Faculty Publications

Below you can find a comprehensive list of published works of CDISE faculty (last update: July 2022):
Q1 journal publications and A/A*-rank conference proceedings are marked in bold.

20222021202020192018201720162015

Bondarenko, A., Fröbe, M, Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., … , Hagen, M. (2022). Overview of Touché 2022: Argument Retrieval: Extended Abstract. Lecture Notes in Computer Science, vol. 13186, pp. 339 – 346
Che, H., Wang, J., & Cichocki, A. (2022). Sparse signal reconstruction via collaborative neurodynamic optimization. Neural Networks, vol. 154, pp. 255 – 269.
Duplyakov, V. M., Morozov, A. D., Popkov, D. O., Shel, E. V., Vainshtein, A. L., Burnaev, E. V., … & Paderin, G. V. (2022). Data-driven model for hydraulic fracturing design optimization. Part II: Inverse problem. Journal of Petroleum Science and Engineering, vol. 208, 109303.
El Daibani, A., Paggi, J., Kim, K., Laloudakis, Y., Popov, P., Bernhard, S., … & Che, T. (2022). Structure Insights into Biased Signaling of kappa Opioid Receptor. The FASEB Journal, vol. 36.
Faria, B. F., Palyulin, V. V., & Vishnyakov, A. M. (2022). Free Energies of Polymer Brushes With Mobile Anchors in a Good Solvent Calculated with the Expanded Ensemble Method. Colloids and Surfaces A: Physicochemical and Engineering Aspects, p. 129443.
Gómez, A., Palyulin, V. V., Ryzhakov, G. V., Brilliantov, N. V., Dubrovin, E. V., Verdaguer, A., & Sort, J. (2022). Measurement of stress distribution at the nanoscale: Towards stress nanotomography. Journal of the Mechanics and Physics of Solids, vol. 164, p. 104895.
Illarionova, S., Shadrin, D., Ignatiev, V., Shayakhmetov, S., Trekin, A., & Oseledets, I. (2022). Estimation of the Canopy Height Model From Multispectral Satellite Imagery With Convolutional Neural Networks. IEEE Access, vol. 10, pp. 34116-34132.
Illarionova, S., Shadrin, D., Ignatiev, V., Shayakhmetov, S., Trekin, A., & Oseledets, I. (2022). Augmentation-Based Methodology for Enhancement of Trees Map Detalization on a Large Scale. Remote Sensing, vol. 14(9), p. 2281.
Kalinov, A., Osinsky, A. I., Matveev, S. A., Otieno, W., & Brilliantov, N. V. (2022). Direct simulation Monte Carlo for new regimes in aggregation-fragmentation kinetics. Journal of Computational Physics, vol. 467, p. 111439.
Karlov, D., Temnyakova, N., Vasilenko, D., Barygin, O., Dron, M., …, Kostyukevich, Y., Popov, P. (2022). Biphenyl scaffold for the design of NMDA-receptor negative modulators: molecular modeling, synthesis, and biological activity. RSC Medicinal Chemistry.
Kurilovich, A. A., Mantsevich, V. N., Mardoukhi, Y., Stevenson, K. J., Chechkin, A. V., & Palyulin, V. V. (2022). Non-Markovian diffusion of excitons in layered perovskites and transition metal dichalcogenides. Physical Chemistry Chemical Physics, vol. 24(22), pp. 13941-13950.
Klyuchnikov, N., Trofimov, I., Artemova, E., Salnikov, M., Fedorov, M., Filippov, A., & Burnaev, E. (2022). NAS-Bench-NLP: neural architecture search benchmark for natural language processing. IEEE Access, vol. 10, pp. 45736-45747.
Kostyukevich, Y., Sosnin, S., Osipenko, S., Kovaleva, O., Rumiantseva, L., Kireev, A., Fedorov, M. V., … & Nikolaev, E. N. (2022). PyFragMS─ A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways. ACS omega, vol. 7(11), pp. 9710-9719.
Kovalenko, E., Shcherbak, A., Somov, A., Bril, E., Zimniakova, O., Semenov, M., & Samoylov, A. (2022). Detecting the Parkinson’s Disease Through the Simultaneous Analysis of Data From Wearable Sensors and Video. IEEE Sensors Journal, v. 22(16), pp. 16430-16439.
Laselva, O., Petrotchenko, E. V., Hamilton, C. M., Qureshi, Z., Borchers, C. H., Young, R. N., & Bear, C. E. (2022). A protocol for identifying the binding sites of small molecules on the cystic fibrosis transmembrane conductance regulator (CFTR) protein. STAR protocols, vol. 3(2), p. 101258.
Li, S., Jin, J., Daly, I., Liu, C., & Cichocki, A. (2022). Feature selection method based on Menger curvature and LDA theory for a P300 brain–computer interface. Journal of Neural Engineering, vol. 18(6), p. 066050.
Liu, C., Jin, J., Daly, I., Sun, H., Huang, Y., Wang, X., & Cichocki, A. (2022). Bispectrum-based hybrid neural network for motor imagery classification. Journal of Neuroscience Methods, vol. 375, p. 109593.
