dlcp2024:letters:main
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+ | ====== Справки о публикации статей ====== | ||
+ | - {{ dlcp: | ||
+ | in high energy physics»}} | ||
+ | - {{ dlcp: | ||
+ | Longevity with Deep Learning»}} | ||
+ | - {{ dlcp: | ||
+ | TAIGA-IACT Experiment with Conditional Generative Adversarial Networks»}} | ||
+ | - {{ dlcp: | ||
+ | differential cross sections and structure functions of single pion electroproduction in the | ||
+ | resonance region»}} | ||
+ | - {{ dlcp: | ||
+ | experiment with neural network methods»}} | ||
+ | - {{ dlcp: | ||
+ | complexes in spiking neural networks»}} | ||
+ | - {{ dlcp: | ||
+ | Integrals Computation in Relativistic Quantum Mechanics»}} | ||
+ | - {{ dlcp: | ||
+ | Bond Ising Models Using LDPC Graph Representations and Nishimori Temperature»}} | ||
+ | - {{ dlcp: | ||
+ | Described by Generalized Nonlinear Schrödinger Equations»}} | ||
+ | - {{ dlcp: | ||
+ | for extensive air shower separation in the SPHERE-3 experiment»}} | ||
+ | - {{ dlcp: | ||
+ | - {{ dlcp: | ||
+ | - {{ dlcp: | ||
+ | detector systems using the example of muon shield in the SHiP experiment»}} | ||
+ | - {{ dlcp: | ||
+ | radial basis function networks»}} | ||
+ | - {{ dlcp: | ||
+ | Active Galactic Nuclei»}} | ||
+ | - {{ dlcp: | ||
+ | directions of UHECRs registered by fluorescence telescopes with neural networks»}} | ||
+ | - {{ dlcp: | ||
+ | HiSCORE Data Using Fully Connectedv Neural Networks»}} | ||
+ | - {{ dlcp: | ||
+ | Modulated Metal Oxide Semiconductor Gas Sensors through Machine Learning Based Response Models»}} | ||
+ | - {{ dlcp: | ||
+ | learning applications in Earth sciences in 2024: achievements and perspectives»}} | ||
+ | - {{ dlcp: | ||
+ | atmospheric modeling: methods and approaches»}} | ||
+ | - {{ dlcp: | ||
+ | reconstruction of multichannel imaging detector events: ELVES and TRACKS»}} | ||
+ | - {{ dlcp: | ||
+ | prediction of PM2.5 in urban agglomerations with complex terrain, using Grenoble as an example»}} | ||
+ | - {{ dlcp: | ||
+ | variability of the urban heat island in Moscow using machine learning»}} | ||
+ | - {{ dlcp: | ||
+ | magnetosphere using a special algorithm for working with multidimensional time series»}} | ||
+ | - {{ dlcp: | ||
+ | latitude power systems using machine learning methods»}} | ||
+ | - {{ dlcp: | ||
+ | for Tailored Material Properties using Large Language Models»}} | ||
+ | - {{ dlcp: | ||
+ | - {{ dlcp: | ||
+ | photoluminescent carbon nanosensor for metal ions in water using artificial neural networks»}} | ||
+ | - {{ dlcp: | ||
+ | Forecast the Kp Geomagnetic Index by Machine Learning»}} | ||
+ | - {{ dlcp: | ||
+ | MoS_2 by given properties»}} | ||
+ | - {{ dlcp: | ||
+ | models of soft sensors in oil refining»}} | ||
+ | - {{ dlcp: | ||
+ | premotor potentials in electroencephalogram signal for neurorehabilitation using a closed-loop brain-computer interface»}} | ||
+ | - {{ dlcp: | ||
+ | multicollinear features for reducing the input dimensionality of optical spectroscopy inverse problem»}} | ||
+ | - {{ dlcp: | ||
+ | at NICA SPD»}} | ||
+ | - {{ dlcp: | ||
+ | Networks via Quasiclassical Loss Functionals»}} | ||
+ | - {{ dlcp: | ||
+ | Underground Gas Storages»}} | ||
+ | - {{ dlcp: | ||
+ | density distribution in core of research nuclear reactor»}} | ||
+ | - {{ dlcp: | ||
+ | reinforcement learning with temporal coding and reward-modulated plasticity»}} |