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Publications in Math-Net.Ru |
Citations |
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2022 |
| 1. |
V. Yu. Kuz'min, “Combined application of recurrent neural networks and statistical methods for improved oceanographic data forecasting accuracy”, Intelligent systems. Theory and applications, 26:1 (2022), 241–245  |
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2021 |
| 2. |
A. K. Gorshenin, V. Yu. Kuzmin, “Method for improving accuracy of neural network forecasts based on probability mixture models and its implementation as a digital service”, Inform. Primen., 15:3 (2021), 63–74  |
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2020 |
| 3. |
A. K. Gorshenin, V. Yu. Kuzmin, “Analysis of configurations of LSTM networks for medium-term vector forecasting”, Inform. Primen., 14:1 (2020), 10–16 |
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2019 |
| 4. |
A. K. Gorshenin, V. Yu. Kuzmin, “Application of recurrent neural networks to forecasting the moments of finite normal mixtures”, Inform. Primen., 13:3 (2019), 114–121 |
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| 5. |
A. K. Gorshenin, V. Yu. Kuzmin, “Optimization of hyperparameters of neural networks using high-performance computing for prediction of precipitation”, Inform. Primen., 13:1 (2019), 75–81 |
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2018 |
| 6. |
A. K. Gorshenin, V. Yu. Kuzmin, “Forecasting moments of finite normal mixtures using feedforward neural networks”, Sistemy i Sredstva Inform., 28:3 (2018), 62–71 |
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2017 |
| 7. |
A. K. Gorshenin, V. Yu. Kuzmin, “MSM Tools as a heterogeneous computing service”, Sistemy i Sredstva Inform., 27:1 (2017), 60–72 |
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2016 |
| 8. |
A. K. Gorshenin, V. Yu. Kuzmin, “Application of the CUDA architecture for implementation of grid-based algorithms for the method of moving separation of mixtures”, Sistemy i Sredstva Inform., 26:4 (2016), 60–73 |
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2011 |
| 9. |
V. Yu. Korolev, V. A. Krylov, V. Yu. Kuz'min, “Stability of finite mixtures of generalized gamma-distributions with respect to disturbance of parameters”, Inform. Primen., 5:1 (2011), 31–38 |
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