Lyapina, E., Marin, E., Gusach, A., Orekhov, P., Gerasimov, A., Luginina, A., …, Popov, P. …, & Cherezov, V. (2022). Structural basis for receptor selectivity and inverse agonism in S1P5 receptors. Nature communications, v.3, iss.1.
Mazyavkina, N., Moustafa, S., Trofimov, I., & Burnaev, E. (2021). Optimizing the Neural Architecture of Reinforcement Learning Agents. In Intelligent Computing (pp. 591-606). Springer, Cham.
Minin, I. B., Matveev, S. A., Fedorov, M. V., Zacharov, I. E., & Rykovanov, S. G. (2022). Benchmarks of Cuda-Based GMRES Solver for Toeplitz and Hankel Matrices and Applications to Topology Optimization of Photonic Components. Computational Mathematics and Modeling, vol. 32(4), pp. 438 – 452.
Mohammed, Y., Touw, C. E., Nemeth, B., van Adrichem, R. A., Borchers, C. H., Rosendaal, F. R., … & Cannegieter, S. C. (2022). Targeted proteomics for evaluating risk of venous thrombosis following traumatic lower‐leg injury or knee arthroscopy. Journal of Thrombosis and Haemostasis.
Nesteruk, S., Illarionova, S., Akhtyamov, T., Shadrin, D., Somov, A., Pukalchik, M., & Oseledets, I. (2022). XtremeAugment: Getting More From Your Data Through Combination of Image Collection and Image Augmentation. IEEE Access, vol. 10, pp. 24010-24028.
Nikishina, I., Tikhomirov, M., Logacheva, V., Nazarov, Y., Panchenko, A., & Loukachevitch, N. (2022). Taxonomy Enrichment with Text and Graph Vector Representations. arXiv preprint arXiv:2201.08598.
Noskova, E. S., Zakharov, I. E., Shkandybin, Y. N., & Rykovanov, S. G. (2022). Towards energy-efficient neural network calculations. Computer Optics, vol. 46(1), pp. 160-166.
Novikov, A., Rakhuba, M., & Oseledets, I. (2022). Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds. SIAM Journal on Scientific Computing, vol. 44(2), pp. A843-A869.
Osinsky, A. I., & Brilliantov, N. V. (2022). Anomalous aggregation regimes of temperature-dependent Smoluchowski equations. Physical Review E, vol. 105(3), p. 034119.
Osinsky, A., & Brilliantov, N. (2022). Scaling laws in fragmentation kinetics. Physica A: Statistical Mechanics and its Applications, vol. 603, p. 127785.
Osipenko, S., Nikolaev, E., & Kostyukevich, Y. (2022). Amine additives for improved in-ESI H/D exchange. Analyst.
Pekov, S. I., Zhvansky, E. S., Eliferov, V. A., Sorokin, A. A., Ivanov, D. G., Nikolaev, E. N., & Popov, I. A. (2022). Determination of brain tissue samples storage conditions for reproducible intraoperative lipid profiling. Molecules, vol. 27(8), p. 2587.
Peng, Y., Zhang, Y., Kong, W., Nie, F., Lu, B. L., & Cichocki, A. (2022). S 3 LRR: A Unified Model for Joint Discriminative Subspace Identification and Semisupervised EEG Emotion Recognition. Transactions on Instrumentation and Measurement, vol. 71, pp. 1-13.
Rumiantseva, L., Osipenko, S., Zharikov, A., Kireev, A., Nikolaev, E. N., & Kostyukevich, Y. (2022). Analysis of 16O/18O and H/D Exchange Reactions between Carbohydrates and Heavy Water Using High-Resolution Mass Spectrometry. International Journal of Molecular Sciences, vol. 23(7), p. 3585.
Samardak, A. S., Ognev, A. V., Kolesnikov, A. G., Stebliy, M. E., Samardak, V. Y., Iliushin, I. G., Yudin, D. I., … & Rogalev, A. (2022). XMCD and ab initio study of interface-engineered ultrathin Ru/Co/W/Ru films with perpendicular magnetic anisotropy and strong Dzyaloshinskii–Moriya interaction. Physical Chemistry Chemical Physics, vol. 24(14), pp. 8225-8232.
Sevgili, O., Shelmanov, A., Arkhipov, M., Panchenko, A., & Biemann, C. (2020). Neural entity linking: A survey of models based on deep learning. arXiv preprint arXiv:2006.00575.
Shamraeva, M. A., Pekov, S. I., Bormotov, D. S., Levin, R. E., Larina, I. M., Nikolaev, E. N., & Popov, I. A. (2022). The lightweight spherical samplers for simplified collection, storage, and ambient ionization of drugs from saliva and blood. Acta Astronautica, vol. 195, pp. 556-560.
Shipitsin, V., Bespalov, I., & Dylov, D. V. (2022). GAFL: Global adaptive filtering layer for computer vision. Computer Vision and Image Understanding, v. 223, 103519.
