[THE INFORMATION ON THIS PAGE IS OUTDATED; the update is in progress.]
One can see group members' publications on their homepages listed in staff section.

Relevant books/papers by group members:

  1. S.I. Nikolenko, A. Kadurin, E. Arkhangelskaya. Deep Learning. Piter Publishing House, 2017 (in Russian).
  2. A. Kadurin, S.I. Nikolenko, K. Khrabrov, A. Aliper, A. Zhavoronkov. druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. Molecular Pharmaceutics, vol. 14, no. 9, 2017, pp. 3098–3104.
  3. S.I. Nikolenko. Topic Quality Metrics Based on Distributed Word Representations. Proc. 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), 2016, pp. 1029–1032
  4. D. Ignatov, S.I. Nikolenko, T. Abaev, J. Poelmans. Online Recommender System for Radio Station Hosting Based on Information Fusion and Adaptive Tag-Aware Profiling. Expert Systems with Applications, vol. 55, 2016, pp. 546–558.
  5. S.I. Nikolenko. SVD-LDA: Topic Modeling for Full-Text Recommender Systems. Proc. 14th Mexican International Conference on Artificial Intelligence ( MICAI 2015), Lecture Notes in Computer Science, vol. 9414, Springer, 2015, pp. 67–79
  6. V. Leksin, S.I. Nikolenko. Semi-Supervised Tag Extraction in a Web Recommender System. Proc. 6th International Conference on Similarity Search and Applications ( SISAP 2013), Lecture Notes in Computer Science, vol. 8199, Springer, 2013, pp. 206–212


Deep Learning for Textual User Modeling and Recommendations


  1. E. Tutubalina, S.I. Nikolenko. Demographic Prediction based on User Reviews about Medications. Computación y Sistemas, vol. 21, no. 2, 2017, pp. 227–241
  2. E. Tutubalina, S.I. Nikolenko. Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews. Journal of Healthcare Engineering, vol. 2017, Article ID 9451342, 2017, 9 pp.
  3. A. Alekseyev, S.I. Nikolenko. Word Embeddings of User Profiling in Online Social Networks. Computación y Sistemas, vol. 21, no. 2, 2017, pp. 203–226.
  4. E. Tutubalina, S.I. Nikolenko. Constructing Aspect-Based Sentiment Lexicons with Topic Modeling. Proc. 5th International Conference on Analysis of Images, Social Networks, and Texts ( AIST 2016), 2016, pp. 208–220
  5. S.I. Nikolenko, A. Alekseyev. User Profiling in Text-Based Recommender Systems Based on Distributed Word Representations. Proc. 5th International Conference on Analysis of Images, Social Networks, and Texts ( AIST 2016), 2016, pp. 196–207

User Preference Prediction in Visual Data


  1. Savchenko A.V. Search Techniques in Intelligent Classification Systems. Switzerland: Springer International Publishing, 2016, http://www.springer.com/in/book/9783319305134
  2. Savchenko A.V. Maximum-Likelihood Approximate Nearest Neighbor Method in Real-time Image Recognition, Pattern Recognition, 2017, vol. 61, pp. 459-469, http://www.sciencedirect.com/science/article/pii/S003132031630228X
  3. Savchenko A.V. Deep neural networks and maximum likelihood search for approximate nearest neighbor in video-based image recognition, Optical Memory and Neural Networks (Information Optics), 2017, vol. 26, no. 2, pp. 129–136, https://link.springer.com/article/10.3103/S1060992X17020102
  4. Savchenko A.V. Clustering and maximum likelihood search for efficient statistical classification with medium-sized databases, Optimization Letters, 2017, vol. 11(2), pp. 329-341, https://link.springer.com/article/10.1007/s11590-015-0948-6
  5. Savchenko, A.V. Fast multi-class recognition of piecewise regular objects based on sequential three-way decisions and granular computing, Knowledge-Based Systems, 2016, vol. 91, pp. 250–260, http://www.sciencedirect.com/science/article/pii/S0950705115003603
  6. Savchenko A.V., Belova N.S. Statistical testing of segment homogeneity in classification of piecewise-regular objects, International Journal of Applied Mathematics and Computer Science, 2015, vol. 25 (4), pp. 915-925, https://www.degruyter.com/view/j/amcs.2015.25.issue-4/amcs-2015-0065/amcs-2015-0065.xml
  7. Savchenko A.V., Savchenko L.V. Towards the creation of reliable voice control system based on a fuzzy approach, Pattern Recognition Letters, 2015, vol. 65, pp. 145-151, http://www.sciencedirect.com/science/article/pii/S0167865515002226.
  8. Savchenko A.V., KhokhlovaYa.I. About Neural-Network Algorithms Application in Viseme Classification Problem With Face Video in Audiovisual Speech Recognition Systems, Optical Memory and Neural Networks (Information Optics), 2014, vol. 23, no. 1, pp. 34–42, https://link.springer.com/article/10.3103/S1060992X14010068
  9. Savchenko A.V., Probabilistic neural network with homogeneity testing in recognition of discrete patterns set, Neural Networks, 2013, vol. 46, pp. 227-241, http://www.sciencedirect.com/science/article/pii/S0893608013001652
  10. Savchenko A.V. Directed enumeration method in image recognition, Pattern Recognition, 2012, 45(8), pp. 2952-2961, http://www.sciencedirect.com/science/article/pii/S0031320312000738
  11. Savchenko A.V. Deep Convolutional Neural Networks and Maximum-Likelihood Principle in Approximate Nearest Neighbor Search. In: Alexandre L., Salvador Sánchez J., Rodrigues J. (eds) Iberian Conference on Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science, vol 10255, pp. 42-49. Springer, Cham (2017), https://link.springer.com/chapter/10.1007/978-3-319-58838-4_5
  12. Shipova K.G., Savchenko A.V. (2016) Video-Based Pedestrian Detection on Mobile Phones with the Cascade Classifiers. In: Kalyagin V., Koldanov P., Pardalos P. (eds) Models, Algorithms and Technologies for Network Analysis. Springer Proceedings in Mathematics & Statistics, vol 156. Springer, Cham, https://link.springer.com/chapter/10.1007/978-3-319-29608-1_15
  13. Rassadin A.G., Gruzdev A.S., Savchenko A.V. Group-level Emotion Recognition using Transfer Learning from Face Identification, ACM ICMI 2017, pp. 544-548, https://dl.acm.org/citation.cfm?id=3143007
  14. Rassadin A. G., Savchenko A. V. Compressing deep convolutional neural networks in visual emotion recognition, Proceedings of the International Conference on Information Technology and Nanotechnology (ITNT). Session Image Processing, Geoinformation Technology and Information Security Image Processing (IPGTIS), 2017, CEUR-WS, vol. 1901, pp. 207-213, http://ceur-ws.org/Vol-1901/paper33.pdf