Zobrazeno 1 - 10
of 10 095
pro vyhledávání: '"KISELEV, A. P."'
Autor:
Severin, Nikita, Ziablitsev, Aleksei, Savelyeva, Yulia, Tashchilin, Valeriy, Bulychev, Ivan, Yushkov, Mikhail, Kushneruk, Artem, Zaryvnykh, Amaliya, Kiselev, Dmitrii, Savchenko, Andrey, Makarov, Ilya
We present LLM-KT, a flexible framework designed to enhance collaborative filtering (CF) models by seamlessly integrating LLM (Large Language Model)-generated features. Unlike existing methods that rely on passing LLM-generated features as direct inp
Externí odkaz:
http://arxiv.org/abs/2411.00556
Autor:
Alekseev, I., Belov, V., Bystryakov, A., Danilov, M., Filosofov, D., Fomina, M., Gorovtsov, P., Iusko, Ye., Kazartsev, S., Khvatov, V., Kiselev, S., Kobyakin, A., Krapiva, A., Kuznetsov, A., Machikhiliyan, I., Mashin, N., Medvedev, D., Nesterov, V., Ponomarev, D., Rozova, I., Rumyantseva, N., Rusinov, V., Samigullin, E., Shevchik, Ye., Shirchenko, M., Shitov, Yu., Skrobova, N., Svirida, D., Tarkovsky, E., Yakushev, E., Zhitnikov, I., Yakovleva, A., Zinatulina, D.
The yields of the inverse beta decay events produced by antineutrinos from a certain nuclear reactor fuel component are used by many experiments to check various model predictions. Yet measurements of the absolute yields feature significant uncertain
Externí odkaz:
http://arxiv.org/abs/2410.19182
Autor:
Alekseev, I., Belov, V., Bystryakov, A., Danilov, M., Filosofov, D., Fomina, M., Gorovtsov, P., Iusko, Ye., Kazartsev, S., Khvatov, V., Kiselev, S., Kobyakin, A., Krapiva, A., Kuznetsov, A., Machikhiliyan, I., Mashin, N., Medvedev, D., Nesterov, V., Ponomarev, D., Rozova, I., Rumyantseva, N., Rusinov, V., Salamatin, A., Samigullin, E., Shevchik, Ye., Shirchenko, M., Shitov, Yu., Skrobova, N., Svirida, D., Tarkovsky, E., Yakushev, E., Zhitnikov, I., Yakovleva, A., Zinatulina, D.
Electron antineutrinos are emitted in the decay chains of the fission products inside a reactor core and could be used for remote monitoring of nuclear reactors. The DANSS detector is placed under the core of the 3.1 GW power reactor at the Kalinin N
Externí odkaz:
http://arxiv.org/abs/2410.18914
Autor:
Moioli, M., Popova, M. M., Hamilton, K. R., Ertel, D., Busto, D., Makos, I., Kiselev, M. D., Yudin, S. N., Ahmadi, H., Schröter, C. D., Pfeifer, T., Moshammer, R., Gryzlova, E. V., Grum-Grzhimailo, A. N., Bartschat, K., Sansone, G.
Attosecond photoelectron interferometry based on the combination of an attosecond pulse train and a synchronized infrared field is a fundamental technique for the temporal characterization of attosecond waveforms and for the investigation of electron
Externí odkaz:
http://arxiv.org/abs/2410.04240
Nambu-determinant brackets on $R^d\ni x=(x^1,...,x^d)$, $\{f,g\}_d(x)=\rho(x) \det(\partial(f,g,a_1,...,a_{d-2})/\partial(x^1,...,x^d))$, with $a_i\in C^\infty(R^d)$ and $\rho\partial_x\in\mathfrak{X}^d(R^d)$, are a class of Poisson structures with (
Externí odkaz:
http://arxiv.org/abs/2409.18875
Kontsevich constructed a map between `good' graph cocycles $\gamma$ and infinitesimal deformations of Poisson bivectors on affine manifolds, that is, Poisson cocycles in the second Lichnerowicz--Poisson cohomology. For the tetrahedral graph cocycle $
Externí odkaz:
http://arxiv.org/abs/2409.15932
Kontsevich constructed a map from suitable cocycles in the graph complex to infinitesimal deformations of Poisson bi-vector fields. Under the deformations, the bi-vector fields remain Poisson. We ask, are these deformations trivial, meaning, do they
Externí odkaz:
http://arxiv.org/abs/2409.12555
Autor:
Kiselev, Nikita, Grabovoy, Andrey
The loss landscape of neural networks is a critical aspect of their training, and understanding its properties is essential for improving their performance. In this paper, we investigate how the loss surface changes when the sample size increases, a
Externí odkaz:
http://arxiv.org/abs/2409.11995
Autor:
Kiselev, Mikhail
In the present paper, it is shown how the columnar/layered CoLaNET spiking neural network (SNN) architecture can be used in supervised learning image classification tasks. Image pixel brightness is coded by the spike count during image presentation p
Externí odkaz:
http://arxiv.org/abs/2409.07833
Autor:
Kiselev, Mikhail
In the present paper, I describe a spiking neural network (SNN) architecture which, can be used in wide range of supervised learning classification tasks. It is assumed, that all participating signals (the classified object description, correct class
Externí odkaz:
http://arxiv.org/abs/2409.01230