Takahashi, K., Sun, Z., Solé-Casals, J., Cichocki, A., Phan, A. H., Zhao, Q., … & Micheletto, R. (2022). Data augmentation for Convolutional LSTM based brain computer interface system. Applied Soft Computing, v. 122, p. 108811.
Talitckii, A., Kovalenko, E., Shcherbak, A., Anikina, A., Bril, E., Zimniakova, O., Dylov, D., …. & Somov, A. (2022). Comparative Study of Wearable Sensors, Video, and Handwriting to Detect Parkinson’s Disease. IEEE Transactions on Instrumentation and Measurement.
Tsymboi, O., Kapushev, Y., Burnaev, E., & Oseledets, I. (2022). Denoising Score Matching via Random Fourier Features. IEEE Access, vol. 10, pp. 34154-34169.
Tursynbek, N., Petiushko, A., & Oseledets, I. (2022). Geometry-inspired top-k adversarial perturbations. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 3398-3407).
Yakimov, B. P., Rubekina, A. A., Zherebker, A. Y., Budylin, G. S., Kompanets, V. O., Chekalin, S. V., Nikolaev, E. N., … & Shirshin, E. A. (2022). Oxidation of Individual Aromatic Species Gives Rise to Humic-like Optical Properties. Environmental Science & Technology Letters.
Zakharova, N. V., Bugrova, A. E., Indeykina, M. I., Fedorova, Y. B., Kolykhalov, I. V., Gavrilova, S. I., Kononikhin, A. S. & Nikolaev. E. N. (2022). Proteomic Markers and Early Prediction of Alzheimer’s Disease. Biochemistry (Moscow), 87(8), 762-776.
Zimmerer, D., Full, P. M., Isensee, F., Jäger, P., Adler, T., Petersen, J., Dylov, D., … & Maier-Hein, K. (2022). MOOD 2020: A public Benchmark for Out-of-Distribution Detection and Localization on medical Images. IEEE Transactions on Medical Imaging.

Ahmadi-Asl, S., Abukhovich, S., Asante-Mensah, M.G., Cichocki, A., Phan, A.H., Tanaka, T., Oseledets, I. (2021). Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD). IEEE Access, vol 9, 9350569, pp. 28684-28706.
Andreev, K., & Frolov, A. (2021, October). On Unsourced Random Access for the MIMO Channel. In 2021 XVII International Symposium” Problems of Redundancy in Information and Control Systems”(REDUNDANCY) pp. 17-21. IEEE.
Andreev, K., Frolov, A., Svistunov, G., Wu, K., & Liang, J. (2021, October). Deep Neural Network Based Decoding of Short 5G LDPC Codes. In 2021 XVII International Symposium” Problems of Redundancy in Information and Control Systems”(REDUNDANCY) (pp. 155-160). IEEE.
Andreev, K., Rybin, P., & Frolov, A. (2021, October). Unsourced Random Access Based on List Recoverable Codes Correcting t Errors. In 2021 IEEE Information Theory Workshop (ITW) pp. 1-6. IEEE.
Appriou, A., Pillette, L., Trocellier, D., Dutartre, D., Cichocki, A., & Lotte, F. (2021). BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification. Sensors, vol. 21(17), p. 5740.
Babiloni, C., Arakaki, X., Azami, H., Bennys, K., Blinowska, K., Bonanni, L., … Cichocki, A. (2021). Measures of resting state EEG rhythms for clinical trials in Alzheimer’s disease: Recommendations of an expert panel. Alzheimer’s & Dementia.
Bachurina, V., Sushchinskaya, S., Sharaev, M., Burnaev, E., & Arsalidou, M. (2021). A machine learning investigation of factors that contribute to predicting cognitive performance: Difficulty level, reaction time and eye-movements. Decision Support Systems, p. 113713.
Balitskiy, G., Frolov, A., & Rybin, P. (2021, October). Linear Programming Decoding of Non-Linear Sparse-Graph Codes. In 2021 XVII International Symposium” Problems of Redundancy in Information and Control Systems”(REDUNDANCY) (pp. 149-154). IEEE.
Baranovsky, A., Ladyko, A., Shkel, T., Sokolov, S., Strushkevich, N., & Gilep, A. (2021). Transformations, NMR studies and biological testing of some 17β-isoxazolyl steroids and their heterocyclic ring cleavage derivatives. Steroids, vol. 166, 108768.
Batselier, K., Cichocki, A., & Wong, N. (2021). Meracle: constructive layer-wise conversion of a tensor train into a mera. Communications on Applied Mathematics and Computation, vol. 3(2), pp. 257-279.
Begicheva, M., & Zaytsev, A. (2021, December). Bank transactions embeddings help to uncover current macroeconomics. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1742-1748). IEEE.
Belikova, K., Rogov, O. Y., Rybakov, A., Maslov, M. V., & Dylov, D. V. (2021). Deep negative volume segmentation. Scientific Reports, vol. 11(1), pp. 1-11.
