Update README.rst

Author: Demoren1

README.rst

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     :Научный руководитель: Бахтеев Олег
     :Научный консультант(при наличии): Бабкин Петр
 
-Abstract
 ========
-
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+**Abstract**: The automated search for optimal neural network architectures (NAS) is a challenging compu-
+tational problem, and Neural Ensemble Search (NES) is even more complex. In this work, we propose a
+surrogate-based approach for ensebmle creation. Neural architectures are represented as graphs, and their
+predictions on a dataset serve as training data for the surrogate function. Using this function, we develop
+an efficient NES framework that enables the selection of diverse and high-performing architectures. The
+resulting ensemble achieves superior predictive accuracy on CIFAR-10 compared to other one-shot NES
+methods, demonstrating the effectiveness of our approach.
+
+**Keywords**: NES, GCN, triplet loss, surrogate function
 
 Research publications
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