Sergey Nikolenko
Sergey Nikolenko
Steklov Institute of Mathematics at St. Petersburg, Russia
Verified email at - Homepage
Cited by
Cited by
SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing
A Bankevich, S Nurk, D Antipov, AA Gurevich, M Dvorkin, AS Kulikov, ...
Journal of computational biology 19 (5), 455-477, 2012
Глубокое обучение
С Николенко, А Кадурин, Е Архангельская
" Издательский дом"" Питер""", 2017
druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico
A Kadurin, S Nikolenko, K Khrabrov, A Aliper, A Zhavoronkov
Molecular pharmaceutics 14 (9), 3098-3104, 2017
BayesHammer: Bayesian clustering for error correction in single-cell sequencing
SI Nikolenko, AI Korobeynikov, MA Alekseyev
BMC genomics 14 (1), 1-11, 2013
Байесовские сети: логико-вероятностный подход
АЛ Тулупьев, СИ Николенко, АВ Сироткин
Наука, 2006
Molecular sets (MOSES): a benchmarking platform for molecular generation models
D Polykovskiy, A Zhebrak, B Sanchez-Lengeling, S Golovanov, O Tatanov, ...
Frontiers in pharmacology 11, 565644, 2020
Глубокое обучение. Погружение в мир нейронных сетей
С Николенко, А Кадурин, Е Архангельская
FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry
A Palmer, P Phapale, I Chernyavsky, R Lavigne, D Fay, A Tarasov, ...
Nature methods 14 (1), 57-60, 2017
Topic modelling for qualitative studies
SI Nikolenko, S Koltcov, O Koltsova
Journal of Information Science 43 (1), 88-102, 2017
Tooth detection and numbering in panoramic radiographs using convolutional neural networks
DV Tuzoff, LN Tuzova, MM Bornstein, AS Krasnov, MA Kharchenko, ...
Dentomaxillofacial Radiology 48 (4), 20180051, 2019
Synthetic data for deep learning
SI Nikolenko
Springer Nature, 2021
Байесовские сети доверия: логико-вероятностный вывод в ациклических направленных графах
АЛ Тулупьев, АВ Сироткин, СИ Николенко
Изд-во Петербургского университета, 2009
Synthetic data for deep learning
SI Nikolenko
arXiv preprint arXiv:1909.11512, 2019
Recvae: A new variational autoencoder for top-n recommendations with implicit feedback
I Shenbin, A Alekseev, E Tutubalina, V Malykh, SI Nikolenko
Proceedings of the 13th international conference on web search and data …, 2020
Deep learning
S Nikolenko, A Kadurin, E Arkhangelskaya
SPb.: Peter, 2018
Самообучающиеся системы
СИ Николенко, АЛ Тулупьев
М.: МНЦМО, 2009
Land cover classification from satellite imagery with u-net and lovász-softmax loss
A Rakhlin, A Davydow, S Nikolenko
Proceedings of the IEEE conference on computer vision and pattern …, 2018
3D molecular representations based on the wave transform for convolutional neural networks
D Kuzminykh, D Polykovskiy, A Kadurin, A Zhebrak, I Baskov, S Nikolenko, ...
Molecular pharmaceutics 15 (10), 4378-4385, 2018
SAX-PAC (scalable and expressive packet classification)
K Kogan, S Nikolenko, O Rottenstreich, W Culhane, P Eugster
Proceedings of the 2014 ACM Conference on SIGCOMM, 15-26, 2014
Latent dirichlet allocation: stability and applications to studies of user-generated content
S Koltcov, O Koltsova, S Nikolenko
Proceedings of the 2014 ACM conference on Web science, 161-165, 2014
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