Bokhovkin, A., Ishimtsev, V., Bogomolov, E., Zorin, D., Artemov, A., Burnaev, E., & Dai, A. (2021). Towards Part-Based Understanding of RGB-D Scans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7484-7494.
Boyko, A. I., Oseledets, I. V., & Ferrer, G. (2021). TT-QI: Faster value iteration in tensor train format for stochastic optimal control. Computational Mathematics and Mathematical Physics, vol. 61(5), 836-846.
Brilliantov, N. V., Otieno, W., & Krapivsky, P. L. (2021). Nonextensive Supercluster States in Aggregation with Fragmentation. Physical review letters, vol. 127(25), p. 250602.
Burkina, M., Nazarov, I., Panov, M., Fedonin, G., & Shirokikh, B. (2021). Inductive Matrix Completion with Feature Selection. Computational Mathematics and Mathematical Physics, vol. 61(5), 719-732.
Burnaev, E.V. (2021). Bayesian Filtering in a Latent Space to Predict Bank Net Income from Acquiring. In Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers (p. 344). Springer Nature.
Burnaev, E.V. (2021). Time-Series Classification for Industrial Applications: Road Surface Damage Detection Use Case. Journal of Communications Technology and Electronics, vol. 65 (12), pp. 1491-1498.
Burnaev, E. V., Bernstein, A. V. (2021). Functional Dimension Reduction in Predictive Modeling. Journal of Communications Technology and Electronics, vol. 66(6), pp. 745-753.
Burnaev, E. V., Bernstein, A. V. (2021). Manifold Modeling in Machine Learning. Journal of Communications Technology and Electronics, vol. 66(6), pp. 754-763.
Butakov, I. D., Malanchuk, S. V., Neopryatnaya, A. M., Tolmachev, A. D., Andreev, K. V., Kruglik, S. A., … & Frolov, A. A. (2021). High-Dimensional Dataset Entropy Estimation via Lossy Compression. Journal of Communications Technology and Electronics, vol. 66(6), pp. 764-768.
Bychkov, R., Osinsky, A., Ivanov, A., & Yarotsky, D. (2021). Data-Driven Beams Selection for Beamspace Channel Estimation in Massive MIMO. In 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) (pp. 1-5). IEEE.
Chekalina, V., & Panchenko, A. (2021). Retrieving Comparative Arguments using Ensemble Methods and Neural Information Retrieval. CEUR Workshop Proceedings 2936, pp. 2354-2365.
Dale, D., Voronov, A., Dementieva, D., Logacheva, V., Kozlova, O., Semenov, N., & Panchenko, A. (2021). Text Detoxification using Large Pre-trained Neural Models. arXiv preprint arXiv:2109.08914.
Dementieva, D., Moskovskiy, D., Logacheva, V., Dale, D., Kozlova, O., Semenov, N., & Panchenko, A. (2021). Methods for Detoxification of Texts for the Russian Language.
Dementieva, D., Ustyantsev, S., Dale, D., Kozlova, O., Semenov, N., Panchenko, A., & Logacheva, V. (2021). Crowdsourcing of Parallel Corpora: the Case of Style Transfer for Detoxification.
Duan, F., Jia, H., Zhang, Z., Feng, F., Tan, Y., Dai, Y., Cichocki, A.,… & Solé-Casals, J. (2021). On the robustness of EEG tensor completion methods. Science China Technological Sciences, pp. 1-15.
Eliferov, V. A., Zhvansky, E. S., Sorokin, A. A., Shurkhay, V. A., Bormotov, D. S., Pekov, S. I., … Nikolaev, E.N., Popov, I. A. (2021). The Role of Lipids in the Classification of Astrocytoma and Glioblastoma Using Mass Spectrometry Tumor Profiling. Biochemistry (Moscow), Supplement Series B: Biomedical Chemistry, vol. 15(2), 153-160.
Ershov P.V., Kaluzhskiy L.A., Yablokov E.O., Gnedenko O.V., Kavaleuski A.A., Tumilovich A.M., Gilep A.A., Strushkevich N.V., Ivanov A.S. (2021). Application of the SPR biosensor for the analysis of protein—protein interactions in aqueous environment and bilayer lipid membrane as exemplified by P450scc (CYP11A1). Biologicheskie Membrany, vol. 2021 (1), pp. 71 – 80.
Faizullin, M., Kornilova, A., Akhmetyanov, A., Ferrer, G. (2021). Twist-n-sync: Software clock synchronization with microseconds accuracy using MEMS-gyroscopes. Sensors (Switzerland), vol. 21 (1), 68, pp. 1-19.
Fedorov, F. S., Yaqin, A., Krasnikov, D. V., Kondrashov, V. A., Ovchinnikov, G., Kostyukevich, Y., … & Nasibulin, A. G. (2021). Detecting cooking state of grilled chicken by electronic nose and computer vision techniques. Food Chemistry, vol. 345, 128747.
