Systems. 2013. Vol. 42. No. 6. P. 572-593. doi
• Chapter Жуков Л. Е. Профессия Data Scientist // В кн.: Большие Данные в национальной экономике. М. : ЗАО "Открытые системы", 2013. С. 48-51.
• Chapter Гнатышак Д. В., Игнатов Д. И., Жуков Л. Е., Кузнецов С. О., Миркин Б. Г. Экспериментальное сравнение некоторых алгоритмов трикластеризации // В кн.: Интеллектуализация обработки информации ИОИ: 9-я международная конференция. Черногория, г. Будва, 2012 г.: сборник докладов / Отв. ред.: К. В. Воронцов. М. : Торус Пресс, 2012. С. 609-612.
• Chapter Ignatov D. I., Kuznetsov S., Zhukov L. E. From Triconcepts to Triclusters, in: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 13th International Conference, RSFDGrC 2011, Moscow, Russia, June 25-27, 2011. Proceedings / Ed. by S. Kuznetsov, D. Slezak, D. H. Hepting, B. Mirkin. Vol. 6743. Berlin, Heidelberg : Springer, 2011. P. 257-264.