Fokina, D., Iliev, O., & Oseledets, I. (2021, June). Deep Neural Networks and Adaptive Quadrature for Solving Variational Problems. In International Conference on Large-Scale Scientific Computing, (pp. 369-377). Springer, Cham.
Fursov, I., Morozov, M., Kaploukhaya, N., Kovtun, E., Rivera-Castro, R., Gusev, G., Zaytsev, A., Burnaev, E. (2021). Adversarial Attacks on Deep Models for Financial Transaction Records.
Gusak, J., Daulbaev, T., Ponomarev, E., Cichocki, A., & Oseledets, I. (2021). Reduced-order modeling of deep neural networks. Computational Mathematics and Mathematical Physics, 61(5), 774-785.
Holzbaur, L., Kruglik, S., Frolov, A., Wachter-Zeh, A. (2021). Secrecy and Accessibility in Distributed Storage. IEEE Global Communications Conference, GLOBECOM 2020 – Proceedings, 9322434; Virtual, Taipei; Taiwan.
Ibrahim, S., Lan, C., Chabot, C., Mitsa, G., Buchanan, M., Aguilar-Mahecha, A., … & Borchers, C. H. (2021). Precise Quantitation of PTEN by Immuno-MRM: A Tool To Resolve the Breast Cancer Biomarker Controversy. Analytical chemistry, vol. 93(31), pp. 10816-10824.
Illarionova, S., Nesteruk, S., Shadrin, D., Ignatiev, V., Pukalchik, M., & Oseledets, I. (2021). MixChannel: Advanced Augmentation for Multispectral Satellite Images. Remote Sensing, vol. 13(11), 2181.
Illarionova, S., Nesteruk, S., Shadrin, D., Ignatiev, V., Pukalchik, M., & Oseledets, I. (2021). Object-Based Augmentation for Building Semantic Segmentation: Ventura and Santa Rosa Case Study. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1659-1668.
Illarionova, S., Trekin, A., Ignatiev, V., Oseledets, I. (2021). Neural-Based Hierarchical Approach for Detailed Dominant Forest Species Classification by Multispectral Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, 2021, 9311828, pp. 1810-1820.
Illarionova, S., Shadrin, D., Trekin, A., Ignatiev, V., & Oseledets, I. (2021). Generation of the NIR spectral Band for Satellite Images with Convolutional Neural Networks.
Isakov, V., Panchenko, A., Toldova, S., & Smirnov, I. RST Discourse Parser for Russian: An Experimental Study of Deep Learning Models. In Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers (p. 105). Springer Nature.
Jin, J., Fang, H., Daly, I., Xiao, R., Miao, Y., Wang, X., & Cichocki, A. (2021). Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI. International Journal of Neural Systems, vol. 31(07), p. 2150030.
Jin, J., Sun, H., Daly, I., Li, S., Liu, C., Wang, X., & Cichocki, A. (2021). A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery based Brain-Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering.
Kalinov, A., Bychkov, R., Ivanov, A., Osinsky, A., Yarotsky, D. (2021). Machine Learning-Assisted PAPR Reduction in Massive MIMO. IEEE Wireless Communications Letters, vol. 10 (3), 9253674, pp. 537-541.
Kaluzhskiy, L., Ershov, P., Yablokov, E., Shkel, T., Grabovec, I., Mezentsev, Y., … Strushkevich, N., Ivanov, A. (2021). Human Lanosterol 14-Alpha Demethylase (CYP51A1) Is a Putative Target for Natural Flavonoid Luteolin 7, 3′-Disulfate. Molecules, vol. 26(8), 2237.
Kan, M., Aliev, R., Rudenko, A., Drobyshev, N., Petrashen, N., Kondrateva, E., Bernstein, A., Burnaev, E. (2021). Interpretation of 3D CNNs for Brain MRI Data Classification. Recent Trends in Analysis of Images, Social Networks and Texts, vol. 1357, pp. 229.
Kail, R., Burnaev, E., & Zaytsev, A. (2021). Recurrent convolutional neural networks help to predict location of earthquakes. IEEE Geoscience and Remote Sensing Letters.
Kapushev, Y., Oseledets, I., Burnaev, E. (2021). Tensor completion via Gaussian process-based initialization. SIAM Journal on Scientific Computing, vol. 42 (6), pp. A3812-A3824.
Kardashin, A., Pervishko, A., Biamonte, J., & Yudin, D. (2021). Numerical hardware-efficient variational quantum simulation of a soliton solution. Physical Review A, vol. 104(2), L020402.
Khrulkov, V., Mirvakhabova, L., Oseledets, I., & Babenko, A. (2021). Latent Transformations via NeuralODEs for GAN-based Image Editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 14428-14437).
Kislenko, V. A., Pavlov, S. V., Fedorov, M. V., & Kislenko, S. A. (2021). New Aspects of Enhancing the Graphene Capacitance by Defects in Aqueous Electrolytes and Ionic Liquids. JETP Letters, pp. 1-7.
Kobelski, A., Osinenko, P., & Streif, S. (2021). Experimental verification of an online traction parameter identification method. Control Engineering Practice, vol. 113, 104837.
Kornilova, A., Salnikov, M., Novitskaya, O., Begicheva, M., Sevriugov, E., Shcherbakov, K., … & Dylov, D. V. (2021). Deep Learning Framework For Mobile Microscopy. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 324-328. IEEE.
Korotin, A., V’yugin, V., & Burnaev, E. (2021). Mixability of integral losses: A key to efficient online aggregation of functional and probabilistic forecasts. Pattern Recognition, vol. 120, 108175.
Korotin, A.A., V’Yugin, V.V., Burnaev, E.V. (2021). Online algorithm for aggregating experts’ predictions with unbounded quadratic loss. Russian Mathematical Surveys, vol. 75 (5), pp. 974-977.
Korotin, A., Li, L., Genevay, A., Solomon, J. M., Filippov, A., & Burnaev, E. (2021). Do neural optimal transport solvers work? a continuous wasserstein-2 benchmark. Advances in Neural Information Processing Systems, vol. 34, pp. 14593-14605.
Kostyukevich, Y., Osipenko, S., Rindin, K., Zherebker, A., Kovaleva, O., Rumiantseva, L., … & Nikolaev, E. (2021). Analysis of the Bio-oil Produced by the Hydrothermal Liquefaction of Biomass Using High-Resolution Mass Spectrometry and Isotope Exchange. Energy & Fuels, vol. 35(15), pp. 12208-12215.
Kovalenko, E., Talitckii, A., Anikina, A., Shcherbak, A., Zimniakova, O., Semenov, M., Bril, E., Dylov, D.V., Somov, A. (2021). Distinguishing Between Parkinson’s Disease and Essential Tremor Through Video Analytics Using Machine Learning: a Pilot Study. IEEE Sensors Journal.
Kozitsin, V., Katser, I., Lakontsev, D. (2021). Online Forecasting and Anomaly Detection Based on the ARIMA Model. Applied Sciences, vol. 11(7), 3194.
Kozlovskii, I., & Popov, P. (2021). Protein–Peptide Binding Site Detection Using 3D Convolutional Neural Networks. Journal of Chemical Information and Modeling, vol. 61(8), pp. 3814-3823.
Kozlovskii, I., & Popov, P. (2021). Structure-based deep learning for binding site detection in nucleic acid macromolecules. NAR genomics and bioinformatics, vol. 3(4), lqab111.
Krasnov, L., Khokhlov, I., Fedorov, M. V., & Sosnin, S. (2021). Transformer-based artificial neural networks for the conversion between chemical notations. Scientific Reports, vol. 11(1), pp. 1-10.
Kruglik, S., Frolov, A. (2021). An Information-Theoretic Approach for Reliable Distributed Storage Systems. Journal of Communications Technology and Electronics, vol. 65 (12), pp. 1505-1516.
Kumbhakar, S. C., Peresetsky, A., Shchetynin, Y., & Zaytsev, A. (2021). Technical efficiency and inefficiency: Reliability of standard SFA models and a misspecification problem. Econometrics and Statistics.
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Kuleshov, A., & Bernstein, A. (2016). Statistical learning on manifold-valued data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9729, pp. 311–325.
Kuzmin, A., Zhang, X., Finche, J., Feigin, M., Anthony, B. W., & Lempitsky, V. (2016). Fast low-cost single element ultrasound reflectivity tomography using angular distribution analysis. Proceedings – International Symposium on Biomedical Imaging, 2016-June, 1021–1024.
Lebedev, V., & Lempitsky, V. (2016). Fast ConvNets Using Group-Wise Brain Damage. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 2554–2564.
Lee, N., & Cichocki, A. (2016). Regularized computation of approximate pseudoinverse of large matrices using low-rank tensor train decompositions. SIAM Journal on Matrix Analysis and Applications, 37(2), 598–623.
Litsarev, M. S., & Oseledets, I. V. (2016). A low-rank approach to the computation of path integrals. Journal of Computational Physics, 305, 557–574.
Mikhalev, A. Y., & Oseledets, I. V. (2016). Iterative representing set selection for nested cross approximation. Numerical Linear Algebra with Applications, 23(2), 230–248.
Misin, M., Palmer, D. S., & Fedorov, M. V. (2016). Predicting Solvation Free Energies Using Parameter-Free Solvent Models. Journal of Physical Chemistry B, 120(25), 5724–5731.
Misin, M., Vainikka, P. A., Fedorov, M. V, & Palmer, D. S. (2016). Salting-out effects by pressure-corrected 3D-RISM. Journal of Chemical Physics, 145(19).
Nazarenko, D. V., Kharyuk, P. V., Oseledets, I. V., Rodin, I. A., & Shpigun, O. A. (2016).Machine learning for LC-MS medicinal plants identification. Chemometrics and Intelligent Laboratory Systems, 156, 174–180.
Oseledets, I. V, Rakhuba, M. V, & Chertkov, A. V. (2016). Black-box solver for one dimensional multiscale modelling using the QTT format. ECCOMAS Congress 2016 – Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering, 4, 7938–7947.
Rakhuba, M. V, & Oseledets, I. V. (2016). Grid-based electronic structure calculations: The tensor decomposition approach. Journal of Computational Physics, 312, 19–30.
Rakhuba, M., & Oseledets, I. (2016). Calculating vibrational spectra of molecules using tensor train decomposition. Journal of Chemical Physics, 145(12).
Struminsky, K., Kruglik, S., Vetrov, D., & Oseledets, I. (2016). A new approach for sparse Bayesian channel estimation in SCMA uplink systems. 2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016.
Sushnikova, D. A., & Oseledets, I. V. (2016). Preconditioners for hierarchical matrices based on their extended sparse form. Russian Journal of Numerical Analysis and Mathematical Modelling, 31(1), 29–40.
Ulyanov, D., Lebedev, V., Vedaldi, A., & Lempitsky, V. (2016). Texture networks: Feed-forward synthesis of textures and stylized images. 33rd International Conference on Machine Learning, ICML 2016, 3, 2027–2041.
Ustinova, E., & Lempitsky, V. (2016). Learning deep embeddings with histogram loss. Advances in Neural Information Processing Systems, 4177–4185.
Yandex, A. B., & Lempitsky, V. (2016). Efficient Indexing of Billion-Scale Datasets of Deep Descriptors. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 2055–2063.
Zeng, Z., Cichocki, A., Cheng, L., Xia, Y., & Hu, X. (2016). Guest Editorial Special Issue on Neurodynamic Systems for Optimization and Applications. IEEE Transactions on Neural Networks and Learning Systems, 27(2), 210–213.
Zhang, Y., Zhao, Q., Zhou, G., Jin, J., Wang, X., & Cichocki, A. (2016). Removal of EEG artifacts for BCI applications using fully Bayesian tensor completion. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings, 2016-May, 819–823.
Zhang, Y., Zhou, G., Zhao, Q., Cichocki, A., & Wang, X. (2016). Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation. Neurocomputing, 198, 148–154.
Zherebker, A. Y., Kostyukevich, Y. I., Kononikhin, A. S., Nikolaev, E. N., & Perminova, I. V. (2016). Molecular compositions of humic acids extracted from leonardite and lignite as determined by Fourier transform ion cyclotron resonance mass spectrometry.Mendeleev Communications, 26(5), 446–448.
Zherebker, A., Kostyukevich, Y., Kononikhin, A., Roznyatovsky, V. A., Popov, I., Grishin, Y. K., … Nikolaev, E. (2016). High desolvation temperature facilitates the ESI-source H/D exchange at non-labile sites of hydroxybenzoic acids and aromatic amino acids. Analyst, 141(8), 2426–2434.
Zhou, G., Cichocki, A., Zhang, Y., & Mandic, D. P. (2016). Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction. IEEE Transactions on Neural Networks and Learning Systems, 27(11), 2426–2439.
Zhou, G., Zhao, Q., Zhang, Y., Adali, T., Xie, S., & Cichocki, A. (2016). Linked Component Analysis from Matrices to High-Order Tensors: Applications to Biomedical Data. Proceedings of the IEEE, 104(2), 310–331.
Zhou, S., Allison, B. Z., Kübler, A., Cichocki, A., Wang, X., & Jin, J. (2016). Effects of background music on objective and subjective performance measures in an auditory BCI.Frontiers in Computational Neuroscience, 10(OCT).

Absil, P.-A., & Oseledets, I. V. (2015). Low-rank retractions: a survey and new results. Computational Optimization and Applications, 62(1), 5–29.
Babenko, A., & Lempitsky, V. (2015). Tree quantization for large-scale similarity search and classification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June, 4240–4248.
Babenko, A., & Lempitsky, V. (2015). The inverted multi-index. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(6), 1247–1260.
Baranov, V., & Oseledets, I. (2015). Fitting high-dimensional potential energy surface using active subspace and tensor train (AS+TT) method. Journal of Chemical Physics, 143(17).
Ganin, Y., & Lempitsky, V. (2015). N4-fields: Neural network nearest neighbor fields for image transforms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9004, pp. 536–551.
Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. 32nd International Conference on Machine Learning, ICML 2015, 2, 1180–1189.
Jertz, R., Friedrich, J., Kriete, C., Nikolaev, E. N., & Baykut, G. (2015). Tracking the Magnetron Motion in FT-ICR Mass Spectrometry. Journal of the American Society for Mass Spectrometry, 26(8), 1349–1366.
Kolesnikov, D. A., & Oseledets, I. V. (2015). From low-rank approximation to a rational Krylov subspace method for the Lyapunov equation. SIAM Journal on Matrix Analysis and Applications, 36(4), 1622–1637.
Kononenko, D., & Lempitsky, V. (2015). Learning to look up: Realtime monocular gaze correction using machine learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June, 4667–4675.
Kostyukevich, Y., Kononikhin, A., Popov, I., Indeykina, M., Kozin, S. A., Makarov, A. A., & Nikolaev, E. (2015). Supermetallization of peptides and proteins during electrospray ionization.Journal of Mass Spectrometry, 50(9), 1079–1087.
Kostyukevich, Y., Kononikhin, A., Popov, I., & Nikolaev, E. (2015). Conformations of cationized linear oligosaccharides revealed by FTMS combined with in-ESI H/D exchange. Journal of Mass Spectrometry, 50(10), 1150–1156.
Kostyukevich, Y., Kononikhin, A., Popov, I., & Nikolaev, E. (2015). Observation of the 16O/18O exchange during electrospray ionization.European Journal of Mass Spectrometry, 21(2), 109–113.
Kostyukevich, Y., Kononikhin, A., Popov, I., Spasskiy, A., & Nikolaev, E. (2015). In ESI-source H/D exchange under atmospheric pressure for peptides and proteins of different molecular weights from 1 to 66kDa: The role of the temperature of the desolvating capillary on H/D exchange. Journal of Mass Spectrometry, 50(1), 49–55.
Kostyukevich, Y., Kononikhin, A., Popov, I., Starodubtzeva, N., Pekov, S., Kukaev, E., … Nikolaev, E. (2015). Analytical potential of the in-electrospray ionization source hydrogen/deuterium exchange for the investigation of oligonucleotides. European Journal of Mass Spectrometry, 21(1), 59–63.
Kostyukevich, Y., Zhdanova, E., Kononikhin, A., Popov, I., Kukaev, E., & Nikolaev, E. (2015). Observation of the multiple halogenation of peptides in the electrospray ionization source.Journal of Mass Spectrometry, 50(7), 899–905.
Kuzmin, A., Zakrzewski, A. M., Anthony, B. W., & Lempitsky, V. (2015). Multi-frame elastography using a handheld force-controlled ultrasound probe. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 62(8), 1486–1500.
Larina, I. M., Pastushkova, L. K., Tiys, E. S., Kireev, K. S., Kononikhin, A. S., Starodubtseva, N. L., Popov, I. A., Custaud, M.-A., Dobrokhotov, I. V., Nikolaev, E. N., Kolchanov, N. A., & Ivanisenko, V. A. (2015). Permanent proteins in the urine of healthy humans during the Mars-500 experiment.Journal of Bioinformatics and Computational Biology, 13(1).
Litsarev, M. S., & Oseledets, I. V. 2015). Fast low-rank approximations of multidimensional integrals in ion-atomic collisions modelling. Numerical Linear Algebra with Applications, 22(6), 1147–1160.
Lubich, C., Oseledets, I. V, & Vandereycken, B. (2015). Time integration of tensor trains. SIAM Journal on Numerical Analysis, 53(2), 917–941.
Mikhalev, A. Y., & Oseledets, I. V. (2015). Rectangular submatrices of maximum volume and their computation.Doklady Mathematics, 91(3), 267–268.
Milyaev, S., Barinova, O., Novikova, T., Kohli, P., & Lempitsky, V. (2015). Fast and accurate scene text understanding with image binarization and off-the-shelf OCR. International Journal on Document Analysis and Recognition, 18(2), 169–182.
Nikolaev, E. N. (2015). Some notes about FT ICR mass spectrometry. International Journal of Mass Spectrometry, 377(1), 421–431.
Rakhuba, M. V, & Oseledets, I. V. (2015). Fast multidimensional convolution in low-rank tensor formats via cross approximation. SIAM Journal on Scientific Computing, 37(2), A565–A582.
Ranger, B. J., Feigin, M., Pestrov, N., Zhang, X., Lempitsky, V., Herr, H. M., & Anthony, B. W. (2015). Motion compensation in a tomographic ultrasound imaging system: Toward volumetric scans of a limb for prosthetic socket design.Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015-Novem, 7204–7207.
Ryzhakov, G. V, Mikhalev, A. Y., Sushnikova, D. A., & Oseledets, I. V. (2015). Numerical solution of diffraction problems using large matrix compression.2015 9th European Conference on Antennas and Propagation, EuCAP 2015.
Vladimirov, G., Kostyukevich, Y., Hendrickson, C. L., Blakney, G. T., & Nikolaev, E. (2015). Effect of magnetic field inhomogeneity on ion cyclotron motion coherence at high magnetic field.European Journal of Mass Spectrometry, 21(3), 443–449.
Yandex, A. B., & Lempitsky, V. (2015). Aggregating local deep features for image retrieval.Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter, 1269–1277.
Zhang, X., Fincke, J., Kuzmin, A., Lempitsky, V., & Anthony, B. (2015). A single element 3D ultrasound tomography system.Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015-Novem, 5541–5544.
Zhang, Z., Yang, X., Oseledets, I. V, Karniadakis, G. E., & Daniel, L. (2015). Enabling high-dimensional hierarchical uncertainty quantification by ANOVA and tensor-train decomposition.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 34(1), 63–76.